Modeling and Bayesian Parameter Estimation for Shape Memory Alloy Bending Actuators

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.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2012
Accession Number
ADA556967

Entities

People

  • John H. Crews
  • Ralph C. Smith

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Actuators
  • Algorithms
  • Alloys
  • Computational Science
  • Control Systems
  • Differential Equations
  • Equations
  • Experimental Data
  • Heat Transfer
  • Markov Chains
  • Mathematics
  • Modulus Of Elasticity
  • Monte Carlo Method
  • Probability
  • Random Walk
  • Shape Memory Alloys
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference