@article{reed_naik_abney_herbert_fine_vadlamannati_morris_taylor_muglia_granlund_et al._2024, title={Experimental Validation of an Iterative Learning-Based Flight Trajectory Optimizer for an Underwater Kite}, volume={32}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2024.3359891}, DOI={10.1109/TCST.2024.3359891}, abstractNote={In this work, we present an iterative learning strategy and experimental validation thereof for optimizing the flight trajectory of an underwater kite. The methodology is adapted to two different power generation configurations. The iterative learning algorithm consists of two main steps, which are executed at each iteration. In the first step, a meta-model is updated using a recursive least squares (RLS) estimate to capture an economic performance index as a function of a set of basis parameters that define the flight trajectory. The second step is an iterative learning update using information from past cycles to update basis parameters at future cycles using a gradient ascent formulation. This algorithm was experimentally validated on a scaled experimental prototype underwater kite system towed behind a test vessel in Lake Norman, North Carolina. Using our experimental system and algorithm, we were able to increase the kite’s mechanical power generation by an average of 24.4% across the tests performed.}, number={4}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Reed, James and Naik, Kartik and Abney, Andrew and Herbert, Dillon and Fine, Jacob and Vadlamannati, Ashwin and Morris, James and Taylor, Trip and Muglia, Michael and Granlund, Kenneth and et al.}, year={2024}, month={Jul}, pages={1240–1253} }
 @article{naik_vermillion_2024, title={Integrated physical design, control design, and site selection for an underwater energy-harvesting kite system}, volume={220}, ISSN={["1879-0682"]}, DOI={10.1016/j.renene.2023.119687}, abstractNote={This paper presents a co-design framework that optimizes the kite design, site, and controller of a kite-based marine hydrokinetic (MHK) energy-harvesting system. The formulation seeks to maximize a techno-economic metric, namely power-to-mass ratio, by simultaneously considering three key categories of decision variables while accounting for the coupling between the three. The simultaneous consideration presents computational challenges associated with optimizing a large number of decision variables, a subset of which (control variables) are time trajectories. The multi-fidelity co-design formulation presented in this work utilizes two techniques, namely nesting and layering, to solve the optimization problem in a computationally tractable manner without significantly compromising on accuracy. Specifically, nesting allows for efficient integration of the three optimization sub-modules into one integrated framework without accuracy losses, whereas layering allows for successive design space reduction as the overall optimization progresses from using a low-fidelity model to using a higher-fidelity model. The resulting integrated co-design tool was applied to a region of interest off the North Carolina coast to optimally choose a combination of deployment site, kite design, and control strategy. We show that the integrated co-design tool results in a two-fold performance improvement over benchmarks derived from sequential (or independent) optimization of the kite categories, thereby underscoring the need for co-design. Computational effectiveness is demonstrated by comparing the computational cost of the nested and layered approach against the estimated computational costs that would be required to perform a single high-fidelity integrated optimization over the entire design space.}, journal={RENEWABLE ENERGY}, author={Naik, Kartik and Vermillion, Chris}, year={2024}, month={Jan} }
 @article{govindarajan_haydon_vermillion_2023, title={Predictive Velocity Trajectory Control for a Persistently Operating Solar-Powered Autonomous Surface Vessel}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156048}, abstractNote={The Gulf Stream represents a major potential resource for renewable energy but is presently only sparsely characterized via radar, buoys, gliders, and intermittently operating human-operated research vessels. Dramatically greater resolution is possible through the use of persistently operating autonomous surface vessels (ASVs), which can be powered by wind, wave, or solar resources. Optimizing the control of these ASVs, taking into account the device and environmental properties, is crucial to obtaining good data. An ASV’s path and velocity profile along that path both significantly influence the amount of a mission domain that can be covered and, ultimately, the scientific quality of the mission. While our previous work focused on optimizing the path of a solar-powered ASV with fixed speed, the present work represents the complement: optimizing the speed for a given path, accounting for the ASV dynamics, flow resource, and solar resource. We perform this optimization through a model predictive controller that maximizes the projected distance traversed, with a terminal incentive that captures the estimated additional long-duration range that is achievable from a given terminal battery state of charge. We present simulation results based on the SeaTrac SP-48 ASV, Mid-Atlantic Bight/South-Atlantic Bight Regional Ocean Model, and European Centre for Medium-Range Weather Forecasts solar model. Our results show improved performance relative to simpler heuristic controllers that aim to maintain constant speed or constant state of charge. However, we also show that the design of the MPC terminal incentive and design of the heuristic comparison controller can significantly impact the achieved performance; by examining underlying simulation results for different designs, we are able to identify likely causes of performance discrepancies.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Govindarajan, Kavin and Haydon, Ben and Vermillion, Chris}, year={2023}, pages={2077–2083} }
 @article{siddiqui_deese_vermillion_2023, title={Recursive Gaussian Process-Based Adaptive Control of a Ducted Kite System for Tidal Energy Harvesting}, volume={31}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2023.3243441}, abstractNote={In this brief, we introduce an adaptive control approach for a novel tidal energy harvesting device called the Duct-Sail. The design combines features of kite-based energy-harvesting devices and ducted turbines to realize power augmentation through cross-current flight at low flow speeds along with the flow augmentation, stationary performance potential, and protection offered by a duct. At low flow speeds, the Duct-Sail executes high-speed figure-8 cross-current flight to generate rated power. At higher flow speeds, it curtails its cross-current motion to limit structural loading while still delivering rated power. We present a detailed dynamic model of the system along with design parameters for an initial prototype. We also present model-based and nonmodel-based adaptive control strategies that are used to control the intensity of cross-current flight in a time-varying flow profile. Lastly, we present simulation results using the real flow data from a candidate installation site, which enables a practically meaningful comparison of various adaptive control strategies.}, number={4}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Siddiqui, Ayaz and Deese, Joe and Vermillion, Chris}, year={2023}, month={Jul}, pages={1949–1956} }
 @article{reed_abney_mishra_naik_perkins_vermillion_2023, title={Stability and Performance of an Undersea Kite Operating in a Turbulent Flow Field}, volume={31}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2023.3237614}, abstractNote={In this article, we examine the effects of flow disturbances resulting from turbulence on the dynamic behavior of an underwater energy-harvesting kite system that executes periodic figure-8 flight. Due to the periodic nature of the kite’s operation, we begin by assessing orbital stability using the Floquet analysis and stroboscopic intersection analysis of a Poincaré section, with the former analysis performed on a simplified “unifoil” model and the latter performed on a six-degree-of-freedom (6-DOF)/flexible tether model. With periodic stability established, a frequency-domain analysis based on a linearization about the kite’s path is used to predict the quality of flight path tracking as a function of the turbulence frequency. To validate the accuracy of these simulation-based predictions under flow disturbances, we compare the predictions of the kite’s behavior against the results of small-scale tow testing experiments performed in a controlled pool environment.}, number={4}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Reed, James and Abney, Andrew and Mishra, Kirti D. and Naik, Kartik and Perkins, Edmon and Vermillion, Chris}, year={2023}, month={Jul}, pages={1663–1678} }
 @article{siddiqui_borek_vermillion_2022, title={A Fused Gaussian Process Modeling and Model Predictive Control Framework for Real-Time Path Adaptation of an Airborne Wind Energy System}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2022.3178038}, abstractNote={This article presents a computationally tractable adaptive control strategy suitable for mobile systems operating in a stochastically and spatiotemporally varying environment by fusing Gaussian process modeling and receding horizon control. This strategy ideally manages the tradeoff between exploration (maintaining an accurate estimate of the stochastic resource) and exploitation (maximizing a performance index, which generally consists of harvesting the resource) subject to partial observability (stochastic resource only measurable at the system’s location) and mobility constraints, which are characteristic of dynamic systems. The case study in this article focuses on a crosswind airborne wind energy (AWE) system where the wind turbine tower is replaced by tethers and a lifting body, allowing the system to adjust its altitude, with the goal of operating at the altitude that maximizes net energy production in a wind environment that is changing in altitude and time. Real wind speed versus altitude data has been used to validate the strategy and results are presented for a variety of control strategies applied to a rigid wing-based AWE system.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Siddiqui, Ayaz and Borek, John and Vermillion, Chris}, year={2022}, month={Jun} }
 @article{razi_ramaprabhu_tarey_muglia_vermillion_2022, title={A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions}, volume={200}, ISSN={["1879-0682"]}, DOI={10.1016/j.renene.2022.10.001}, abstractNote={Understanding the effects of ambient turbulence (expressed often in terms of the turbulence intensity It) is critical to the development of predictive models for the performance of Ocean Current Turbine (OCTs). This paper describes a new, wake interaction modeling framework capable of capturing the detailed effects of turbulence on various performance parameters associated with OCTs that may be arranged in any arbitrary configuration. The model accounts for the effects of turbulence on the structure of the turbine wakes, specifically the extents of near- and far-wake regions, and the dependence of the transition point between the two regions on It. The analytical description for turbine wake is combined with an existing wake interaction model, the Unrestricted Wind Farm Layout Optimization (UWFLO) model to predict the global power output from an array of OCTs. The resulting modelling framework accurately captures the effect of inlet turbulence on the OCT farm power and efficiency, and can be applied to any array configuration. Results from the model are validated against Large Eddy Simulations (LES) in which the OCTs are modeled using the Blade Element Momentum (BEM) model, while the inlet flow is superposed with a synthetic turbulence field designed to approximate turbulence properties obtained from observational measurements of the Gulf Stream. The simulations show that OCT wakes recover faster at higher levels of inlet turbulence due to the enhanced entrainment and mixing between ambient flow and the wake, an effect that is captured by the modified UWFLO model.}, journal={RENEWABLE ENERGY}, author={Razi, P. and Ramaprabhu, P. and Tarey, P. and Muglia, M. and Vermillion, C.}, year={2022}, month={Nov}, pages={1602–1617} }
 @article{bhattacharjee_tiburcio_opila_vermillion_fathy_2022, title={An Analytic Solution to the Inverse Dynamics of an Energy Harvesting Tethered Kite}, volume={144}, ISSN={["1528-9028"]}, DOI={10.1115/1.4055169}, abstractNote={Abstract
               This paper solves the inverse dynamics of a tethered kite analytically. Specifically, the paper presents a procedure for determining the angle of attack, induced roll angle, and tether tension magnitude needed to achieve a desired combination of translational kite position, velocity, and acceleration. The focus of the paper is on energy harvesting kites. However, the underlying approach is applicable to other kite systems, such as kites for propulsion (e.g., SkySails, Hamburg, Germany). Solving inverse kite dynamics analytically is valuable for trajectory optimization, online state estimation, and the analysis of fundamental limitations on kite maneuvers. Previous work in the literature presents several models of kite dynamics, with varying degrees of fidelity and complexity. However, the nonlinearity of these models often makes them difficult to use for optimization, estimation, and control. The paper shows that, under reasonable assumptions, inverse kite dynamics can be solved in terms of the roots of a fourth-order polynomial function of angle of attack. This function has a geometric interpretation, providing insight into the multiplicity of resulting solutions. Moreover, for special cases including a kite with noncambered wings, these solutions can be computed analytically. A simulation validates the success of the proposed approach in computing inverse kite dynamics for a cross-current trajectory.}, number={11}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Bhattacharjee, Debapriya and Tiburcio, Miguel Alvarez and Opila, Daniel F. and Vermillion, Christopher and Fathy, Hosam K.}, year={2022}, month={Nov} }
 @article{abney_reed_naik_bryant_herbert_leonard_vadlamannati_mook_beknalkar_alvarez_et al._2022, title={Autonomous Closed-Loop Experimental Characterization and Dynamic Model Validation of a Scaled Underwater Kite}, volume={144}, ISSN={["1528-9028"]}, DOI={10.1115/1.4054141}, abstractNote={Abstract
               This paper presents the closed-loop experimental framework and dynamic model validation for a 1/12-scale underwater kite design. The pool-based tow testing framework described herein, which involves a fully actuated, closed-loop controlled kite and flexible tether, significantly expands upon the capabilities of any previously developed open-source framework for experimental underwater kite characterization. Specifically, the framework has allowed for the validation of three closed-loop flight control strategies, along with a critical comparison between dynamic model predictions and experimental results. In this paper, we provide a detailed presentation of the experimental tow system and kite setup, describe the control algorithms implemented and tested, and quantify the level of agreement between our multi-degree-of-freedom kite dynamic model and experimental data. We also present a sensitivity analysis that helps to identify the most influential parameters to kite performance and further explain the remaining mismatches between the model and data.}, number={7}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Abney, Andrew and Reed, James and Naik, Kartik and Bryant, Samuel and Herbert, Dillon and Leonard, Zak and Vadlamannati, Ashwin and Mook, Mariah and Beknalkar, Sumedh and Alvarez, Miguel and et al.}, year={2022}, month={Jul} }
 @article{beknalkar_naik_vermillion_mazzoleni_2022, title={Closed-Loop-Flight-Based Combined Geometric and Structural Wing Design Optimization Framework for a Marine Hydrokinetic Energy Kite}, ISBN={["978-1-6654-6809-1"]}, ISSN={["0197-7385"]}, DOI={10.1109/OCEANS47191.2022.9977369}, abstractNote={A marine hydrokinetic (MHK) kite offers an economical solution to the challenges of size and investment costs posed by the existing class of energy converters used to harvest tidal and ocean current energy. MHK kite systems are complicated devices that harvest ocean current energy by flying a tethered kite perpendicular to the motion of the current flow. They possess strong coupling between closed-loop flight control, geometric design, and structural design and hence it is important to consider all three facets simultaneously while designing a MHK kite system. Our previous work addressed this problem of simultaneous optimization of plant and controller through a control-aware optimization framework that fuses a geometric optimization tool, a structural optimization tool, and a closed-loop flight efficiency map. While our previous work analyzed the effect of key wing geometric parameters (wingspan and aspect ratio) on the performance of MHK kite systems, the present work represents the next crucial step in the study of ocean energy-harvesting kite systems and expands the design space to include several other wing geometric parameters - airfoil design, wing taper, wing twist, and dihedral angle. The effect of these decision variables on the power-to-mass ratio is estimated through an optimization framework based on a sequential approach. First, using sensitivity analysis, the framework determines which design variables in the design space affect the peak mechanical power generated while flying a cross-current path. In the next step, the combined geometric and structural optimization tool derives optimal values of variables in the reduced design space that results in a minimum structural mass. The constraints in the optimization problem include a lower limit on the peak power and limits on the number and dimensions of I-beam spars and the thickness of the wing shell. With a wing structure that can sustain peak lifting loads equal to less than a fixed value, the rest of the design variables are optimized to achieve maximum time-averaged power using medium-fidelity closed-loop-flight-based simulations. The final results of the optimization framework include an optimized wing geometry and wing structure with a maximized power-to-mass ratio for an MHK kite.}, journal={2022 OCEANS HAMPTON ROADS}, author={Beknalkar, Sumedh and Naik, Kartik and Vermillion, Chris and Mazzoleni, Andre}, year={2022} }
 @article{bhattacharjee_tiburcio_opila_vermillion_fathy_2023, title={Co-Optimization of the Spooling and Cross-Current Trajectories of an Energy Harvesting Marine Hydrokinetic Kite}, volume={145}, ISSN={["1528-9028"]}, DOI={10.1115/1.4062574}, abstractNote={Abstract
               This paper examines the problem of simultaneously optimizing the spooling and cross-current flight trajectory of a tethered marine hydrokinetic kite using an analytic solution of its inverse dynamics. Tethered kites hold considerable promise for energy production, especially when undergoing cross-current motion. The novelty of this work lies in the use of an analytic solution of the inverse dynamics of the kite to solve the trajectory optimization problem. The term “inverse dynamics” refer to the process of obtaining an exact solution for the actuator inputs from the position, velocity, and acceleration of the kite. While the literature on tethered kites explores trajectory optimization in great detail, most of the work exploits the forward dynamics of the kite, and does not simultaneously optimize the spooling motion and cross-current trajectory. This paper formulates the co-optimization of the kite spooling and cross-current trajectory using a three degrees-of-freedom kite model, paired with an inelastic tether model. The analytic solution of the inverse dynamics is solved in terms of the roots of a fourth-order polynomial in terms of the angle of attack. A simulation study validates the optimization approach and shows that the kite is able to achieve significant energy production.}, number={7}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Bhattacharjee, Debapriya and Tiburcio, Miguel Alvarez and Opila, Daniel F. and Vermillion, Christopher and Fathy, Hosam K.}, year={2023}, month={Jul} }
 @article{earnhardt_groelke_borek_pelletier_brennan_vermillion_2022, title={Cooperative Exchange-Based Platooning Using Predicted Fuel-Optimal Operation of Heavy-Duty Vehicles}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2022.3169390}, abstractNote={Several driving situations exist where fuel-optimal driving in terms of aggregate performance can only be achieved when one or more vehicles incurs a sacrifice in its own fuel consumption. For these situations, an economic incentive is needed to entice that vehicle to participate in aggregate fuel-optimal driving. Focusing on platooning amongst automated heavy-duty vehicles and using real trucking routes, we examine the precise extent to which the benefits of platooning can be expanded through the incorporation of exchange-based incentives. We focus on two mechanisms for incentivized platooning: (i) incentivized “catch-up” along a prescribed highway route and (ii) incentivized re-routing to allow for platooning. For the incentivized “catch-up” mechanism, platoon capable vehicles begin at staggered positions, using a novel platoon catch-up algorithm capable of determining the fuel-optimal platoon engagement position and fuel-optimal velocity trajectories. Additionally, the incentivized re-routing mechanism determines the optimal route for a network of platoon-capable vehicles, allowing for a vehicle to reroute its trajectory to engage within the platoon. Because such scenarios will be shown to frequently lead to aggregate benefit, while actually hurting the fuel economy of one or more participants, we propose three methods for explicitly computing the monetary value of the exchange. Assuming a known trajectory and traffic pattern, the first uses the Shapley value to determine the exchange value. The second method adjusts the Shapley value, accounting for uncertainty associated with traffic modeling. The final method assumes a competitive market, requiring each individual operator to implement a bid.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Pelletier, Evan and Brennan, Sean and Vermillion, Chris}, year={2022}, month={May} }
 @article{abney_vermillion_2022, title={Drag-Mitigating Dynamic Flight Path Design for an Ultra-Long Tether Underwater Kite}, volume={55}, ISSN={["2405-8963"]}, DOI={10.1016/j.ifacol.2022.11.176}, abstractNote={This paper presents a computational study of an underwater kite operating in an ultra-long tether (ULT) application. Leveraging a dynamic model established in literature, we study the relationship between path shape and tether drag at varying tether lengths in order to develop meaningful insights as to the operation of systems that require ultra-long tethers in order to reach available flow resources. The results are compared to fundamental tether drag relationships developed in the airborne wind energy field, including the multi-airborne wind energy system (MAWES) proposed by Leuthold et al. (2017, 2018). It will be shown that by careful selection of path shape, these fundamental relationships break down in deep-water marine environments, and that high performance rivaling that of the MAWES system can be achieved, without the extra required mechanical complexity.}, number={37}, journal={IFAC PAPERSONLINE}, author={Abney, Andrew and Vermillion, Chris}, year={2022}, pages={151–157} }
 @article{cobb_reed_wu_mishra_barton_vermillion_2022, title={Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2022.3165734}, abstractNote={This brief presents an iterative learning control (ILC) framework for a class of repetitive control (RC) applications characterized by: 1) continuous operation; 2) flexible iteration time; and 3) an economic performance metric. Specifically, the effect of iteration-varying initial conditions, resulting from the continuous nature of the operation, is accounted for through an iteration domain receding horizon formulation. To address the need for flexible iteration times, the time-domain dynamics are transformed into path-domain dynamics characterized by a non-dimensional parameter spanning an iteration-invariant range. The resulting model is used to derive learning filters that minimize a multi-objective economic cost. The proposed methodology is applied to the control a kite-based marine hydrokinetic (MHK) system, which executes high-speed, repetitive flight paths with the objective of maximizing its lap-averaged power output. The proposed approach is validated via simulations of a medium-fidelity nonlinear model of a kite-based MHK system, and the results demonstrate robust and fast convergence of the kite to power-optimal flight patterns.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Cobb, Mitchell and Reed, James and Wu, Maxwell and Mishra, Kirti D. and Barton, Kira and Vermillion, Chris}, year={2022}, month={Apr} }
 @article{naik_beknalkar_reed_mazzoleni_fathy_vermillion_2023, title={Pareto Optimal and Dual-Objective Geometric and Structural Design of an Underwater Kite for Closed-Loop Flight Performance}, volume={145}, ISSN={["1528-9028"]}, DOI={10.1115/1.4055978}, abstractNote={Abstract
               This paper presents the formulation and results for a control-aware optimization of the combined geometric and structural design of an energy-harvesting underwater kite. Because kite-based energy-harvesting systems, both airborne and underwater, possess strong coupling between closed-loop flight control, geometric design, and structural design, consideration of all three facets of the design within a single codesign framework is highly desirable. However, while prior literature has addressed one or two attributes of the design at a time, this work constitutes the first comprehensive effort aimed at addressing all three. In particular, focusing on the goals of power maximization and mass minimization, we present a codesign formulation that fuses a geometric optimization tool, structural optimization tool, and closed-loop flight efficiency map. The resulting integrated codesign tool is used to address two mathematical optimization formulations that exhibit subtle differences: a Pareto optimal formulation and a dual-objective formulation that focuses on a weighted power-to-mass ratio as the techno-economic metric of merit. Based on the resulting geometric and structural designs, using a mediumfidelity closed-loop simulation tool, the proposed formulation is shown to achieve more than three times the powerto-mass ratio of a previously published, unoptimized benchmark design.}, number={1}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Naik, Kartik and Beknalkar, Sumedh and Reed, James and Mazzoleni, Andre and Fathy, Hosam and Vermillion, Chris}, year={2023}, month={Jan} }
 @article{groelke_earnhardt_borek_vermillion_2021, title={A Predictive Command Governor-Based Adaptive Cruise Controller With Collision Avoidance for Non-Connected Vehicle Following}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3112113}, abstractNote={This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty non-connected lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles’ position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, “safe following distance” is based on the ego vehicle’s ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Groelke, Ben and Earnhardt, Christian and Borek, John and Vermillion, Chris}, year={2021}, month={Oct} }
 @article{groelke_borek_earnhardt_vermillion_2021, title={Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks}, volume={143}, ISSN={["1528-9028"]}, DOI={10.1115/1.4049544}, abstractNote={Abstract
               This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.}, number={6}, journal={Journal of Dynamic Systems, Measurement, and Control}, author={Groelke, Ben and Borek, John and Earnhardt, Christian and Vermillion, Chris}, year={2021}, month={Jun}, pages={061009} }
 @article{haydon_mishra_keyantuo_panagou_chow_moura_vermillion_2021, title={Dynamic Coverage Meets Regret: Unifying Two Control Performance Measures for Mobile Agents in Spatiotemporally Varying Environments}, ISSN={["0743-1546"]}, DOI={10.1109/CDC45484.2021.9682826}, abstractNote={Numerous mobile robotic applications require agents to persistently explore and exploit spatiotemporally varying, partially observable environments. Ultimately, the mathematical notion of regret, which quite simply represents the instantaneous or time-averaged difference between the optimal reward and realized reward, serves as a meaningful measure of how well the agents have exploited the environment. However, while numerous theoretical regret bounds have been derived within the machine learning community, restrictions on the manner in which the environment evolves preclude their application to persistent missions. On the other hand, meaningful theoretical properties can be derived for the related concept of dynamic coverage, which serves as an exploration measurement but does not have an immediately intuitive connection with regret. In this paper, we demonstrate a clear correlation between an appropriately defined measure of dynamic coverage and regret, then go on to derive performance bounds on dynamic coverage as a function of the environmental parameters. We evaluate the correlation for several variants of an airborne wind energy system, for which the objective is to adjust the operating altitude in order to maximize power output in a spatiotemporally evolving wind field.}, journal={2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)}, author={Haydon, Ben and Mishra, Kirti D. and Keyantuo, Patrick and Panagou, Dimitra and Chow, Fotini and Moura, Scott and Vermillion, Chris}, year={2021}, pages={521–526} }
 @misc{vermillion_cobb_fagiano_leuthold_diehl_smith_wood_rapp_schmehl_olinger_et al._2021, title={Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems}, volume={52}, ISSN={["1872-9088"]}, DOI={10.1016/j.arcontrol.2021.03.002}, abstractNote={Airborne wind energy systems convert wind energy into electricity using tethered flying devices, typically flexible kites or aircraft. Replacing the tower and foundation of conventional wind turbines can substantially reduce the material use and, consequently, the cost of energy, while providing access to wind at higher altitudes. Because the flight operation of tethered devices can be adjusted to a varying wind resource, the energy availability increases in comparison to conventional wind turbines. Ultimately, this represents a rich topic for the study of real-time optimal control strategies that must function robustly in a spatiotemporally varying environment. With all of the opportunities that airborne wind energy systems bring, however, there are also a host of challenges, particularly those relating to robustness in extreme operating conditions and launching/landing the system (especially in the absence of wind). Thus, airborne wind energy systems can be viewed as a control system designer's paradise or nightmare, depending on one's perspective. This survey article explores insights from the development and experimental deployment of control systems for airborne wind energy platforms over approximately the past two decades, highlighting both the optimal control approaches that have been used to extract the maximal amount of power from tethered systems and the robust modal control approaches that have been used to achieve reliable launch, landing, and extreme wind operation. This survey will detail several of the many prototypes that have been deployed over the last decade and will discuss future directions of airborne wind energy technology as well as its nascent adoption in other domains, such as ocean energy.}, journal={ANNUAL REVIEWS IN CONTROL}, author={Vermillion, Chris and Cobb, Mitchell and Fagiano, Lorenzo and Leuthold, Rachel and Diehl, Moritz and Smith, Roy S. and Wood, Tony A. and Rapp, Sebastian and Schmehl, Roland and Olinger, David and et al.}, year={2021}, pages={330–357} }
 @article{haydon_cole_dunn_keyantuo_chow_moura_vermillion_2022, title={Generalized Empirical Regret Bounds for Control of Renewable Energy Systems in Spatiotemporally Varying Environments}, volume={144}, ISSN={["1528-9028"]}, DOI={10.1115/1.4052396}, abstractNote={Abstract
               This paper focuses on the empirical derivation of regret bounds for mobile systems that can optimize their locations in real-time within a spatiotemporally varying renewable energy resource. The case studies in this paper focus specifically on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In this work, we make use of a very large synthetic dataset, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches. In addition, we use dimensional analysis to generalize the aforementioned results to other spatiotemporally varying environments, making the results applicable to a wider variety of renewably powered mobile systems. Finally, to deal with more general environmental mean models, we introduce a novel approach to modify calculable regret bounds to accommodate any mean model through what we term an “effective spatial domain.”}, number={4}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Haydon, Ben and Cole, Jack and Dunn, Laurel and Keyantuo, Patrick and Chow, Fotini K. and Moura, Scott and Vermillion, Chris}, year={2022}, month={Apr} }
 @article{borek_goelke_earnhardt_vermillion_2020, title={Hierarchical Control of Heavy-Duty Trucks Through Signalized Intersections With Non-Deterministic Signal Timing}, volume={23}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3128068}, abstractNote={This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy’s performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.}, number={8}, journal={IEEE Transactions on Intelligent Transportation Systems}, author={Borek, John and Goelke, Ben and Earnhardt, Christian and Vermillion, Chris}, year={2020}, month={Sep}, pages={13769–13781} }
 @article{earnhardt_groelke_borek_vermillion_2021, title={Hierarchical Model Predictive Control Approaches for Strategic Platoon Engagement of Heavy-Duty Trucks}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2021.3076963}, abstractNote={For a group of vehicles, collaborative platooning can be valuable in certain situations due to aerodynamic drag reduction, while being detrimental or altogether impractical in others. This paper details a platoon engagement/disengagement controller, which alternates between velocity trajectory optimization (VTO) in isolation and a fused platooning and VTO approach, capable of disengaging a platoon during segments detrimental to fuel savings and rejoining the platoon afterwards without significant energy expenditure. The proposed approach leverages parallel model predictive control (MPC) computations that (i) can identify when a platoon should be engaged/disengaged and (ii) performs the engagement/disengagement in a fuel-optimal manner. Using a medium-fidelity Simulink model furnished by Volvo, two real-world trucking routes, and two different traffic scenarios, the effectiveness of the approach was compared against non-platooning VTO, as well as a baseline controller that uses a PI-based cruise controller that incorporates Gipps car-following model. Results for a two-vehicle platoon using a 1 vehicle following distance reveal a 9.6% to 11.9% decrease in aggregate fuel consumption for both vehicles within the platoon, as compared to the baseline, highlighting the ability to disengage and rejoin a platoon without expending unnecessary fuel consumption. Additionally, the approaches for disengaging a platoon result in a 4-7% decrease in aggregate fuel consumption, as compared to a VTO-only approach.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Vermillion, Chris}, year={2021}, month={May} }
 @article{mishra_reed_wu_barton_vermillion_2021, title={Hierarchical Structures for Economic Repetitive Control}, ISSN={["0743-1546"]}, DOI={10.1109/CDC45484.2021.9683000}, abstractNote={For many emerging repetitive control applications such as wind and marine energy generation systems, gait-cycle following in legged locomotion, remote sensing, surveillance, and reconnaissance, the primary objective for repetitive control (RC) is optimization of a cycle cost such as the lap-averaged power generated and metabolic cost of locomotion, as opposed to the classical requirement of tracking a known reference trajectory by the system output. For this newer class of applications, only a range of reference trajectories suitable for cyclic operation is known a priori, the range potentially encapsulating various operational constraints, and as part of repetitive control, it is desired that over a number of operation cycles, the cycle cost, or the economic metric, is optimized. With this underlying motivation, a hierarchical solution is presented, wherein the inner loop includes a classical repetitive controller that tracks a reference trajectory of known period, and the outer loop iteratively learns the desired reference trajectory using a combination of the system and cost function models and the measured cycle cost. This approach results in optimum steady-state cyclic operation. A steepest descent type algorithm is used in the outer loop, and via Lyapunov-like arguments, the existence of tuning parameters resulting in robust and optimal steady-state cyclic operation is discussed. Appropriate guidelines for parameter tuning are presented, and the proposed method is numerically validated using an example of an inverted pendulum.}, journal={2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)}, author={Mishra, Kirti D. and Reed, James and Wu, Maxwell and Barton, Kira and Vermillion, Chris}, year={2021}, pages={5838–5844} }
 @article{cobb_reed_daniels_siddiqui_wu_fathy_barton_vermillion_2021, title={Iterative Learning-Based Path Optimization With Application to Marine Hydrokinetic Energy Systems}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2021.3070526}, abstractNote={This article presents an iterative learning control (ILC)-based approach for optimizing the flight path geometry of a tethered marine hydrokinetic (MHK) energy system. This type of system, which replaces the tower of a conventional system with a tether and a lifting body, can capture energy either through an on-board rotor or by driving a generator with tension in the tether. In the latter mode of operation, which represents the focal point of this effort, net positive energy is generated over one cycle of high-tension spool-out followed by low-tension spool-in. Because the net energy generation is sensitive to the shape of the flown path, we employ an iterative learning update law to adapt the path shape from one lap to the next. This update law is complemented with an iterative power take-off (PTO) controller, which adjusts the spooling profile at each iteration to ensure zero net spooling. We present and validate the proposed control approach in both uniform and spatiotemporally varying turbulent flow environments, based on a realistic ocean model detailed in this article. Finally, based on simulation results across a wide range of excitation levels, we perform a simulation-based assessment of convergence properties, comparing these results against bounds derived in the authors’ prior work.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Cobb, Mitchell and Reed, James and Daniels, Joshua and Siddiqui, Ayaz and Wu, Max and Fathy, Hosam and Barton, Kira and Vermillion, Chris}, year={2021}, month={Apr} }
 @article{reed_wu_barton_vermillion_mishra_2021, title={Library-Based Norm-Optimal Iterative Learning Control}, ISSN={["0743-1546"]}, DOI={10.1109/CDC45484.2021.9682812}, abstractNote={This paper presents a new iterative learning control (ILC) methodology, termed library-based norm-optimal ILC, which optimally accounts for variations in measurable disturbances and plant parameters from one iteration to the next. In this formulation, previous iteration-varying disturbance and/or plant parameters, along with the corresponding control and error sequences, are intelligently maintained in a dynamically evolving library. The library is then referenced at each iteration, in order to base the new control sequence on the most relevant prior iterations, according to an optimization metric. In contrast with the limited number of library-based ILC methodologies pursued in the literature, the present work (i) selects provably optimal interpolation weights, (ii) presents methods for starting with an empty library and intelligently truncating the library when it becomes too large, and (iii) demonstrates convergence to an optimal performance value. To demonstrate the effectiveness of our new methodology, we simulate our library-based norm-optimal ILC method on a linear time-varying model of a micro-robotic deposition system.}, journal={2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)}, author={Reed, James and Wu, Maxwell and Barton, Kira and Vermillion, Chris and Mishra, Kirti D.}, year={2021}, pages={5851–5857} }
 @article{wu_vermillion_barton_2021, title={Point-to-Point Repetitive Control with Optimal Tracking Time}, ISSN={["0743-1546"]}, DOI={10.1109/CDC45484.2021.9683731}, abstractNote={For systems that execute tasks repetitively, learning control strategies such as iterative learning control and repetitive control have proven to be useful tools for mitigating the effects of model uncertainty to improve system performance. While such strategies have been used to solve the point-to-point motion tracking problem, existing techniques require either that the reference positions are tracked at the same time within each iteration, or that the initial conditions are reset between each iteration of the task. This paper aims to relax these assumptions through the development of a two stage repetitive control framework that drives the system to periodically track a sequence of temporally-varying reference positions by using minimal control effort. Here, the first stage updates the control signal such that the system accurately tracks the reference positions, while the second stage modifies the tracking time requirements of the reference positions to minimize the applied control effort. The effectiveness of the control strategy is demonstrated via simulated application of the algorithm on a mass-spring-damper system.}, journal={2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)}, author={Wu, Maxwell and Vermillion, Chris and Barton, Kira}, year={2021}, pages={5845–5850} }
 @article{earnhardt_groelke_borek_naghnaeian_vermillion_2021, title={A Multirate, Multiscale Economic Model Predictive Control Approach for Velocity Trajectory Optimization of a Heavy Duty Truck}, volume={143}, ISSN={["1528-9028"]}, DOI={10.1115/1.4048658}, abstractNote={Abstract
               This paper introduces a hierarchical economic model predictive control (MPC) approach for maximizing the fuel economy of a heavy-duty truck, which simultaneously accounts for aggregate terrain changes that occur over very long length scales, fine terrain changes that occur over shorter length scales, and lead vehicle behavior that can vary over much shorter time/length scales. To accommodate such disparate time and length scales, the proposed approach uses a multilayer MPC approach wherein the upper-level MPC uses a long distance step, a long time-step, and coarse discretization to account for the slower changes in road grade, while the lower-level MPC uses a shorter time-step to account for fine variations in road grade and rapidly changing lead vehicle behavior. The benefit of this multirate, multiscale approach is that the lower-level MPC leverages the upper-level's sufficiently long look-ahead while allowing for safe vehicle following and adjustment to fine road grade variations. The proposed strategy has been evaluated over four real-world road profiles in both open-highway and traffic environments, using a medium-fidelity simulink model furnished by Volvo Group North America. Compared with a conventional cruise control system plus vehicle following controller as a baseline, results show 4–5% fuel savings in an open highway setting and 6–8% fuel savings in the presence of traffic, without compromising trip time.}, number={3}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Earnhardt, Christian and Groelke, Ben and Borek, John and Naghnaeian, Mohammad and Vermillion, Chris}, year={2021}, month={Mar} }
 @article{deese_tkacik_vermillion_2021, title={Gaussian Process-Driven, Nested Experimental Co-Design: Theoretical Framework and Application to an Airborne Wind Energy System}, volume={143}, ISSN={["1528-9028"]}, DOI={10.1115/1.4049011}, abstractNote={Abstract
               This paper presents and experimentally evaluates a nested combined plant and controller optimization (co-design) strategy that is applicable to complex systems that require extensive simulations or experiments to evaluate performance. The proposed implementation leverages principles from Gaussian process (GP) modeling to simultaneously characterize performance and uncertainty over the design space within each loop of the co-design framework. Specifically, the outer loop uses a GP model and batch Bayesian optimization to generate a batch of candidate plant designs. The inner loop utilizes recursive GP modeling and a statistically driven adaptation procedure to optimize control parameters for each candidate plant design in real-time, during each experiment. The characterizations of uncertainty made available through the GP models are used to reduce both the plant and control parameter design space as the process proceeds, and the optimization process is terminated once sufficient design space reduction has been achieved. The process is validated in this work on a lab-scale experimental platform for characterizing the flight dynamics and control of an airborne wind energy (AWE) system. The proposed co-design process converges to a design space that is less than 8% of the original design space and results in more than a 50% increase in performance.}, number={5}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Deese, Joe and Tkacik, Peter and Vermillion, Chris}, year={2021}, month={May} }
 @article{reed_cobb_daniels_siddiqui_muglia_vermillion_2020, title={Hierarchical Control Design and Performance Assessment of an Ocean Kite in a Turbulent Flow Environment}, volume={53}, ISSN={["2405-8963"]}, DOI={10.1016/j.ifacol.2020.12.1887}, abstractNote={This paper presents a hierarchical control framework for a kite-based marine hydrokinetic (MHK) system that executes power-augmenting cross-current flight, along with simulation results based on a high-fidelity turbulent flow model that is representative of flow conditions in the Gulf Stream. The hierarchical controller is used to robustly regulate both the kite's flight path and the intra-cycle spooling behavior, which is ultimately used to realize net positive energy production at a base station motor/generator system. Two configurations are examined in this paper: one in which the kite is suspended from a surface-mounted platform, and another in which the kite is deployed from the seabed. To evaluate the robustness of this control framework in a realistic ocean environment, we present simulation results whereby we superimpose low-frequency data from the Mid Atlantic Bight South Atlantic Bight Regional Ocean Modeling System and acoustic Doppler current profiler measurements with a high-frequency turbulence model, resulting in a high-fidelity 3D spatiotemporal flow field that is presented to the kite system. Based on this simulation framework, we demonstrate the effectiveness of the control system both in terms of robust flight and power generation.}, number={2}, journal={IFAC PAPERSONLINE}, author={Reed, James and Cobb, Mitchell and Daniels, Joshua and Siddiqui, Ayaz and Muglia, Michael and Vermillion, Chris}, year={2020}, pages={12726–12732} }
 @article{siddiqui_naik_cobb_granlund_vermillion_2020, title={Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite}, volume={142}, ISSN={["1528-9028"]}, DOI={10.1115/1.4047825}, abstractNote={Abstract
               This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. To address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.}, number={11}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Siddiqui, Ayaz and Naik, Kartik and Cobb, Mitchell and Granlund, Kenneth and Vermillion, Chris}, year={2020}, month={Nov} }
 @article{daniels_reed_cobb_siddiqui_vermillion_2020, title={Optimal Cyclic Spooling Control for Kite-Based Energy Systems}, volume={53}, ISSN={["2405-8963"]}, DOI={10.1016/j.ifacol.2020.12.1883}, abstractNote={This paper presents a control strategy for optimizing the the spooling speeds of tethered energy harvesting systems that generate energy through cyclic spooling motions which consist of high-tension spool-out and low-tension spool-in. Specifically, we fuse continuous-time optimal control tools, including Pontryagin’s Maximum Principle, with an iteration domain co-state correction, to develop an optimal spooling controller for energy extraction. In this work, we focus our simulation results specifically on an ocean kite system where the goal is to optimize the spooling profile while remaining at a consistent operating depth and corresponding average tether length. This paper demonstrates a 14-45% improvement (depending on the operating tether length and environmental flow speed) in power generation compared to a baseline, heuristic, control strategy.}, number={2}, journal={IFAC PAPERSONLINE}, author={Daniels, Joshua and Reed, James and Cobb, Mitchell and Siddiqui, Ayaz and Vermillion, Chris}, year={2020}, pages={12719–12725} }
 @article{deese_vermillion_2021, title={Recursive Gaussian Process-Based Adaptive Control, With Application to a Lighter-Than-Air Wind Energy System}, volume={29}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2020.3014159}, abstractNote={This brief presents a nonmodel-based adaptive control technique that combines principles from machine learning and iterative design optimization with those of continuous-time, falsification-based adaptive control. At the crux of the proposed control strategy are two core elements. First, the recursive Gaussian Process (RGP) modeling is used to maintain an online characterization of the system at hand without the need to maintain a complete database of previously collected measurements (which is required in traditional GP modeling). Second, an adaptation strategy is employed that falsifies candidate controllers from a continuous candidate design space based on desired performance specifications and statistical hypothesis testing. In specific, the control parameter design space is explored by selecting points associated with high uncertainty. Through the use of statistical hypothesis testing, regions of the design space determined to be suboptimal at a user-specified level of confidence are rejected in order to converge to an optimal set of control parameters. The RGP-based adaptation is validated through simulations and laboratory-scale experiments using an airborne wind energy case study. Through these studies, the RGP-based adaptation approach is shown to be effective and is shown to exhibit favorable convergence times when compared with a mature adaptive control technique, extremum seeking (ES).}, number={4}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Deese, Joe and Vermillion, Chris}, year={2021}, month={Jul}, pages={1823–1830} }
 @inproceedings{cobb_barton_fathy_vermillion_2019, title={An Iterative Learning Approach for Online Flight Path Optimization for Tethered Energy Systems Undergoing Cyclic Spooling Motion}, ISBN={9781538679265}, url={http://dx.doi.org/10.23919/acc.2019.8814773}, DOI={10.23919/acc.2019.8814773}, abstractNote={This paper presents an iterative learning based approach for optimizing the crosswind flight path of an energy-harvesting tethered system that executes cyclic spool-inlspool-out motions. Through the combination of a high-tension crosswind spool-out motion (made possible through a high lift wing) and low-tension spool-in motion, net energy is generated at every cycle. Because the net energy generated by the system is highly sensitive to the crosswind flight patterns used on spool-out, and because the motions of the system are repetitive, we use an iterative learning formulation to optimize the flight patterns in real time. Using a medium-fidelity dynamic model, we demonstrate that an optimization approach based on iterative learning control (ILC) significantly increases the average power generated by such a system.}, booktitle={2019 American Control Conference (ACC)}, publisher={IEEE}, author={Cobb, Mitchell and Barton, Kira and Fathy, Hosam and Vermillion, Chris}, year={2019}, month={Jul} }
 @inproceedings{bin-karim_muglia_vermillion_2019, title={Centralized Position optimization of Multiple Agents in Spatiotemporally-Varying Environment: a Case Study with Relocatable Energy-Harvesting Autonomous Underwater Vehicles in the Gulf Stream}, ISBN={9781728127675}, url={http://dx.doi.org/10.1109/ccta.2019.8920645}, DOI={10.1109/ccta.2019.8920645}, abstractNote={This paper evaluates a strategy for using multiple energy-harvesting autonomous underwater vehicles (AUVs) to extract hydrokinetic energy out of a spatiotemporally-varying Gulf Stream (GS) resource. When anchored, the conceptual AUV can generate energy with on-board turbines, and can relocate itself when needed. Model predictive control (MPC)based centralized optimization is used to optimize the locations of a team of AUVs along a cross-stream transect. To maintain an accurate estimate of the transect flow profile, a Kalman filter-based estimator is used to blend flow speed measurements from the AUVs with intermittent auxiliary flow speed data from a High Frequency Radar Network (HFRNet). To characterize the uncertainty of the GS flow profile estimation, variance of flow speed is modeled using tools from Gaussian Process (GP). The MPC strategy proposed in the paper ensures an appropriate balance between learning the exact location of GS jet (termed exploration) and maximizing overall power generation (termed exploitation). The importance of estimating the flow pattern in the transect with high resolution is shown through simulation results.}, booktitle={2019 IEEE Conference on Control Technology and Applications (CCTA)}, publisher={IEEE}, author={Bin-Karim, Shamir and Muglia, Michael and Vermillion, Christopher}, year={2019}, month={Aug} }
 @article{baheri_vermillion_2019, title={Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems}, volume={141}, ISSN={0022-0434 1528-9028}, url={http://dx.doi.org/10.1115/1.4043224}, DOI={10.1115/1.4043224}, abstractNote={This paper presents a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored toward instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian process (GP); then, Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of codesign literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of batch Bayesian optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for Altaeros' buoyant airborne turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.}, number={9}, journal={Journal of Dynamic Systems, Measurement, and Control}, publisher={ASME International}, author={Baheri, Ali and Vermillion, Chris}, year={2019}, month={May} }
 @article{borek_groelke_earnhardt_vermillion_2020, title={Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment}, volume={28}, ISSN={["1558-0865"]}, DOI={10.1109/TCST.2019.2918472}, abstractNote={This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, off-line dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include a nonlinear time-based formulation that directly penalizes the predicted fuel consumption, a nonlinear time-based formulation that penalizes the braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck’s longitudinal and engine dynamics, we analyze the optimization’s performance on four highway routes under various traffic scenarios. Results demonstrate 3.7%–8.3% fuel economy improvement on highway routes without traffic and 6.5%–10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.}, number={5}, journal={IEEE Transactions on Control Systems Technology}, author={Borek, John and Groelke, Ben and Earnhardt, Christian and Vermillion, Chris}, year={2020}, month={Sep}, pages={1652–1664} }
 @article{cobb_barton_fathy_vermillion_2019, title={Iterative Learning-Based Path Optimization for Repetitive Path Planning, With Application to 3-D Crosswind Flight of Airborne Wind Energy Systems}, ISSN={1063-6536 1558-0865 2374-0159}, url={http://dx.doi.org/10.1109/tcst.2019.2912345}, DOI={10.1109/tcst.2019.2912345}, abstractNote={This paper presents an iterative learning approach for optimizing the course geometry in repetitive path following applications. In particular, we focus on airborne wind energy (AWE) systems. Our proposed algorithm consists of two key features. First, a recursive least squares (RLS) fit is used to construct an estimate of the behavior of the performance index. Second, an iteration-to-iteration path adaptation law is used to adjust the path shape in the direction of optimal performance. We propose two candidate update laws, both of which parallel the mathematical structure of common iterative learning control (ILC) update laws but replace the tracking-dependent terms with terms based on the performance index. We apply our formulation to the iterative crosswind path optimization of an AWE system, where the goal is to maximize the average power output over a figure-8 path. Using a physics-based AWE system model, we demonstrate that the proposed adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions under both constant and real wind profiles.}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Cobb, Mitchell K. and Barton, Kira and Fathy, Hosam and Vermillion, Chris}, year={2019}, pages={1–13} }
 @article{tandon_divi_muglia_vermillion_mazzoleni_2019, title={Modeling and dynamic analysis of a mobile underwater turbine system for harvesting Marine Hydrokinetic Energy}, volume={187}, ISSN={["0029-8018"]}, DOI={10.1016/j.oceaneng.2019.05.051}, abstractNote={We present the modeling and dynamic analysis of a Mobile Underwater Turbine System, a novel integration of Autonomous Underwater Vehicles and Hydrokinetic Turbines, for harvesting Marine Hydrokinetic Energy from the Gulf Stream. The Gulf Stream, an ocean current that flows off the coast of North Carolina, is a source of hydrokinetic energy. However, the meandering nature of the Gulf Stream makes it challenging to harvest the full energy potential of the stream using fixed turbine systems. One possible solution for increasing the amount of energy that can be extracted from the Gulf Stream involves using a mobile underwater energy harvester system that can follow the meandering stream so as to remain in regions of maximum energy potential. The framework for the conceptual design, and studies focusing on the feasibility of such a system, have been presented previously in (Divi, 2017). The focus of this paper is a mathematical model of the system which has been developed to analyze the dynamics of such a system, along with parametric studies utilizing this model to come up with a system with an optimized set of design parameters. A 6-DOF analytical model of the system is developed to gain an understanding of the system's dynamic behavior and stability. A bead-based tether model is further developed to analyze the behavior of the system when it is anchored and harvesting energy. A study regarding the effects of tether parameters such as the number of tether elements, the spring constant, and the damping coefficient of the tether on the tether behavior and computation time required for analysis, is put forth to help determine an optimal set of tether parameters. In addition, a set of system parameters such as turbine diameter, hull diameter, L/D ratio of the hull and ballast tank size, are analyzed to see how they affect the net energy produced and the maximum distance travelled by the system. Finally, three modes of power transfer to the shore are considered, and an optimization algorithm is presented and used to find the best set of parameters suited for maximum energy transfer for each mode.}, journal={Ocean Engineering}, author={Tandon, Shubham and Divi, Sathvik and Muglia, Michael and Vermillion, Chris and Mazzoleni, Andre}, year={2019}, month={Sep}, pages={106069} }
 @inproceedings{dunn_vermillion_chow_moura_2019, title={On Wind Speed Observability for Altitude Control of Airborne Wind Energy Systems}, booktitle={Proceedings of the 2019 American Control Conference}, author={Dunn, Laurel and Vermillion, Chris and Chow, Fotini Katopodes and Moura, Scott}, year={2019} }
 @inproceedings{borek_groelke_earnhardt_vermillion_2019, title={Optimal Control of Heavy-duty Trucks in Urban Environments Through Fused Model Predictive Control and Adaptive Cruise Control}, ISBN={9781538679265}, url={http://dx.doi.org/10.23919/acc.2019.8814703}, DOI={10.23919/acc.2019.8814703}, abstractNote={This paper presents an optimal control strategy for heavy-duty trucks that minimizes fuel consumption in urban environments using infrastructure-to-vehicle communication. This strategy uses an online convex model predictive control strategy that balances a trade-off between reducing braking effort and mechanical energy expenditure and tracking an optimal desired velocity setpoint. The desired velocity profile comes from two different sources, depending on the vehicle's proximity to signalized intersections. In the presence of signalized intersections, the desired velocity profile is constructed to timely arrive at intersections when the light is green. With no intersections present, the desired velocity profile comes from a global, offline dynamic programming optimization, which is available to the vehicle through cloud connectivity. In parallel with MPC, we implement a continuous vehicle following controller to maintain safe vehicle following in the presence of traffic. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal vehicle and engine dynamics, we characterize the control strategy's performance over a single city route with multiple signal timing and traffic patterns. Results demonstrate 10-25% reduction in fuel consumption, compared to a baseline control strategy. Furthermore, we show that this control strategy is computationally feasible for vehicle implementation. Finally, we present a detailed breakdown of where the fuel consumption reductions originate.}, booktitle={2019 American Control Conference (ACC)}, publisher={IEEE}, author={Borek, John and Groelke, Ben and Earnhardt, Christian and Vermillion, Chris}, year={2019}, month={Jul} }
 @inproceedings{deese_vermillion_2019, title={Recursive Gaussian Process-based Adaptive Control: Theoretical Framework and Application to an Airborne Wind Energy System}, ISBN={9781728127675}, url={http://dx.doi.org/10.1109/ccta.2019.8920712}, DOI={10.1109/ccta.2019.8920712}, abstractNote={This paper presents a unique adaptive control methodology that fuses real-time Gaussian process (GP) modeling with statistically-driven design space reduction in order to converge upon an optimal set of control parameters. The proposed approach represents a dramatic departure from traditional adaptive control techniques that assume significant underlying structural knowledge of the system model, in addition to representing a significant departure from traditional GP-based regression, which has traditionally been applied to iterative design optimization problems rather than real-time control. Within the proposed adaptive control strategy, GP modeling is tailored to control systems with relatively short time steps through the use of a recursive GP (RGP)-based update to process new information as it is made available, which circumvents the need to retain a complete database of prior data. Exploration of the design space is achieved by selecting design points associated with large prediction variance. At each time step, suboptimal points within the design space are rejected based upon the uncertainty characterization from the GP model and statistical hypothesis testing. The RGP-based adaptive control law has been validated using a numerical model of an airborne wind energy system.}, booktitle={2019 IEEE Conference on Control Technology and Applications (CCTA)}, publisher={IEEE}, author={Deese, Joe and Vermillion, Chris}, year={2019}, month={Aug} }
 @article{kalabic_li_vermillion_kolmanovsky_2019, title={Reference governors for chance-constrained systems}, volume={109}, ISSN={["1873-2836"]}, DOI={10.1016/j.automatica.2019.108500}, abstractNote={This paper presents a reference governor formulation that is applicable to systems with stochastic disturbances and achieves constraint satisfaction properties that are analogous to those of conventional reference governors. In particular, the reference governor proposed herein is shown to enforce chance constraints, guarantee a form of eventual recursive feasibility, and guarantee almost-sure convergence to constant, constraint-admissible reference inputs. It can also be applied to enforce constraints for closed-loop systems with state observers. This stands in contrast with traditional reference governor techniques, which must be heuristically tuned in order to achieve a balance between constraint satisfaction and the size of achievable steady-state references in the presence of stochastic disturbances. A numerical example is reported which illustrates the operation of the reference governor for chance-constrained systems and is compared to the conventional, robust approach.}, journal={AUTOMATICA}, author={Kalabic, Uros V. and Li, Nan I. and Vermillion, Christopher and Kolmanovsky, Ilya V.}, year={2019}, month={Nov} }
 @inproceedings{groelke_earnhardt_borek_vermillion_2018, title={A Cascaded Multi-Rate Model Predictive Control and Reference Governor Approach for Real-Time Velocity Optimization in the Presence of Traffic}, booktitle={Proceedings of AVEC 2018}, author={Groelke, Ben and Earnhardt, Christian and Borek, John and Vermillion, Chris}, year={2018} }
 @inproceedings{groelke_borek_earnhardt_li_geyer_vermillion_2018, title={A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks}, ISBN={9781538654286}, url={http://dx.doi.org/10.23919/acc.2018.8431050}, DOI={10.23919/acc.2018.8431050}, abstractNote={This paper provides a comparative assessment of three control strategies that fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC) in an effort to minimize fuel consumption for a heavy-duty truck. The online MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. Three candidate economic MPC formulations are evaluated: a time-based formulation that directly penalizes predicted fuel consumption, a simplified time-based formulation that penalizes braking effort in place of fuel consumption, and a distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity based on DP. The performance of each approach is presented for three representative route profiles, using a medium-fidelity proprietary Volvo model of the heavy-duty truck's longitudinal dynamics. Results demonstrate 4-7% fuel economy improvement across all three formulations, when compared to a baseline strategy. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control approach.}, booktitle={2018 Annual American Control Conference (ACC)}, publisher={IEEE}, author={Groelke, Ben and Borek, John and Earnhardt, Christian and Li, Jian and Geyer, Stephen and Vermillion, Chris}, year={2018}, month={Jun} }
 @article{bafandeh_bin-karim_baheri_vermillion_2018, title={A comparative assessment of hierarchical control structures for spatiotemporally-varying systems, with application to airborne wind energy}, volume={74}, ISSN={0967-0661}, url={http://dx.doi.org/10.1016/j.conengprac.2018.02.008}, DOI={10.1016/j.conengprac.2018.02.008}, abstractNote={Optimal control in a spatiotemporally varying environment is difficult, especially if the environment is partially observable. Altitude optimization of an airborne wind energy (AWE) system, in which the tower and foundation of a contemporary wind turbine is replaced by tethers and a lifting body, is a challenging problem of this kind. The wind velocity changes both spatially and temporally, and it can only be measured at the altitude where the system is flying, making the problem partially observable. In this work, we propose and evaluate hierarchical structures for the aforementioned problem, which fuse coarse, global for the chosen grid resolution and prediction horizon, where applicable control with fine, local control. These controllers leverage the advantages of both fine, local and coarse, global control schemes, while addressing their limitations. We show through simulation, using the real wind velocity data, that the hierarchical structures outperform legacy control strategies in terms of net energy generation.}, journal={Control Engineering Practice}, publisher={Elsevier BV}, author={Bafandeh, Alireza and Bin-Karim, Shamir and Baheri, Ali and Vermillion, Christopher}, year={2018}, month={May}, pages={71–83} }
 @inproceedings{bin-karim_muglia_mazzoleni_vermillion_2018, title={Control of a Relocatable Energy-Harvesting Autonomous Underwater Vehicle in a Spatiotemporally-Varying Gulf Stream Resource}, ISBN={9781538654286}, url={http://dx.doi.org/10.23919/acc.2018.8431318}, DOI={10.23919/acc.2018.8431318}, abstractNote={This paper describes and evaluates, through data-driven simulation, a strategy for using a relocatable autonomous underwater vehicle (AUV) with on-board turbines to extract hydrokinetic energy out of a shifting Gulf Stream. The conceptual AUV generates energy while anchored, and can relocate itself when needed. In order to maintain an estimate of the Gulf Stream flow profile that can be referenced for control, an estimator blends a measurement of the flow speed at the AUV's location with intermittent secondary flow speed data (which is available through a High Frequency Radar Network (HFRNet)). Model predictive control (MPC)-based spatiotemporal optimization is then used to optimize the location of the AUV along a cross-stream transect whose velocity profile varies as a function of time and cross-stream location. The proposed MPC strategy is structured in a way that the need for instantaneous power maximization (termed exploitation) is balanced with the need to maintain an accurate map of ocean current speed versus location on transect (termed exploration). The control strategy is validated through data from a numerical model of coastal circulation dynamics.}, booktitle={2018 Annual American Control Conference (ACC)}, publisher={IEEE}, author={Bin-Karim, Shamir and Muglia, Mike and Mazzoleni, Andre and Vermillion, Christopher}, year={2018}, month={Jun} }
 @article{deodhar_vermillion_2019, title={Convergence Analysis and Experimental Validation of a Fused Numerical/Experimental Active System Optimization Framework}, volume={141}, ISSN={["1528-9028"]}, DOI={10.1115/1.4042032}, abstractNote={This paper presents a convergence analysis and experimental validation of an iterative design optimization framework that fuses numerical simulations with experiments. At every iteration, a G-optimal design generates a set of simulations and experiments that are used to characterize response surfaces. A subset of the experiments termed as the training points are used to fit a combined numerical/experimental response. This numerical response is obtained as a result of numerical model correction via experiments. The quality of fit for this combined response is evaluated using the remaining validation points. Based on the quality of fit, the feasible design space is reduced for a given confidence interval using hypothesis testing. A convergence analysis of the framework quantifies the closeness of the corrected numerical model to the true system as a function of response estimation error. This design optimization framework, along with the convergence result, is validated through an airborne wind energy (AWE) application using a lab-scale water channel setup. The quality of flight is greatly improved by optimizing the center of mass location, pitch angle set point, horizontal and vertical stabilizer areas using an effective experimental infusion as compared to a pure numerically optimized design.}, number={4}, journal={JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME}, author={Deodhar, Nihar and Vermillion, Christopher}, year={2019}, month={Apr} }
 @inproceedings{deodhar_vermillion_2018, title={Experimentally-Infused Active System Optimization Framework: Theoretical Convergence Analysis and Airborne Wind Energy Case Study}, ISBN={9780791851753}, url={http://dx.doi.org/10.1115/detc2018-85305}, DOI={10.1115/detc2018-85305}, abstractNote={This research presents a convergence analysis for an iterative framework for optimizing plant and controller parameters for active systems. The optimization strategy fuses expensive yet valuable experiments with less accurate yet cheaper simulations. The numerical model is improved at each iteration through a cumulative correction law, using an optimally designed set of experiments. The iterative framework reduces the feasible design space between iterations, ultimately yielding convergence to a small design space that contains the optimum. This paper presents the derivation of an asymptotic upper bound on the difference between the corrected numerical model and true system response. Furthermore, convergence of the numerical model to the true system response and convergence of the design space are demonstrated on an airborne wind energy (AWE) application.}, booktitle={Volume 2A: 44th Design Automation Conference}, publisher={American Society of Mechanical Engineers}, author={Deodhar, Nihar and Vermillion, Christopher}, year={2018}, month={Aug} }
 @inproceedings{deese_razi_muglia_ramaprabhu_vermillion_2018, title={Fused Closed-Loop Flight Dynamics and Wake Interaction Modeling of Tethered Energy Systems}, ISBN={9780791851906}, url={http://dx.doi.org/10.1115/dscc2018-9190}, DOI={10.1115/dscc2018-9190}, abstractNote={In this paper, we present a fused flight dynamics and wake interaction modeling framework for arrays (farms) of tethered wind and marine hydrokinetic energy systems. The replacement of conventional towers with tethers necessitates a dynamic model that captures the flight characteristics of each system, whereas the arrangement of the systems in an array necessitates a wake interaction model. The integration of these components is unique to the tethered energy systems literature and is applicable to both airborne wind energy systems and tethered marine hydrokinetic energy systems. In the application case study of this paper, we focus specifically on arrays of ocean current turbines (OCTs), which are intended to operate in the deep waters of the Gulf Stream, adjacent to the eastern coast of the United States. In particular, we evaluate the dynamic performance and resulting projected energy output of an array of tethered OCTs, based on real Gulf Stream resource data from an acoustic Doppler current profiler (ADCP) located adjacent to Cape Hatteras, North Carolina.}, booktitle={Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems}, publisher={American Society of Mechanical Engineers}, author={Deese, Joe and Razi, Peyman and Muglia, Michael and Ramaprabhu, Praveen and Vermillion, Chris}, year={2018}, month={Sep} }
 @inproceedings{earnhardt_borek_groelke_geyer_vermillion_2018, title={Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck}, booktitle={Proceedings of AVEC 2018}, author={Earnhardt, Christian and Borek, John and Groelke, Ben and Geyer, Stephen and Vermillion, Chris}, year={2018} }
 @article{baheri_ramaprabhu_vermillion_2018, title={Iterative 3D layout optimization and parametric trade study for a reconfigurable ocean current turbine array using Bayesian Optimization}, volume={127}, ISSN={0960-1481}, url={http://dx.doi.org/10.1016/j.renene.2018.05.040}, DOI={10.1016/j.renene.2018.05.040}, abstractNote={In this paper, we present an online approach for optimizing the 3D layout of a reconfigurable ocean current turbine (OCT) array. Unlike towered turbines, most OCT concepts for Gulf Stream energy harvesting involve tethered systems. The replacement of towers with tethers provides the opportunity for OCTs to adjust their locations within some domain by paying out/in tether to adjust depth and manipulating control surfaces (elevators and rudders) to adjust longitudinal and lateral positions. The ability to adjust the OCT positions online provides the capacity to reconfigure the array layout in response to changing flow conditions; however, successful online array layout reconfiguration requires optimization schemes that are not only effective but also enable fast convergence to the optimal configuration. To address the above needs, we present a reconfigurable layout optimization algorithm with two novel features. First, we describe the location of each turbine through a small set of basis parameters; the number of basis parameters does not grow with increasing array size, thereby leading to an optimization that is not only computationally tractable but is also highly scalable. Secondly, we use Bayesian Optimization to optimize these basis parameters. Bayesian Optimization is a very powerful iterative optimization technique that, at every iteration, fuses a best-guess model of a complex function (array power as a function of basis parameters, in our case) with a characterization of the model uncertainty in order to determine the next evaluation point. Using a low-order analytical wake interaction model, we demonstrate the effectiveness of the proposed optimization approach for various array sizes.}, journal={Renewable Energy}, publisher={Elsevier BV}, author={Baheri, Ali and Ramaprabhu, Praveen and Vermillion, Christopher}, year={2018}, month={Nov}, pages={1052–1063} }
 @article{deese_vermillion_2018, title={Nested Plant/Controller Codesign Using G-Optimal Design and Continuous Time Adaptation Laws: Theoretical Framework and Application to an Airborne Wind Energy System}, volume={140}, ISSN={0022-0434 1528-9028}, url={http://dx.doi.org/10.1115/1.4040759}, DOI={10.1115/1.4040759}, abstractNote={This paper presents a nested codesign (combined plant and controller design) formulation that uses optimal design of experiments (DoE) techniques at the upper level to globally explore the plant design space, with continuous-time control parameter adaptation laws used at the lower level. The global design space exploration made possible through optimal DoE techniques makes the proposed methodology appealing for complex, nonconvex optimization problems for which legacy approaches are not effective. Furthermore, the use of continuous-time adaptation laws for control parameter optimization allows for the extension of the proposed optimization framework to the experimental realm, where control parameters can be optimized during experiments. At each full iteration, optimal DoE are used to generate a batch of plant designs within a prescribed design space. Each plant design is tested in either a simulation or experiment, during which an adaptation law is used for control parameter optimization. Two techniques are proposed for control parameter optimization at each iteration: extremum seeking (ES) and continuous-time DoE. The design space is reduced at the end of each full iteration, based on a response surface characterization and quality of fit estimate. The effectiveness of the approach is demonstrated for an airborne wind energy (AWE) system, where the plant parameters are the center of mass location and stabilizer area, and the control parameter is the trim pitch angle.}, number={12}, journal={Journal of Dynamic Systems, Measurement, and Control}, publisher={ASME International}, author={Deese, Joe and Vermillion, Chris}, year={2018}, month={Aug} }
 @inproceedings{bafandeh_vermillion_2018, place={New York}, title={Optimal Altitude Control of an Integrated Airborne Wind Energy System with Globalized Lyapunov-based Switched Extremum Seeking}, DOI={10.23919/ECC.2018.8550106}, abstractNote={Airborne wind energy (AWE) systems replace the tower and foundation of contemporary wind turbines with tethers and a lifting body. This enables AWE systems to adjust their operating altitudes to deliver the greatest amount of net energy possible. However, determining the optimal operating altitude requires knowledge of the wind speed vs. altitude (wind shear) profile, leading to a tradeoff between exploration and exploitation. In this work, we consider an integrated AWEbattery-generator system in which it is possible to explore the domain of admissible altitudes during periods of low load demand and exploit the best altitude at other times. Specifically, we propose and evaluate four candidate hierarchical structures, based on a globalized Lyapunov-based switched extremum seeking (G-LSES) control structure, for control of the integrated system. We present simulation-based results that are based on actual wind speed and load demand data.}, booktitle={Proceedings of the 2018 European Control Conference}, publisher={IEEE}, author={Bafandeh, Alireza and Vermillion, Chris}, year={2018} }
 @inproceedings{baheri_vermillion_2017, title={Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach}, ISBN={9781509059928}, url={http://dx.doi.org/10.23919/acc.2017.7963143}, DOI={10.23919/acc.2017.7963143}, abstractNote={This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.}, booktitle={2017 American Control Conference (ACC)}, publisher={IEEE}, author={Baheri, Ali and Vermillion, Christopher}, year={2017}, month={May} }
 @article{kalabić_vermillion_kolmanovsky_2017, title={Constraint Enforcement for a Lighter-than-Air Wind-Energy System: An Application of Reference Governors with Chance Constraints}, volume={50}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2017.08.1962}, DOI={10.1016/j.ifacol.2017.08.1962}, abstractNote={This paper considers the application of a reference governor scheme to a lighter-than-air wind-energy system subject to wind turbulence. The turbulence is treated as the output of the von Kármán model, which can be represented by a linear filter with a normally distributed disturbance input. The conventional reference governor, which enforces constraints robustly, has to be heuristically tuned in order to be applied to stochastic systems. In this work, the reference governor is generalized to the stochastic setting in order to handle stochastic disturbances. A simulation is presented, which shows satisfactory constraint enforcement with properties that are superior to the heuristically-tuned reference governor results of the authors’ previous work.}, number={1}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Kalabić, Uroš and Vermillion, Christopher and Kolmanovsky, Ilya}, year={2017}, month={Jul}, pages={13258–13263} }
 @inproceedings{baheri_vermillion_2017, place={Long Beach, CA}, title={Context-Dependent Bayesian Optimization in Real-Time Control; A Case Study in Airborne Wind Energy Systems}, booktitle={Proceedings of BayesOpt 2017}, author={Baheri, Ali and Vermillion, Chris}, year={2017} }
 @article{deodhar_deese_vermillion_2017, title={Experimentally Infused Plant and Controller Optimization Using Iterative Design of Experiments—Theoretical Framework and Airborne Wind Energy Case Study}, volume={140}, ISSN={0022-0434 1528-9028}, url={http://dx.doi.org/10.1115/1.4037014}, DOI={10.1115/1.4037014}, abstractNote={This research presents an iterative framework for optimizing the plant and controller for complex systems by fusing expensive but valuable experiments with cheap yet less accurate simulations. At each iteration, G-optimal design is used to generate experiments and simulations within a prescribed design space that is shrunken in size after each successful iteration. The shrinking of the design space is determined through statistical characterization of a response surface model, and further shrinking is achieved at successive iterations through a numerical model correction factor that is driven by the results of experiments. An initial validation of this iterative design optimization framework was performed on an airborne wind energy (AWE) system, where tethers and an aerostat are used in place of a tower to elevate the turbine to high altitudes. Using a unique lab-scale setup for the experiments, the aforementioned iterative methodology was used to optimize the center of mass location and pitch angle set point for the airborne wind energy system. The optimum configuration yielded a substantial improvement in system responses as compared to a numerically optimized configuration. The framework was recently extended to include four variables (horizontal and vertical stabilizer areas, center of mass location, and pitch angle set point).}, number={1}, journal={Journal of Dynamic Systems, Measurement, and Control}, publisher={ASME International}, author={Deodhar, Nihar and Deese, Joseph and Vermillion, Christopher}, year={2017}, month={Aug} }
 @inproceedings{bafandeh_bin-karim_vermillion_2017, title={Fused local-global control of spatiotemporally-varying systems: A case study in airborne wind energy}, ISBN={9781509021826}, url={http://dx.doi.org/10.1109/ccta.2017.8062512}, DOI={10.1109/ccta.2017.8062512}, abstractNote={This paper presents a real-time altitude optimization technique for airborne wind energy (AWE) systems that fuses a coarse, global optimization with a fine, local optimization. The ultimate goal is to maximize net energy consumption by operating at an altitude where the wind speed is closest to the turbine's rated wind speed. Without the use of auxiliary wind profiling equipment, this results in a challenging real-time optimization that must be performed over a spatiotemporally varying, partially observable environment. As a result of computational complexity, global optimization techniques must be performed over a very coarse grid. Local optimization techniques alone, on the other hand, are unlikely to yield convergence to the globally-optimal altitude trajectory. The fused control strategy proposed in this work overcomes the limitations that both strategies pose when used in isolation. Unlike traditional hierarchical control architectures, where the upper-level controller prescribes a setpoint to the lower-level, our proposed upper-level controller passes an advisory input to the lower level. This advisory input prevents the AWE system from getting stuck in non-global optima while still giving the lower level controller freedom to explore. We demonstrate the effectiveness of the proposed controller using real wind data.}, booktitle={2017 IEEE Conference on Control Technology and Applications (CCTA)}, publisher={IEEE}, author={Bafandeh, Alireza and Bin-Karim, Shamir and Vermillion, Chris}, year={2017}, month={Aug} }
 @inproceedings{kehs_cobb_fathy_vermillion_2017, title={Insights from an experimental study on the crosswind flight of a lab-scale buoyant air turbine}, ISBN={9781509059928}, url={http://dx.doi.org/10.23919/acc.2017.7963809}, DOI={10.23919/acc.2017.7963809}, abstractNote={This paper presents an experimental investigation into the crosswind motion of a lab-scale buoyant air turbine under a variety of flow conditions. A buoyant air turbine consists of a horizontal-axis turbine placed inside a helium-filled shroud and connected to the ground by tethers. Because of this setup, the system can reach higher altitudes where winds are typically stronger. Furthermore, there is an opportunity to improve power production by executing crosswind flight. This paper builds directly on (i) previous work that uses a water channel to develop a lab-scale setup that is dynamically equivalent to a full-scale system and (ii) previous work that uses this setup to execute crosswind motion induced by a square wave roll set-point trajectory. The water channel setup allows for many tests to be run at a relatively low cost. In this paper, we use the water channel to run crosswind experiments at a variety of flow speeds and with a variety of control parameters. These experimental conditions are selected using the G-optimal Design of Experiments. To the best of the authors' knowledge, this is the first time that such a variety of crosswind flight conditions have been tested on a lab-scale buoyant air turbine. Results show that (i) crosswind motion can be achieved at a variety of flow speeds, (ii) roll set-points with periods between 4 and 6 seconds are most effective out of the conditions tested, and (iii) at low flow speeds, the system can oscillate, even when unprompted by a periodic roll set-point trajectory.}, booktitle={2017 American Control Conference (ACC)}, publisher={IEEE}, author={Kehs, Michelle and Cobb, Mitchell and Fathy, Hosam K. and Vermillion, Chris}, year={2017}, month={May} }
 @inproceedings{baheri_ramaprabhu_vermillion_2017, place={New York}, title={Iterative In-Situ 3D Layout Optimization of a Reconfigurable Ocean Current Turbine Array Using Bayesian Optimization}, volume={3}, DOI={10.1115/dscc2017-5230}, abstractNote={In this paper, we present an online approach for optimizing the 3D layout of an ocean current turbine (OCT) array. Unlike towered turbines, most OCT concepts for Gulf Stream energy harvesting involve tethered systems. The replacement of towers with tethers provides the opportunity for OCTs to adjust their locations within some domain by paying out/in tether to adjust depth and manipulating control surfaces (elevators and rudders) to adjust longitudinal and lateral positions. The ability to adjust the OCT positions online provides the capacity to reconfigure the array layout in response to changing flow conditions; however, successful online array layout reconfiguration requires optimization schemes that are not only effective but also enable fast convergence to the optimal configuration. To address the above needs, we present a reconfigurable layout optimization algorithm with two novel features. First, we describe the location of each turbine through a small set of basis parameters; the number of basis parameters does not grow with increasing array size, thereby leading to an optimization that is not only computationally tractable but is also highly scalable. Secondly, we use Bayesian Optimization to optimize these basis parameters. Bayesian Optimization is a very powerful iterative optimization technique that, at every iteration, fuses a best-guess model of a complex function (array power as a function of basis parameters, in our case) with a characterization of the model uncertainty in order to determine the next evaluation point. Using a low-order analytical wake interaction model, we demonstrate the effectiveness of the proposed optimization approach for various array sizes.}, booktitle={ASME 2017 Dynamic Systems and Control Conference : October 11-13, 2017, Tysons, Virginia, USA.}, publisher={American Society of Mechanical Engineers}, author={Baheri, Ali and Ramaprabhu, Praveen and Vermillion, Chris}, year={2017} }
 @inproceedings{cobb_barton_fathy_vermillion_2017, title={Iterative learning-based waypoint optimization for repetitive path planning, with application to airborne wind energy systems}, ISBN={9781509028733}, url={http://dx.doi.org/10.1109/cdc.2017.8264051}, DOI={10.1109/cdc.2017.8264051}, abstractNote={This paper presents an iterative learning approach for optimizing waypoints in repetitive path following applications. Our proposed algorithm consists of two key features: First, a recursive least squares fit is used to construct an estimate of the behavior of the performance index. Secondly, an iteration-to-iteration waypoint adaptation law is used to update waypoints in the direction of optimal performance. This waypoint update law parallels the mathematical structure of a traditional iterative learning control (ILC) update but replaces the tracking error term with an error between the present and estimated optimal waypoint sequences. The proposed methodology is applied to the crosswind path optimization of an airborne wind energy (AWE) system, where the goal is to maximize the average power output over a figure-8 path. In validating the tools from this work, we introduce a simplified 2-dimensional analog to the more complex 3-dimensional AWE system, which distills the problem to its core elements. Using this model, we demonstrate that the proposed waypoint adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions.}, booktitle={2017 IEEE 56th Annual Conference on Decision and Control (CDC)}, publisher={IEEE}, author={Cobb, Mitchell and Barton, Kira and Fathy, Hosam and Vermillion, Chris}, year={2017}, month={Dec} }
 @article{cobb_deodhar_vermillion_2018, title={Lab-Scale Experimental Characterization and Dynamic Scaling Assessment for Closed-Loop Crosswind Flight of Airborne Wind Energy Systems}, volume={140}, ISSN={0022-0434 1528-9028}, url={http://dx.doi.org/10.1115/1.4038650}, DOI={10.1115/1.4038650}, abstractNote={This paper presents the experimental validation and dynamic similarity analysis for a lab-scale version of an airborne wind energy (AWE) system executing closed-loop motion control. Execution of crosswind flight patterns, achieved in this work through the asymmetric motion of three tethers, enables dramatic increases in energy generation compared with stationary operation. Achievement of crosswind flight in the lab-scale experimental framework described herein allows for rapid, inexpensive, and dynamically scalable characterization of new control algorithms without recourse to expensive full-scale prototyping. We first present the experimental setup, then derive dynamic scaling relationships necessary for the lab-scale behavior to match the full-scale behavior. We then validate dynamic equivalence of crosswind flight over a range of different scale models of the Altaeros Buoyant airborne turbine (BAT). This work is the first example of successful lab-scale control and measurement of crosswind motion for an AWE system across a range of flow speeds and system scales. The results demonstrate that crosswind flight can achieve significantly more power production than stationary operation, while also validating dynamic scaling laws under closed-loop control.}, number={7}, journal={Journal of Dynamic Systems, Measurement, and Control}, publisher={ASME International}, author={Cobb, Mitchell and Deodhar, Nihar and Vermillion, Christopher}, year={2018}, month={Jan} }
 @article{deodhar_bafandeh_deese_smith_muyimbwa_vermillion_tkacik_2017, title={Laboratory-Scale Flight Characterization of a Multitethered Aerostat for Wind Energy Generation}, volume={55}, ISSN={0001-1452 1533-385X}, url={http://dx.doi.org/10.2514/1.j054407}, DOI={10.2514/1.j054407}, abstractNote={Tethered lifting bodies have attracted significant attention from surveillance, communications, and (most recently) wind energy domains. As with many aerospace systems, the costs of full-scale test...}, number={6}, journal={AIAA Journal}, publisher={American Institute of Aeronautics and Astronautics (AIAA)}, author={Deodhar, Nihar and Bafandeh, Alireza and Deese, Joe and Smith, Brian and Muyimbwa, Tim and Vermillion, Christopher and Tkacik, Peter}, year={2017}, month={Jun}, pages={1823–1832} }
 @article{nikpoorparizi_deodhar_vermillion_2018, title={Modeling, Control Design, and Combined Plant/Controller Optimization for an Energy-Harvesting Tethered Wing}, volume={26}, DOI={10.1109/TCST.2017.2721361}, abstractNote={This paper presents a combined plant and controller performance analysis and optimization for a tethered rigid wing with on-board rotors, flying in crosswind patterns. Specifically, we use a 3-D model of the tethered wing to assess the influence of critical design parameters on both quality of flight and energy-generation performance, as quantified by the “Loyd Factor,” which compares energy-generation performance to a theoretical upper bound. Recognizing that the optimal performance occurs when the system is on the verge of closed-loop instability, we demonstrate how a combined optimization of the plant and controller can aid in further pushing the boundaries of the system. The results of this combined optimization show a critical tradeoff between robustness and energy-generation performance. We demonstrate that attaining maximum energy-generation performance requires operating on the verge of closed-loop instability and also results in a reduced set of parameters for which the system is stable.}, number={4}, journal={IEEE Transactions on Control Systems Technology}, author={Nikpoorparizi, Parvin and Deodhar, Nihar and Vermillion, Chris}, year={2018}, month={Jul}, pages={1157–1169} }
 @article{deese_deodhar_vermillion_2017, title={Nested Plant/Controller Co-Design Using G-Optimal Design and Extremum Seeking: Theoretical Framework and Application to an Airborne Wind Energy System * *This work was supported by NSF grant number 1453912, entitled CAREER: Efficient Experimental Optimization for High Performance Airborne Wind Energy Systems.}, volume={50}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2017.08.1182}, DOI={10.1016/j.ifacol.2017.08.1182}, abstractNote={This paper presents a unique nested optimization framework for the co-design of a physical system (plant) and controller, which leverages optimal Design of Experiments (DoE) techniques for the plant optimization and extremum seeking for the control system optimization. At each iteration of the optimization, candidate plant parameters are generated by using G-optimal DoE. Unlike gradient-based approaches, the use of optimal DoE enables efficient global exploration of a plant design space that can contain multiple local optima. For each candidate plant design, the corresponding controller optimization is performed in real time, using extremum seeking. This enables the real-time adjustment of controller parameters during the course of simulations or experiments, thereby expediting the overall optimization process. The co-design process is carried out iteratively, where sub-optimal plant designs are rejected based on a response surface characterization and hypothesis testing. The co-design framework was validated in simulation for a Buoyant Airborne Turbine (BAT). Here, the optimized plant parameters were a reference area scale factor (scales the horizontal and vertical stabilizer areas uniformly) and center of mass location, whereas the optimized control parameter was the pitch angle setpoint. After four complete iterations, the flight performance index improved and the feasible plant design space (i.e., the locus of plant design parameters that could possibly be optimal, based on hypothesis testing) shrunk by 99%.}, number={1}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Deese, Joe and Deodhar, Nihar and Vermillion, Chris}, year={2017}, month={Jul}, pages={11965–11971} }
 @article{kehs_vermillion_fathy_2018, title={Online Energy Maximization of an Airborne Wind Energy Turbine in Simulated Periodic Flight}, volume={26}, ISSN={1063-6536 1558-0865}, url={http://dx.doi.org/10.1109/tcst.2017.2665553}, DOI={10.1109/tcst.2017.2665553}, abstractNote={This paper presents a controller for optimizing the crosswind figure-8 motion of a buoyant airborne wind energy turbine. Crosswind figure-8 motion has the potential to increase average power generation compared with stationary flight. To achieve crosswind motion, we use a hierarchical control scheme, where a high-level controller adjusts the roll set-point trajectory and a lower-level motor controller tracks it. The optimal crosswind trajectory changes with both wind speed and the plant’s aerodynamic parameters. This creates a need for an optimal controller that adjusts the roll set-point trajectory both in response to wind speed variations and plant uncertainties. Adaptation is complicated by the facts that: 1) wind speed is difficult to measure accurately at high altitudes and 2) the use of an optimal roll set-point trajectory can induce instability if actual wind conditions are different from anticipated conditions. Building on these observations and the existing literature, this paper presents a controller that adapts the figure-8 trajectory in changing and uncertain wind conditions by fusing direct anemometry-based wind speed estimation with extremum seeking (ES). The fast anemometry-based estimation allows for quick set-point adjustments. The slow-converging ES adds a correction factor that can be used to account for uncertainties such as estimator bias or plant parameter uncertainties. In one simulation with real wind data, the proposed approach improves energy generation by 92% over a stationary controller and 40% over a similar controller based on anemometry-based speed estimation alone.}, number={2}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Kehs, Michelle and Vermillion, Chris and Fathy, Hosam}, year={2018}, month={Mar}, pages={393–403} }
 @article{baheri_bin-karim_bafandeh_vermillion_2017, title={Real-time control using Bayesian optimization: A case study in airborne wind energy systems}, volume={69}, ISSN={0967-0661}, url={http://dx.doi.org/10.1016/j.conengprac.2017.09.007}, DOI={10.1016/j.conengprac.2017.09.007}, abstractNote={This paper presents a framework by which a data-driven optimization technique known as Bayesian Optimization can be used for real-time optimal control. In particular, Bayesian Optimization is applied to the real-time altitude optimization of an Airborne Wind Energy (AWE) system, for the purpose of maximizing net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system’s operating altitude. In this work, Gaussian Process modeling and Bayesian Optimization are used in real-time to optimize the AWE system’s operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, the underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent operating altitude. In the AWE application, context-dependent Bayesian Optimization is used to handle the time-varying nature of the wind shear profile (wind speed vs. altitude). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in significantly increasing power production over these baselines.}, journal={Control Engineering Practice}, publisher={Elsevier BV}, author={Baheri, Ali and Bin-Karim, Shamir and Bafandeh, Alireza and Vermillion, Christopher}, year={2017}, month={Dec}, pages={131–140} }
 @article{bin-karim_bafandeh_baheri_vermillion_2019, title={Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy}, volume={27}, ISSN={1063-6536 1558-0865 2374-0159}, url={http://dx.doi.org/10.1109/tcst.2017.2779428}, DOI={10.1109/tcst.2017.2779428}, abstractNote={This brief presents a model predictive control (MPC)-based spatiotemporal optimization strategy that is applied to the problem of optimizing the altitude of a type of airborne wind energy (AWE) system, specifically a buoyant airborne turbine. Altitude optimization for AWE systems represents a challenging problem under which the wind speed varies with both time and altitude, is only instantaneously observable at the altitude where the AWE system is operating, and dictates the net power produced by the system. The proposed MPC strategy avoids the need for a computationally expensive Markov process model for characterizing the wind speed and is structured in a way that the need for instantaneous power maximization (termed exploitation) is balanced with the need to maintain an accurate map of wind speed versus altitude (termed exploration). The MPC strategy is calibrated through a Gaussian process regression framework. Real wind speed versus altitude data have been used to validate the strategy.}, number={2}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Bin-Karim, Shamir and Bafandeh, Alireza and Baheri, Ali and Vermillion, Christopher}, year={2019}, month={Mar}, pages={798–805} }
 @inproceedings{deodhar_vermillion_2016, title={A Framework for Fused Experimental/Numerical Plant and Control System Optimization Using Iterative G-Optimal Design of Experiments}, ISBN={9780791850107}, url={http://dx.doi.org/10.1115/detc2016-60488}, DOI={10.1115/detc2016-60488}, abstractNote={This paper presents a methodology for optimally fusing experiments and numerical simulations in the design of a combined plant and control system. The proposed methodology uses G-optimal Design of Experiments to balance the need for experimental data with the expense of collecting a multitude of experimental results. Specifically, G-optimal design is used to first select a batch of candidate experimental configurations, then determine which of those points to test experimentally and which to numerically simulate. The optimization process is carried out iteratively, where the set of candidate design configurations is shrunken at each iteration using a Z-test, and the numerical model is corrected according to the most recent experimental results. The methodology is presented on a model of an airborne wind energy system, wherein both the center of mass location (plant parameter) and trim pitch angle (controller parameter) are critical to system performance.}, booktitle={Volume 2A: 42nd Design Automation Conference}, publisher={American Society of Mechanical Engineers}, author={Deodhar, Nihar and Vermillion, Christopher}, year={2016}, month={Aug} }
 @article{bafandeh_vermillion_2017, title={Altitude Optimization of Airborne Wind Energy Systems via Switched Extremum Seeking—Design, Analysis, and Economic Assessment}, volume={25}, ISSN={1063-6536 1558-0865}, url={http://dx.doi.org/10.1109/tcst.2016.2632534}, DOI={10.1109/tcst.2016.2632534}, abstractNote={This paper applies a Lyapunov-based switched extremum seeking (LSES) control algorithm to the application of altitude optimization of airborne wind energy systems. We perform an economic analysis to evaluate the effectiveness of the control scheme. Finding the altitude with the highest energy yield requires energy to be consumed in the search for this optimal altitude. The simultaneous desires to optimize altitude and minimize control energy consumption are balanced in this paper through a variant of ES control, where the periodic perturbation signal is reduced when convergence upon an optimal altitude is detected. The signal is reinstated when the wind speed begins to deviate from its instantaneous optimal value. Because the wind shear profile (wind speed versus altitude) is subject to continual variations, this application represents a challenging case study in LSES control. Using real wind shear data acquired over a 25-day period, the results presented in this paper show that the LSES controller is successful in significantly increasing the net energy production over fixed-altitude and standard ES strategies. The economic advantage of the approach is illustrated through a comparison of achievable wind energy penetration with and without LSES-based altitude optimization in place, using real load demand data.}, number={6}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Bafandeh, Alireza and Vermillion, Chris}, year={2017}, month={Nov}, pages={2022–2033} }
 @inproceedings{nikpoorparizi_deodhar_vermillion_2016, place={Piscataway, NJ}, title={Combined plant and controller performance analysis and optimization for an energy-harvesting tethered wing}, ISBN={9781467386821}, url={http://dx.doi.org/10.1109/acc.2016.7525564}, DOI={10.1109/acc.2016.7525564}, abstractNote={This paper presents a combined plant and controller analysis and optimization for a tethered rigid wing with on-board rotors, flying in crosswind patterns. Specifically, we use a 3-D model of the tethered wing to assess the influence of critical design parameters on both quality of flight and energy generation performance, as quantified by the “Loyd Factor” [1], which compares energy generation performance to a theoretical upper bound established by Miles Loyd [2]. Recognizing that the optimal performance occurs when the system is on the verge of instability, we demonstrate how a combined optimization of the plant and controller can aid in further pushing the boundaries of the system. The results of this combined optimization show a critical trade-off between robustness and energy generation performance, wherein the attainment of peak performance not only requires operation on the verge of instability but also results in the shrinking of the set of parameters for which the system is stable.}, booktitle={2016 American Control Conference (ACC)}, publisher={IEEE}, author={NikpoorParizi, Parvin and Deodhar, Nihar and Vermillion, Christopher}, year={2016}, month={Jul} }
 @inproceedings{dimarco_vermillion_ziegert_2016, place={New York}, title={Disturbance and Performance-Weighted Iterative Learning Control With Application to Modulated Tool Path-Based Manufacturing}, volume={2}, ISBN={9780791850701}, url={http://dx.doi.org/10.1115/dscc2016-9898}, DOI={10.1115/dscc2016-9898}, abstractNote={Single-point metal turning processes can create chip nests that are hazards to both parts and machine tools. This is mitigated by a process called Modulated Tool Path (MTP) machining, which superimposes an oscillation in the tool tip feed direction in order to break these chips and provide an adequate surface finish. MTP machining is highly sensitive to the amplitude and frequency of this oscillation, both of which can often be diminished by standard machine tool controllers. These controllers are also unresponsive to iteration-varying disturbances such as temperature fluctuations, which can cause positional and velocity-related inaccuracies. This paper presents a library-based variant of Iterative Learning Control (ILC) called Disturbance and Performance-Weighted ILC (DPW-ILC), which is designed to improve the accuracy of machine tool trajectories that are highly oscillatory in nature, as well as provide robustness to varying, but measurable disturbances. DPW-ILC has been shown in simulation to provide a tremendous accuracy benefit over standard ILC techniques, specifically in the presence of two separate types of temperature-based disturbances.}, booktitle={ASME 2016 Dynamic Systems and Control Conference : October 12-14, 2016, Minneapolis, Minnesota, USA}, publisher={American Society of Mechanical Engineers}, author={DiMarco, Christopher and Vermillion, Christopher and Ziegert, John C.}, year={2016}, month={Oct} }
 @inproceedings{cobb_vermillion_fathy_2016, title={Lab-Scale Experimental Crosswind Flight Control System Prototyping for an Airborne Wind Energy System}, ISBN={9780791850695}, url={http://dx.doi.org/10.1115/dscc2016-9737}, DOI={10.1115/dscc2016-9737}, abstractNote={This paper presents an original experimental setup for controlling and measuring the crosswind flight of airborne wind energy systems in a laboratory environment. Execution of cross-wind flight patterns, which is achieved in this work through the asymmetric motion of three tethers, enables dramatic increases in energy generation compared with stationary operation. Achievement of crosswind flight in the 1:100-scale experimental framework described herein allows for rapid, inexpensive, and dynamically scalable characterization of new control algorithms without recourse to expensive full-scale prototyping. This work is the first example of successful lab-scale control and measurement of crosswind motion for an airborne wind energy system. Specifically, this paper presents the experimental setup, crosswind flight control strategy, and experimental results for a model of the Altaeros Buoyant Airborne Turbine (BAT). The results demonstrate that crosswind flight control can achieve nearly 50 percent more power production then stationary operation, while also demonstrating the potential of the experimental framework for further algorithm development.}, booktitle={Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation}, publisher={American Society of Mechanical Engineers}, author={Cobb, Mitchell and Vermillion, Christopher and Fathy, Hosam}, year={2016}, month={Oct} }
 @inproceedings{bafandeh_vermillion_2016, title={Real-time altitude optimization of airborne wind energy systems using Lyapunov-based switched extremum seeking control}, ISBN={9781467386821}, url={http://dx.doi.org/10.1109/acc.2016.7526144}, DOI={10.1109/acc.2016.7526144}, abstractNote={This paper applies a Lyapunov-based switched extremum seeking (LSES) control algorithm to the application of altitude optimization of airborne wind energy systems. We evaluate the stability and convergence of this control algorithm for the application at hand. Achievement of the optimal altitude to maximize energy production requires use of energy in order to search for the optimal altitude, which is continually and randomly varying. The simultaneous desires to optimize altitude and minimize control energy consumption are balanced in this paper through a variant of extremum seeking control, where the periodic perturbation signal is reduced when convergence upon an optimal altitude is detected. The signal is reinstated when the wind speed begins to deviate from its instantaneous optimal value. Because the wind shear profile (wind speed vs. altitude) is subject to continual variations, this application represents a challenging case study in LSES control. Using real wind shear data, the results presented in this paper show that the LSES controller is successful in significantly increasing the net energy production over fixed-altitude and standard extremum seeking strategies.}, booktitle={2016 American Control Conference (ACC)}, publisher={IEEE}, author={Bafandeh, Alireza and Vermillion, Chris}, year={2016}, month={Jul} }
 @inproceedings{bin-karim_bafandeh_vermillion_2016, title={Spatio-temporal optimization through model predictive control: A case study in airborne wind energy}, ISBN={9781509018376}, url={http://dx.doi.org/10.1109/cdc.2016.7798913}, DOI={10.1109/cdc.2016.7798913}, abstractNote={This paper presents a model predictive control (MPC)-based spatio-temporal optimization strategy that is applied to the problem of optimizing the altitude of an airborne wind energy (AWE) system. Altitude optimization for AWE systems represents a challenging problem under which the wind speed at the operating altitude dictates the net power produced by the system. The wind speed varies with both time and altitude and is typically only instantaneously observable at the operating altitude of the AWE system. The MPC strategy proposed in this work avoids the need for a computationally expensive Markov process model for characterizing the wind speed and is structured in a way that the need for instantaneous power maximization (termed exploitation) is balanced with the need to maintain an accurate map of wind speed vs. altitude (termed exploration). The MPC strategy is calibrated through data-driven statistical characterizations of the wind profile and is validated through real wind speed vs. altitude data.}, booktitle={2016 IEEE 55th Conference on Decision and Control (CDC)}, publisher={IEEE}, author={Bin-Karim, Shamir and Bafandeh, Alireza and Vermillion, Christopher}, year={2016}, month={Dec} }
 @inproceedings{deodhar_vermillion_tkacik_2015, title={A case study in experimentally-infused plant and controller optimization for airborne wind energy systems}, ISBN={9781479986842}, url={http://dx.doi.org/10.1109/acc.2015.7171087}, DOI={10.1109/acc.2015.7171087}, abstractNote={This paper presents a combined plant and controller optimization process for airborne wind energy systems (AWEs) that fuses numerical optimization with lab-scale experimental results. The methodology introduced in this paper, referred to as experimentally-infused optimization, addresses several challenges faced by AWE system designers, including a strong coupling between the controller and plant design, significant modeling uncertainties (which require the use of experiments), and high costs associated with full-scale experimental prototypes. This paper presents an initial case study of the proposed experimentally-infused optimization, where experiments were conducted on a 1/100th-scale model of Altaeros Buoyant Air Turbine (BAT), which was tethered and flown in the University of North Carolina at Charlotte 1m × 1m water channel. The lab-scale experimental platform reduced the cost of evaluating flight dynamics and control by more than two orders of magnitude, while resulting in substantially improved flight performance, quantified by a 15.2 percent improvement in an objective function value, as compared to a purely numerical optimization.}, booktitle={2015 American Control Conference (ACC)}, publisher={IEEE}, author={Deodhar, Nihar and Vermillion, Chris and Tkacik, Peter}, year={2015}, month={Jul} }
 @inproceedings{dimarco_ziegert_vermillion_2015, title={Exponential and Sigmoid-Interpolated Machining Strategies}, volume={37}, ISSN={0278-6125}, url={http://dx.doi.org/10.1016/j.jmsy.2015.04.007}, DOI={10.1016/j.jmsy.2015.04.007}, abstractNote={In single-point metal turning and boring processes, a chip nest can often be created that is a hazard to part and operators alike. In order to mitigate this, a process called modulated tool path (MTP) machining was developed that superimposes a sinusoidal motion tangent to the feed direction onto the tool feed path to break chips. The sinusoidal motions are created under CNC control in the part program. In the current implementation, the sinusoidal motion is approximated as a series of short linear moves. Linear interpolation is currently used to create position and velocity commands to the axis servomotors at each control loop closure. Linear interpolation is a computationally heavy and dated method that is not well tailored to a sinusoidal trajectory. In this paper a new method called the sigmoidal interpolator is introduced that honors all physical constraints of a machining system while offering better tracking performance and lower accelerations than the linear interpolator, all while reducing the number of possible state transitions of the implemented software from approximately 17 to 4.}, number={Part 2}, booktitle={Proceedings of the 2015 SME North American Manufacturing Research Conference}, publisher={Elsevier BV}, author={DiMarco, Christopher and Ziegert, John C. and Vermillion, Christopher}, year={2015}, month={Oct}, pages={535–541} }
 @inproceedings{deese_muyimbwa_deodhar_vermillion_tkacik_2015, title={Lab-Scale Characterization of a Lighter-Than-Air Wind Energy System - Closing the Loop}, ISBN={9781624103650}, url={http://dx.doi.org/10.2514/6.2015-3350}, DOI={10.2514/6.2015-3350}, abstractNote={Airborne Wind Energy systems (AWEs), which replace conventional systems’ towers with tethers and a lifting body, can provide inexpensive and clean energy to remote locations that have traditionally relied on expensive diesel fuel as their principal fuel source. However, many AWEs have not been implemented because of the lack of flight dynamic characterization that has resulted from the high costs of full-scale models. This paper presents recent developments in a lab-scale, water channel-based test platform for the characterization of AWEs, focusing on the flight dynamics of the Buoyant Airborne Turbine (BAT) of Altaeros Energies, which uses a lighter-than-air shell to elevate a horizontal-axis turbine to altitudes as high as 600 m. Specifically, the paper describes the lab-scale testing framework implemented in the UNC-Charlotte water channel, which includes real-time motion capture, closed-loop control of tethers, and rapid variability over a variety of model parameters (including tether attachment location, center of mass location, and fin geometry), which represent significant advances with respect to the authors’ previous work. The research specifically focused on the impact of center of mass location, trim pitch angle, and horizontal stabilizer pitch angle on flight dynamics, demonstrating the sensitivity of flight dynamic performance on these parameters both in the openand closed-loop setting.}, booktitle={22nd AIAA Lighter-Than-Air Systems Technology Conference}, publisher={American Institute of Aeronautics and Astronautics}, author={Deese, Joseph T. and Muyimbwa, Timothy and Deodhar, Nihar A. and Vermillion, Christopher R. and Tkacik, Peter}, year={2015}, month={Jun} }
 @inproceedings{kehs_vermillion_fathy_2015, title={Maximizing Average Power Output of an Airborne Wind Energy Generator Under Parametric Uncertainties}, ISBN={9780791857250}, url={http://dx.doi.org/10.1115/dscc2015-9764}, DOI={10.1115/dscc2015-9764}, abstractNote={This paper presents a controller for maximizing the time-averaged power output from an airborne wind energy generator in uncertain wind conditions. This system’s optimal energy output often involves flying in periodic figure-8 trajectories, but the precise optimal figure-8 shape is sensitive to environmental conditions, including wind speed. The literature presents controllers that are able to adapt to uncertainties, and this work expands on the current literature by using an extremum seeking based method. Extremum seeking is particularly well-suited for this application because of its well understood stability properties. In this work, extremum seeking is used to search through a family of optimal trajectories (computed offline) that correspond to discrete wind speeds. The controller is efficient in that it only searches for the optimum trajectory over the uncertain parameter (in this paper, wind speed). Results show that the controller converges to the optimal trajectory, provided it is initialized to a stable figure-8. The speed of convergence is dependent on the difference between the initial average power output and the optimal average power output.}, booktitle={Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications}, publisher={American Society of Mechanical Engineers}, author={Kehs, Michelle A. and Vermillion, Chris and Fathy, Hosam K.}, year={2015}, month={Oct} }
 @inproceedings{vermillion_glass_szalai_2014, title={Development and Full-Scale Experimental Validation of a Rapid Prototyping Environment for Plant and Control Design of Airborne Wind Energy Systems}, ISBN={9780791846193}, url={http://dx.doi.org/10.1115/dscc2014-5907}, DOI={10.1115/dscc2014-5907}, abstractNote={Airborne wind energy systems present great promise for inexpensive, clean energy at remote locations, but have only been demonstrated through short-duration flights in very limited wind conditions. Because of the time and money that is required to implement full-scale airborne wind energy prototypes, convergence toward designs that achieve longer-duration flight in adverse weather has been slow. This paper presents an inexpensive rapid prototyping approach for improving the flight dynamics and control of airborne wind energy systems, which has been implemented and validated on Altaeros Energies most recent full-scale flight prototype. The approach involves the 3d printing of lab-scale water channel models of airborne wind energy lifting bodies, which enable prediction of dynamic flight characteristics, rapid iteration between the designs, identification of unknown or poorly known parameters, and improved control design. By applying this approach to its last prototype design cycle, Altaeros demonstrated robust operation in double the wind speeds sustained by its previous prototype (reaching a maximum of 21.2 m/s, with sustained 10–15 m/s winds), with demonstrably improved flight characteristics.}, booktitle={Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing}, publisher={American Society of Mechanical Engineers}, author={Vermillion, Chris and Glass, Ben and Szalai, Balazs}, year={2014}, month={Oct} }
 @inproceedings{vermillion_glass_greenwood_2014, title={Evaluation of a Water Channel-Based Platform for Characterizing Aerostat Flight Dynamics: A Case Study on a Lighter-Than-Air Wind Energy System}, ISBN={9781624102868}, url={http://dx.doi.org/10.2514/6.2014-2711}, DOI={10.2514/6.2014-2711}, abstractNote={Aerostat development and testing costs often suffer from a lack of scalability. In particular, it very difficult to fabricate an inexpensive lighter-than-air system that can be evaluated in a lab environment, since the maximum allowable mass of the aerostat becomes prohibitively low for small length scales. This paper presents an evaluation of a novel water channel-based platform for assessing the flight dynamics of aerostats at a very small scale, in a lab environment, for a very low cost. Altaeros Energies’ buoyant airborne turbine (BAT) is used as a case study to demonstrate the effectiveness of the proposed approach. Specifically, we identify important dynamic scaling properties and show how the water channel experiments are run to match these properties closely in the water channel vs. full-scale settings. We then show how the water channel results can be used in concert with a simulation model to predict the performance of the full-scale system. The ultimate result is a design which, after an inexpensive evaluation process, can proceed to a larger-scale prototype stage with a high degree of confidence in its success.}, booktitle={21st AIAA Lighter-Than-Air Systems Technology Conference}, publisher={American Institute of Aeronautics and Astronautics}, author={Vermillion, Chris and Glass, Ben and Greenwood, Sam}, year={2014}, month={Jun} }
 @inproceedings{samson_katebi_vermillion_2013, title={A Critical Assessment of Airborne Wind Energy Systems}, ISBN={9781849197588}, url={http://dx.doi.org/10.1049/cp.2013.1852}, DOI={10.1049/cp.2013.1852}, abstractNote={This paper focuses on the different types design of Airborne Wind Energy Systems (AWES) and their control architecture. The main focus of this paper will be on a novel lighter than air system developed by Altaeros Energies. AWES combines cutting edge innovation with practical engineering design to produce a system capable of rivalling conventional wind energy generation. Closed-form control strategies provide greater transparency and make tuning and debugging in real-time much easier. Control architecture of this type will be presented for the Altaeros system. This paper contributes to existing literature by focusing on the Altaeros design, discussing the relative advantages and disadvantages that this system holds over other airborne systems and conventional wind generation.}, booktitle={2nd IET Renewable Power Generation Conference (RPG 2013)}, publisher={Institution of Engineering and Technology}, author={Samson, J. and Katebi, R. and Vermillion, C.}, year={2013} }
 @inproceedings{vermillion_2013, title={Altitude and Crosswind Motion Control for Optimal Power-Point Tracking in Tethered Wind Energy Systems With Airborne Power Generation}, ISBN={9780791856147}, url={http://dx.doi.org/10.1115/dscc2013-3796}, DOI={10.1115/dscc2013-3796}, abstractNote={This paper presents a control strategy that combines altitude and crosswind motion control for tethered wind energy systems with airborne turbines and generators. The proposed algorithm adjusts altitude and induces an appropriate level of crosswind motion to present the system with an apparent wind speed that most closely meets, but does not exceed, the rated wind speed of the on-board turbine(s), thereby tracking the turbine’s optimal power point. The adjustment of both altitude and motion control, along with the reduction in crosswind motion and altitude when the rated wind speed is exceeded, differentiates the proposed control architecture from other strategies proposed in the literature. Initial control laws and simulation results are presented for the Altaeros lighter-than-air wind energy system.}, booktitle={Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control}, publisher={American Society of Mechanical Engineers}, author={Vermillion, Chris}, year={2013}, month={Oct} }
 @article{vermillion_fagiano_2013, title={Electricity in the Air: Tethered Wind Energy Systems}, volume={135}, ISSN={0025-6501 1943-5649}, url={http://dx.doi.org/10.1115/1.2013-sep-5}, DOI={10.1115/1.2013-sep-5}, abstractNote={This article summarizes the fundamental dynamics and control attributes and challenges faced by stationary and crosswind airborne wind energy (AWE) systems. AWE systems have undergone rapid and steady technological development over the past decade, with several organizations demonstrating basic economic and technical viability of their concepts. The theoretical and numerical analyses performed so far indicate that crosswind systems have the potential to achieve a power curve similar in shape to that of current commercial wind turbines, with rated power of 2–5 MW. The ongoing development activities are increasing the viability of the concept; yet, several technical issues remain and need to be addressed, to definitively show that this technology can be scaled up to industrial size. The expert analysis suggests that AWE technologies are at the dawn of their development, and there is significant untapped potential for the use of innovative solutions in multiple fields such as materials, power electronics, and aerodynamics, to tackle problems. These challenges present a wealth of opportunities for future, multidisciplinary research and development activities.}, number={09}, journal={Mechanical Engineering}, publisher={ASME International}, author={Vermillion, Chris and Fagiano, Lorenzo}, year={2013}, month={Sep}, pages={S13–S21} }
 @inbook{vermillion_glass_rein_2013, title={Lighter-Than-Air Wind Energy Systems}, ISBN={9783642399640 9783642399657}, ISSN={1865-3529 1865-3537}, url={http://dx.doi.org/10.1007/978-3-642-39965-7_30}, DOI={10.1007/978-3-642-39965-7_30}, abstractNote={Several wind energy concepts utilize airborne systems that contain lighterthan-air gas, which supplements aerodynamic lift and expands these systems’ available operating regimes. While lighter-than-air systems can incorporate the traction and crosswind flight motions of their heavier-than-air counterparts, several lighterthan-air concepts have also been designed to deliver large amounts of power under completely stationary operation and remain aloft during periods of intermittent wind. This chapter provides an overview of the history of LTA airborne wind energy concepts, including the design drivers and principal design constraints. The focus then turns to the structural and aerodynamic design principles behind lighterthan air systems, along with fundamental flight dynamic principles that must be addressed. A prototype design developed by Altaeros Energies is examined as an example of the application of these principles. The chapter closes with suggestions for future research to enable commercially-viable LTA systems.}, booktitle={Airborne Wind Energy}, publisher={Springer Berlin Heidelberg}, author={Vermillion, Chris and Glass, Ben and Rein, Adam}, year={2013}, pages={501–514} }
 @article{vermillion_grunnagle_lim_kolmanovsky_2014, title={Model-Based Plant Design and Hierarchical Control of a Prototype Lighter-Than-Air Wind Energy System, With Experimental Flight Test Results}, volume={22}, ISSN={1063-6536 1558-0865}, url={http://dx.doi.org/10.1109/tcst.2013.2263505}, DOI={10.1109/tcst.2013.2263505}, abstractNote={This paper presents the modeling, control system design, and experimental results for a prototype lighter-than-air wind energy system being pioneered by Altaeros Energies. This unique design features a horizontal-axis turbine that is elevated to high altitudes through a buoyant shroud, which is tethered to a ground-based platform. The system's altitude can be adjusted to maximize power production, and because the system is both functional and economical in a stationary position, it circumvents many of the controls challenges faced by kite-based wind energy systems. However, the need for generation of energy introduces pointing, efficiency, and autonomy requirements, which are not faced by conventional aerostats, thereby requiring a careful model-based control design. In this paper, we provide a dynamic model of the Altaeros system, then show how this model is leveraged in the plant design and in the design of the control system, which provides full autonomy, from takeoff, through power production, to autonomous landing. We provide simulation and experimental results that demonstrate the performance of the prototype and point to important areas where Altaeros will focus its efforts moving forward.}, number={2}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Vermillion, Chris and Grunnagle, Trey and Lim, Ronny and Kolmanovsky, Ilya}, year={2014}, month={Mar}, pages={531–542} }
 @inproceedings{kalabic_vermillion_kolmanovsky_2013, title={Reference governor design for computationally efficient attitude and tether tension constraint enforcement on a lighter-than-air wind energy system}, ISBN={9783033039629}, url={http://dx.doi.org/10.23919/ecc.2013.6669490}, DOI={10.23919/ecc.2013.6669490}, abstractNote={In this paper, we propose a reference governor-based approach to guarantee enforcement of critical flight constraints on the Altaeros tethered, lighter-than-air wind energy system. While the high-altitude flight made available by the tethered system leads to significant increases in power production over traditional, tower-mounted systems, the freedom of motion resulting from the tethers and aerodynamic shell introduces critical attitude and tether tension constraints. To date, methods considered for enforcing these constraints have relied upon heuristic static maps or model predictive control (MPC). The former cannot guarantee transient constraint satisfaction, whereas the latter is computationally burdensome given Altaeros's current microcontroller capabilities. The approach pursued in this paper uses a reference governor, which is a computationally simple add-on to the existing controller that enforces transient and steady-state constraints. The methodology proposed in this paper is demonstrated through simulations on linear and nonlinear models of the longitudinal dynamics of the Altaeros system with wind gust disturbances.}, booktitle={2013 European Control Conference (ECC)}, publisher={IEEE}, author={Kalabic, Uros and Vermillion, Chris and Kolmanovsky, Ilya}, year={2013}, month={Jul} }
 @article{vermillion_menezes_kolmanovsky_2014, title={Stable hierarchical model predictive control using an inner loop reference model and <mml:math altimg="si32.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd" xmlns:sa="http://www.elsevier.com/xml/common/struct-aff/dtd"><mml:mi>λ</mml:mi></mml:math>-contractive terminal constraint sets}, volume={50}, ISSN={0005-1098}, url={http://dx.doi.org/10.1016/j.automatica.2013.10.009}, DOI={10.1016/j.automatica.2013.10.009}, abstractNote={This paper proposes a novel hierarchical model predictive control (MPC) strategy that guarantees overall system stability. This method differs significantly from previous approaches to guaranteeing overall stability, which have relied upon a multi-rate framework where the inner loop (low level) is updated at a faster rate than the outer loop (high level), and the inner loop must reach a steady state within each outer loop time step. In contrast, the method proposed in this paper is aimed at stabilizing the origin of an error system characterized by the difference between the inner loop state and the state specified by a full-order reference model. This makes the method applicable to systems with reduced levels of time scale separation. This paper proposes a framework for guaranteeing stability that leverages the use of the reference model, in conjunction with λ-contractive constraint sets for both the inner and outer loops. The effectiveness of the proposed reference model-based strategy is shown through simulation on an existing stirred-tank reactor problem, where we demonstrate that the MPC optimization problem remains feasible and that the system remains stable and continues to perform well when time scale separation between the inner and outer loops is reduced.}, number={1}, journal={Automatica}, publisher={Elsevier BV}, author={Vermillion, Chris and Menezes, Amor and Kolmanovsky, Ilya}, year={2014}, month={Jan}, pages={92–99} }
 @inproceedings{weng_balasubramanian_vermillion_kolmanovsky_2012, place={Fort Lauderdale, FL}, title={Model Predictive Longitudinal Control of a Lighter-Than-Air Wind Energy System}, ISBN={9780791845301}, url={http://dx.doi.org/10.1115/dscc2012-movic2012-8613}, DOI={10.1115/dscc2012-movic2012-8613}, abstractNote={This paper presents the design and simulation results of a model predictive controller (MPC) applied to the longitudinal dynamics of a lighter-than-air wind energy system being pioneered by Altaeros Energies. The unique Altaeros design features a traditional horizontal axis wind turbine that is held aloft by a buoyant shroud, which is tethered to a ground based platform. This structure provides access to strong, high-altitude winds, requires minimal setup, and builds upon proven aerostat components, making the system an attractive component in expanding wind energy throughout the world. However, because the system replaces a conventional tower with tethers, its dynamics are highly susceptible to variations in the wind. In particular, the control system must keep the shroud pitch angle and tether tensions within acceptable bounds in order to maintain stable operation and remain within structural limitations of the system. In this paper, we apply MPC to achieve desirable longitudinal system performance while simultaneously enforcing the constraints. We describe the longitudinal dynamic model of the system, detail the linear MPC design, and provide simulation results on both the linearized and nonlinear system for a variety of real-world wind conditions, including a Dryden turbulence model and data acquired from the Altaeros functional prototype test site at Loring Air Force Base in Limestone, Maine.Copyright © 2012 by ASME}, booktitle={Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines;}, publisher={ASME}, author={Weng, Richard and Balasubramanian, Kiran and Vermillion, Chris and Kolmanovsky, Ilya}, year={2012}, month={Oct} }
 @inproceedings{vermillion_grunnagle_kolmanovsky_2012, title={Modeling and control design for a prototype lighter-than-air wind energy system}, ISBN={9781457710964 9781457710957 9781457710940 9781467321020}, url={http://dx.doi.org/10.1109/acc.2012.6315434}, DOI={10.1109/acc.2012.6315434}, abstractNote={This paper presents the hardware configuration, modeling, and control design for a lighter-than-air wind power system being pioneered by Altaeros Energies. This unique design features a horizontal-axis turbine that is elevated to high altitudes via a buoyant shroud, which is tethered to a ground-based platform. Because the system is based on proven aerostat technology and is designed to remain substantially stationary, it circumvents many of the controls challenges faced by so-called aerodynamic (e.g. kite-based) wind energy systems. However, the need to generate energy introduces pointing, efficiency, and autonomy requirements that are not faced by conventional aerostats, thereby requiring a careful model-based control design. In this paper, we first provide a detailed description of the system and controls hardware for the Altaeros 2.4 kW proof-of-concept prototype. We provide a detailed 3-dimensional dynamic model for the Altaeros system, which is used in the design of a fully autonomous control system. This paper details the control system design and shows simulation results that substantiate the system's performance.}, booktitle={2012 American Control Conference (ACC)}, publisher={IEEE}, author={Vermillion, C. and Grunnagle, T. and Kolmanovsky, I.}, year={2012}, month={Jun} }
 @article{vermillion_sun_butts_2012, title={Robust modular control system design using an inner-loop reference model and μ synthesis techniques}, volume={23}, ISSN={1049-8923}, url={http://dx.doi.org/10.1002/rnc.2822}, DOI={10.1002/rnc.2822}, abstractNote={SUMMARYFor complex dynamic systems, a modular control design process is often employed, wherein the overall design is partitioned into smaller modules. This paper considers a particular inner‐loop/outer‐loop modular control strategy in which the designer of the outer‐loop module does not know the specifics of the inner loop but instead possesses a reference model that captures the ideal inner‐loop input–output behavior. In the first part of this paper, we establish analytical properties of the modular reference‐model‐based design. In the second part, we introduce a novel mechanism, referred to as the modular control error compensation, which mitigates the performance loss that arises when the inner‐loop reference model is not matched. We propose an iterative algorithm, using μ synthesis, to design this compensator to reduce performance loss on the basis of two concrete worst‐case performance metrics. The effectiveness of the modular control strategy with the modular control error compensation is demonstrated through experimental results on an automotive system. Copyright © 2012 John Wiley & Sons, Ltd.}, number={12}, journal={International Journal of Robust and Nonlinear Control}, publisher={Wiley}, author={Vermillion, Chris and Sun, Jing and Butts, Ken}, year={2012}, month={May}, pages={1338–1359} }
 @article{vermillion_menezes_kolmanovsky_2011, title={Stable Hierarchical Model Predictive Control Using an Inner Loop Reference Model*}, volume={44}, ISSN={1474-6670}, url={http://dx.doi.org/10.3182/20110828-6-it-1002.02733}, DOI={10.3182/20110828-6-it-1002.02733}, abstractNote={This paper proposes a novel hierarchical model predictive control (MPC) strategy that guarantees overall system stability. Our method differs significantly from previous approaches to guaranteeing overall stability, which have relied upon a multi-rate framework where the inner loop (low level) is updated at a faster rate than the outer loop (high level), and the inner loop must reach a steady-state within each outer loop time step. In contrast, our approach is aimed at stabilizing the origin of an error system characterized by the difference between the inner loop state and the state specified by a full-order reference model. This makes our method applicable to systems that do not possess the level of time scale separation that is required to apply the multi-rate framework successfully. Stability constraints for the proposed algorithm are derived, and the effectiveness of the proposed reference model-based strategy is shown through simulation on a stirred tank reactor problem, where we demonstrate that the MPC optimization problem remains feasible and that the system remains stable and continues to perform well when time scale separation between the inner and outer loops is reduced.}, number={1}, journal={IFAC Proceedings Volumes}, publisher={Elsevier BV}, author={Vermillion, Chris and Menezes, Amor and Kolmanovsky, Ilya}, year={2011}, month={Jan}, pages={9278–9283} }
 @inproceedings{boris_vermillion_butts_2010, title={A comparative analysis of electronic pedal algorithms using a driver-in-the-loop simulator and system identification of driver behavior}, ISBN={9781424474271 9781424474264 9781424474257}, url={http://dx.doi.org/10.1109/acc.2010.5531117}, DOI={10.1109/acc.2010.5531117}, abstractNote={In modern automobiles, the driver's accelerator pedal position is fed to an electronic control unit, which has traditionally interpreted this pedal input as desired throttle angle but can interpret the pedal position in other ways as well. In this paper, we consider three interpretations of pedal position, namely desired throttle angle, desired net engine torque, and desired wheel torque. We design separate controllers for each pedal interpretation. For each controller, we evaluate drivers' abilities to simultaneously track a speed setpoint and keep high frequency vehicle acceleration to a minimum, relying on classical control theory to come up with preliminary hypotheses and a driver-in-the-loop simulator for determining which hypotheses hold. We also perform parametric system identification for each of the subjects used in this study, for each of the controllers, to assess any differences in driver behavior across the different controllers. We have concluded, for the vehicle platform studied here, the engine torque control provides comparable performance to direct throttle control, with improved drivability, whereas both throttle and engine torque control yield performance that is far superior to wheel torque control.}, booktitle={Proceedings of the 2010 American Control Conference}, publisher={IEEE}, author={Boris, Ryan and Vermillion, Chris and Butts, Ken}, year={2010}, month={Jun} }
 @inproceedings{vermillion_2010, place={Linz, Austria}, title={Automated Sensitivity-Based Optimization for Control and Identification}, booktitle={2010 Workshop on Identification for Automotive Systems}, author={Vermillion, Chris}, year={2010} }
 @inproceedings{vermillion_butts_reidy_2010, title={Model predictive engine torque control with real-time driver-in-the-loop simulation results}, ISBN={9781424474271 9781424474264 9781424474257}, url={http://dx.doi.org/10.1109/acc.2010.5531241}, DOI={10.1109/acc.2010.5531241}, abstractNote={This paper presents the design and simulation results for a novel off-idle engine torque control strategy that uses online model predictive control (MPC) to simultaneously manage drivability, emissions, and fuel economy, while delivering desired engine torque. In order to achieve a tractable optimization, a modular control approach is used, wherein MPC is used to manipulate desired air/fuel ratio, engine air charge, spark advance, and variable valve timing, whereas lower level controllers are designed using conventional design techniques to deliver these desired values. The performance of the proposed control strategy is exhibited through simulation results on two test cases, including a driver-in-the-loop simulator. Results show that the model predictive torque control strategy yields a significant overall improvement in terms of a combined drivability, emissions, and fuel economy metric.}, booktitle={Proceedings of the 2010 American Control Conference}, publisher={IEEE}, author={Vermillion, Chris and Butts, K and Reidy, Kevin}, year={2010}, month={Jun} }
 @article{vermillion_sun_butts_2011, title={Predictive Control Allocation for a Thermal Management System Based on an Inner Loop Reference Model—Design, Analysis, and Experimental Results}, volume={19}, ISSN={1063-6536 1558-0865}, url={http://dx.doi.org/10.1109/tcst.2010.2053370}, DOI={10.1109/tcst.2010.2053370}, abstractNote={This paper addresses the challenge of controlling an overactuated engine thermal management system where two actuators, with different dynamic authorities and saturation limits, are used to obtain tight temperature regulation. A modular control strategy is proposed that combines model predictive control allocation (MPCA) with the use of an inner loop reference model. This results in an inner loop controller that closely matches a dynamic specification for input-output performance while addressing actuator dynamics and saturation constraints. This paper presents the design and implementation strategy and illustrates the effectiveness of the proposed solution through real-time simulation and experimental results.}, number={4}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Vermillion, Chris and Sun, Jing and Butts, Ken}, year={2011}, month={Jul}, pages={772–781} }
 @inproceedings{vermillion_sun_butts_2009, title={Model predictive control allocation - Design and experimental results on a thermal management system}, ISBN={9781424445233}, url={http://dx.doi.org/10.1109/acc.2009.5160203}, DOI={10.1109/acc.2009.5160203}, abstractNote={In this paper, we consider the challenge of controlling an overactuated engine thermal management system where two actuators, with different dynamic authorities and saturation limits, are used to obtain tight temperature regulation. We propose a modular control strategy that combines model predictive control allocation (MPCA) with the concepts of model reference control to design an inner loop controller that closely matches a dynamic specification for the inner loop input-output performance while addressing actuator dynamics and saturation constraints. We present the design and implementation strategy and illustrate the effectiveness of the proposed solution through real-time simulation and experimental results.}, booktitle={2009 American Control Conference}, publisher={IEEE}, author={Vermillion, Chris and Sun, Jing and Butts, Ken}, year={2009} }
 @article{vermillion_sun_butts_2009, title={Modeling, Control Design, and Experimental Validation of an Overactuated Thermal Management System for Engine Dynamometer Applications}, volume={17}, ISSN={1063-6536 1558-0865}, url={http://dx.doi.org/10.1109/tcst.2008.2001267}, DOI={10.1109/tcst.2008.2001267}, abstractNote={Effective engine mapping and calibration are contingent upon tight control of the environment in which the mapping and calibration are performed. Among the most important variables to be controlled are the temperatures of coolant and oil that circulate through the engine block. Because of the large time constants associated with thermodynamic systems, controlling these variables often represents a bottleneck in the engine mapping and calibration processes. In this paper, we examine a particular layout for a thermal management unit, which is currently being used in practice. By developing and analyzing a thermodynamic model of the system, we are able to gain insight into the system dynamics and explore special features to optimize the temperature response. In particular, we will show how the overactuation in the system may be leveraged in the presence of hard saturation constraints and different dynamic actuator authorities. We present design and validation results (both simulation and experimental) for the proposed controller, and compare the performance to the baseline controller in order to quantify improvements.}, number={3}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Vermillion, C. and Sun, Jing and Butts, K.}, year={2009}, month={May}, pages={540–551} }
 @inproceedings{vermillion_sun_butts_2008, title={Performance enhancement of modular control systems using µ synthesis}, ISBN={9781424431236}, url={http://dx.doi.org/10.1109/cdc.2008.4738997}, DOI={10.1109/cdc.2008.4738997}, abstractNote={For complex dynamic systems, a modular control design process is often employed, wherein the overall design is partitioned into smaller modules. The designers of each module only possess a model for a particular subset of the entire plant as well as closed loop performance specifications for the other module(s). In this paper, we will examine a common modular control strategy in which an outer loop controller computes a desired virtual control input and the inner loop computes real control inputs in order to achieve this desired virtual control input as closely as possible. The outer loop design is based on a specification for the inner loop, which may not always be achieved. We propose a modular control error compensator that is aimed at mitigating the performance degradation caused when the inner loop specifications are not achieved. We show that this compensator can be designed using μ synthesis and propose an iterative procedure to optimize performance based on two concrete worst-case metrics. The effectiveness of the proposed compensator is shown through an automotive example.}, booktitle={2008 47th IEEE Conference on Decision and Control}, publisher={IEEE}, author={Vermillion, Chris and Sun, Jing and Butts, Ken}, year={2008} }
 @inproceedings{vermillion_sun_butts_2007, title={Model predictive control allocation for overactuated systems - stability and performance}, ISBN={9781424414970}, url={http://dx.doi.org/10.1109/cdc.2007.4434722}, DOI={10.1109/cdc.2007.4434722}, abstractNote={Overactuated systems often arise in automotive, aerospace, and robotics applications, where for reasons of redundancy or performance constraints, it is beneficial to equip a system with more control inputs than outputs. This necessitates control allocation methods that distribute control effort amongst many actuators to achieve a desired effect. Until recently, most methods have treated the control allocation as static in the sense that different dynamic authorities of the actuators were not taken into account. Recent advances have used model predictive control allocation (MPCA) to consider the dynamic authorities of the actuators over a receding horizon. In this paper, we consider the dynamic control allocation problem for overactuated systems where each actuator has different dynamic control authority and hard saturation limits. A modular control design approach is proposed, where the controller consists of an outer loop controller that synthesizes a desired virtual control input signal and an inner loop controller that uses MPCA to achieve the desired virtual control signal. We derive sufficient stability conditions for the composite feedback system and show how these conditions may be realized by imposing an additional constraint on the MPCA design. An automotive example is provided to illustrate the effectiveness of the proposed algorithm.}, booktitle={2007 46th IEEE Conference on Decision and Control}, publisher={IEEE}, author={Vermillion, Chris and Sun, Jing and Butts, Ken}, year={2007} }
 @inproceedings{vermillion_sun_butts_hall_2006, title={Modeling and Analysis of a Thermal Management System for Engine Calibration}, ISBN={0780397967 0780397959}, url={http://dx.doi.org/10.1109/cca.2006.286181}, DOI={10.1109/cca.2006.286181}, booktitle={2006 IEEE International Conference on Control Applications}, publisher={IEEE}, author={Vermillion, Chris and Sun, Jing and Butts, Ken and Hall, Andy}, year={2006}, month={Oct} }
 @inproceedings{vermillion_sun_butts_biens, place={Berlin, Germany}, title={Control Strategies and Experimental Results of an Engine Thermal Management System for Fast and Accurate Dynamometer Engine Mapping}, booktitle={Proceedings of the 2007 IAV Conference on Design of Experiments}, author={Vermillion, Chris and Sun, Jing and Butts, Ken and Biens, Frank} }