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Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment

Author

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  • Zhao, Lv
  • Jin, Jie
  • Gong, Jianqiang

Abstract

Based on a new robust zeroing neural network (RZNN) model, the trajectory tracking control of a wheeled mobile robot (WMR) within fixed-time in noise-polluted environment is presented in this paper. Unlike most of the previous reported works, the RZNN model approach for trajectory tracking control of the WMR reaches fixed-time convergence and noise suppression simultaneously. Besides, detailed theoretical analysis of its convergence and robustness are provided. Numerical simulation verification is also provided to demonstrate the superior robustness and accurateness of the RZNN model approach for trajectory tracking control of the WMR in noise-polluted environment. Both of the theoretical analysis and numerical simulation results verify the effectiveness and robustness of the RZNN model approach.

Suggested Citation

  • Zhao, Lv & Jin, Jie & Gong, Jianqiang, 2021. "Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 289-307.
    • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:289-307
    DOI: 10.1016/j.matcom.2020.12.030
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    Cited by:

    1. Zhu, Jingcan & Jin, Jie & Chen, Weijie & Gong, Jianqiang, 2022. "A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 291-307.
    2. Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.

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