Modeling and Bayesian parameter estimation for shape memory alloy bending actuators

John H. Crews; Ralph C. Smith

doi:10.1117/12.914792

28 March 2012 Modeling and Bayesian parameter estimation for shape memory alloy bending actuators

John H. Crews, Ralph C. Smith

Proceedings Volume 8342, Behavior and Mechanics of Multifunctional Materials and Composites 2012; 83421N (2012) https://doi.org/10.1117/12.914792
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2012, San Diego, California, United States

Abstract

In this paper, we employ a homogenized energy model (HEM) for shape memory alloy (SMA) bending actuators. Additionally, we utilize a Bayesian method for quantifying parameter uncertainty. The system consists of a SMA wire attached to a flexible beam. As the actuator is heated, the beam bends, providing endoscopic motion. The model parameters are fit to experimental data using an ordinary least-squares approach. The uncertainty in the fit model parameters is then quantified using Markov Chain Monte Carlo (MCMC) methods. The MCMC algorithm provides bounds on the parameters, which will ultimately be used in robust control algorithms. One purpose of the paper is to test the feasibility of the Random Walk Metropolis algorithm, the MCMC method used here.

Citation Download Citation

John H. Crews and Ralph C. Smith "Modeling and Bayesian parameter estimation for shape memory alloy bending actuators", Proc. SPIE 8342, Behavior and Mechanics of Multifunctional Materials and Composites 2012, 83421N (28 March 2012); https://doi.org/10.1117/12.914792

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