@article{wiest_buckner_2015, title={Path optimization and control of a shape memory alloy actuated catheter for endocardial radiofrequency ablation}, volume={65}, ISSN={["1872-793X"]}, DOI={10.1016/j.robot.2014.10.019}, abstractNote={This paper introduces a real-time path optimization and control strategy for shape memory alloy (SMA) actuated cardiac ablation catheters, potentially enabling the creation of more precise lesions with reduced procedure times and improved patient outcomes. Catheter tip locations and orientations are optimized using parallel genetic algorithms to produce continuous ablation paths with near normal tissue contact through physician-specified points. A nonlinear multivariable control strategy is presented to compensate for SMA hysteresis, bandwidth limitations, and coupling between system inputs. Simulated and experimental results demonstrate efficient generation of ablation paths and optimal reference trajectories. Closed-loop control of the SMA-actuated catheter along optimized ablation paths is validated experimentally.}, journal={ROBOTICS AND AUTONOMOUS SYSTEMS}, author={Wiest, Jennifer H. and Buckner, Gregory D.}, year={2015}, month={Mar}, pages={88–97} }
 @inproceedings{crews_smith_hannen_2013, title={Development of robust control algorithms for shape memory alloy bending actuators}, DOI={10.1115/smasis2012-7989}, abstractNote={In this paper, we present a systematic approach to developing robust control algorithms for a single-tendon shape memory alloy (SMA) bending actuator. Parameter estimation and uncertainty quantification are accomplished using Bayesian techniques. Specifically, we utilize Markov Chain Monte Carlo (MCMC) methods to estimate parameter uncertainty. The Bayesian parameter estimation results are used to construct a sliding mode control (SMC) algorithm where the bounds on uncertainty are used to guarantee controller robustness. The sliding mode controller utilizes the homogenized energy model (HEM) for SMA. The inverse HEM compensates for hysteresis and converts a reference bending angle to a reference temperature. Temperature in the SMA actuator is estimated using an observer, and the sliding mode controller ensures that the observer temperature tracks the reference temperature. The SMC is augmented with proportional-integral (PI) control on the bending angle error.}, booktitle={Proceedings of the ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, vol 1}, author={Crews, J. H. and Smith, Ralph and Hannen, J. C.}, year={2013}, pages={391–400} }
 @inproceedings{hannen_buckner_2013, title={Indirect intelligent sliding mode control using hysteretic recurrent neural networks with application to a shape memory alloy actuated beam}, DOI={10.1115/smasis2012-7930}, abstractNote={This paper presents the development of an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators. The controller manipulates applied voltage, enabling temperature control in one or more SMA tendons, which are offset to produce bending in a flexible beam tip. Hysteresis compensation is achieved using a hysteretic recurrent neural network (HRNN), which maps the nonlinear, hysteretic relationships between SMA temperatures and bending angle. Incorporating this HRNN into a variable structure control architecture provides robustness to model uncertainties and parameter variations. Single input, single output and multivariable implementations of this control strategy are presented. Controller performance is evaluated using a flexible beam deflected by single and antagonistic SMA tendons. Experimental results demonstrate precise tracking of a variety of reference trajectories for both configurations, with superior performance compared to an optimized PI controller for each system. Additionally, the IISMC demonstrates robustness to parameter variations and disturbances.}, booktitle={Proceedings of the ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, vol 1}, author={Hannen, J. C. and Buckner, G. D.}, year={2013}, pages={295–303} }
 @article{crews_mcmahan_smith_hannen_2013, title={Quantification of parameter uncertainty for robust control of shape memory alloy bending actuators}, volume={22}, ISSN={["1361-665X"]}, DOI={10.1088/0964-1726/22/11/115021}, abstractNote={In this paper, we employ Bayesian parameter estimation techniques to derive gains for robust control of smart materials. Specifically, we demonstrate the feasibility of utilizing parameter uncertainty estimation provided by Markov chain Monte Carlo (MCMC) methods to determine controller gains for a shape memory alloy bending actuator. We treat the parameters in the equations governing the actuator’s temperature dynamics as uncertain and use the MCMC method to construct the probability densities for these parameters. The densities are then used to derive parameter bounds for robust control algorithms. For illustrative purposes, we construct a sliding mode controller based on the homogenized energy model and experimentally compare its performance to a proportional-integral controller. While sliding mode control is used here, the techniques described in this paper provide a useful starting point for many robust control algorithms.}, number={11}, journal={SMART MATERIALS AND STRUCTURES}, author={Crews, John H. and McMahan, Jerry A. and Smith, Ralph C. and Hannen, Jennifer C.}, year={2013}, month={Nov} }
 @article{hannen_crews_buckner_2012, title={Indirect intelligent sliding mode control of a shape memory alloy actuated flexible beam using hysteretic recurrent neural networks}, volume={21}, ISSN={["1361-665X"]}, DOI={10.1088/0964-1726/21/8/085015}, abstractNote={This paper introduces an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators, specifically a flexible beam deflected by a single offset SMA tendon. The controller manipulates applied voltage, which alters SMA tendon temperature to track reference bending angles. A hysteretic recurrent neural network (HRNN) captures the nonlinear, hysteretic relationship between SMA temperature and bending angle. The variable structure control strategy provides robustness to model uncertainties and parameter variations, while effectively compensating for system nonlinearities, achieving superior tracking compared to an optimized PI controller.}, number={8}, journal={SMART MATERIALS AND STRUCTURES}, author={Hannen, Jennifer C. and Crews, John H. and Buckner, Gregory D.}, year={2012}, month={Aug} }