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.
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ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
September 19–21, 2012
Stone Mountain, Georgia, USA
Conference Sponsors:
- Aerospace Division
ISBN:
978-0-7918-4509-7
PROCEEDINGS PAPER
Development of Robust Control Algorithms for Shape Memory Alloy Bending Actuators
John H. Crews,
John H. Crews
North Carolina State University, Raleigh, NC
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Ralph C. Smith,
Ralph C. Smith
North Carolina State University, Raleigh, NC
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Jennifer C. Hannen
Jennifer C. Hannen
North Carolina State University, Raleigh, NC
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John H. Crews
North Carolina State University, Raleigh, NC
Ralph C. Smith
North Carolina State University, Raleigh, NC
Jennifer C. Hannen
North Carolina State University, Raleigh, NC
Paper No:
SMASIS2012-7989, pp. 391-400; 10 pages
Published Online:
July 24, 2013
Citation
Crews, JH, Smith, RC, & Hannen, JC. "Development of Robust Control Algorithms for Shape Memory Alloy Bending Actuators." Proceedings of the ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Structural Health Monitoring. Stone Mountain, Georgia, USA. September 19–21, 2012. pp. 391-400. ASME. https://doi.org/10.1115/SMASIS2012-7989
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