@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{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{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} }
 @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{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{mishra_srinivasan_2021, title={Parametric Iterative Learning Control: A Method for Adaptation of Feedforward Control, and Application to Gearshift Control}, ISSN={["2378-5861"]}, DOI={10.23919/ACC50511.2021.9483394}, abstractNote={A framework for parametric iterative learning is introduced in this study, the underlying philosophy of which entails parameterization of feedforward control inputs in the form of look-up tables as functions of the trial-varying aspects of repetitive system operation. The current study focuses on tracking of a trial-varying reference trajectory in the presence of trial-varying external disturbances. Theoretical results for convergence are presented, and the proposed method of parametric learning is validated numerically for the application of adaptive control of gearshift controllers.}, journal={2021 AMERICAN CONTROL CONFERENCE (ACC)}, author={Mishra, Kirti D. and Srinivasan, K.}, year={2021}, pages={218–223} }