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Jiles-Atherton Based Hysteresis Identification of Shape Memory Alloy-Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm

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Listed:
  • Le Chen
  • Ying Feng
  • Rui Li
  • Xinkai Chen
  • Hui Jiang

Abstract

Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.

Suggested Citation

  • Le Chen & Ying Feng & Rui Li & Xinkai Chen & Hui Jiang, 2019. "Jiles-Atherton Based Hysteresis Identification of Shape Memory Alloy-Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm," Complexity, Hindawi, vol. 2019, pages 1-11, February.
    • Handle: RePEc:hin:complx:7465461
    DOI: 10.1155/2019/7465461
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    References listed on IDEAS

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    1. Xuehui Gao & Ruiguo Liu, 2018. "Multiscale Chebyshev Neural Network Identification and Adaptive Control for Backlash-Like Hysteresis System," Complexity, Hindawi, vol. 2018, pages 1-9, October.
    2. Jun Zhong & Xu Zhou & Minzhou Luo, 2018. "A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator-Driven Setup Using Back Propagation Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-9, October.
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