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Multiscale Chebyshev Neural Network Identification and Adaptive Control for Backlash-Like Hysteresis System

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  • Xuehui Gao
  • Ruiguo Liu

Abstract

An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is proposed for the backlash-like hysteresis nonlinearity system in this paper. Firstly, a MSCNN is introduced to approximate the backlash-like nonlinearity of the system, and then, the Lyapunov theorem assures the identification approach is effective. Afterward, to simplify the control design, tracking error is transformed into a scalar error with Laplace transformation. Therefore, an adaptive control strategy based on the transformed scalar error is proposed, and all the signals of the closed-loop system are uniformly ultimately bounded (UUB). Finally, simulation results have demonstrated the performance of the proposed control scheme.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:1872493
    DOI: 10.1155/2018/1872493
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    Cited by:

    1. 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.
    2. Ruiguo Liu & Xuehui Gao, 2019. "Neural Network Identification and Sliding Mode Control for Hysteresis Nonlinear System with Backlash-Like Model," Complexity, Hindawi, vol. 2019, pages 1-10, July.

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