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Control of Strain Characteristics of SMA Wires Using Seebeck Voltage

[+] Author Affiliations
V. V. N. Sriram Malladi, Pablo A. Tarazaga

Virginia Polytechnic Institute and State University, Blacksburg, VA

Paper No. SMASIS2013-3099, pp. V001T03A020; 8 pages
  • ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation
  • Snowbird, Utah, USA, September 16–18, 2013
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5603-1
  • Copyright © 2013 by ASME


Shape Memory Alloys (SMAs), as a branch of smart material actuators, are widely researched in the areas of control applications. These actuators exhibit considerable hysteresis between the supply voltage (conventionally used in resistive heating) and position characteristics of the SMA. Unless a model matches the actuator’s nonlinearities, the control of an SMA would result in an error between the desired and actual strain. An Adaptive Neuro-Fuzzy Inference System (or ANFIS) model is proposed to model the hysteresis of the system. The hysteresis of an SMA is path dependent, thus controlling the SMA in real-time requires a time series forecasting a nonlinear model. The input parameters for such an ANFIS model would be a physical variable at time t and at a time t-n, where n is a time delay. The present work studies the effect of time delay on the actuator nonlinearities for two ANFIS models. One of the models studies the relationship between the desired displacement of an SMA and the supply voltage across the SMA, while the other model predicts the actual displacement of an SMA from the feedback temperature.

Copyright © 2013 by ASME



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