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Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

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  • Bin Xu
  • Pengchao Zhang

Abstract

This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Suggested Citation

  • Bin Xu & Pengchao Zhang, 2017. "Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope," Complexity, Hindawi, vol. 2017, pages 1-8, July.
    • Handle: RePEc:hin:complx:6019175
    DOI: 10.1155/2017/6019175
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    Cited by:

    1. Zhao Guo & Yongping Pan & Tairen Sun & Yubing Zhang & Xiaohui Xiao, 2017. "Adaptive Neural Network Control of Serial Variable Stiffness Actuators," Complexity, Hindawi, vol. 2017, pages 1-9, November.
    2. Zhijia Zhao & Yonghao Ma & Guiyun Liu & Dachang Zhu & Guilin Wen, 2019. "Vibration Control of an Axially Moving System with Restricted Input," Complexity, Hindawi, vol. 2019, pages 1-10, January.
    3. Bin Xu & Pengchao Zhang, 2017. "Composite Learning Sliding Mode Control of Flexible-Link Manipulator," Complexity, Hindawi, vol. 2017, pages 1-6, August.
    4. Yue, Xiaohui & Shao, Xingling & Li, Jie, 2021. "Prescribed chattering reduction control for quadrotors using aperiodic signal updating," Applied Mathematics and Computation, Elsevier, vol. 405(C).

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