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Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

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  • Xuehui Gao

Abstract

An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error into a scalar error . Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.

Suggested Citation

  • Xuehui Gao, 2018. "Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model," Complexity, Hindawi, vol. 2018, pages 1-9, July.
    • Handle: RePEc:hin:complx:9765861
    DOI: 10.1155/2018/9765861
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    Cited by:

    1. 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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