Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

Journal: Computational intelligence and neuroscience
Published Date: Jan 6, 2022

Abstract

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Authors

  • Zhijun Fu
    Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
  • Yan Lu
    National Institute of Standards and Technology.
  • Fang Zhou
    Center of Robot Minimally Invasive Surgery, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan 61000, China.
  • Yaohua Guo
    Research Center of Yutong Bus Co., Ltd., No. 66, Yuxing Road, Zhengzhou 450061, China.
  • Pengyan Guo
    Department of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36, Beihuan Road, Zhengzhou 450045, China.
  • Heyang Feng
    Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

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