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Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network

Author

Listed:
  • Jiashun Huang

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China)

  • Dengguo Xu

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
    School of Physics and Electrical Engineering, Liupanshui Normal University, Liupanshui 553000, China)

  • Yahui Li

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China)

  • Yan Ma

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China)

Abstract

This paper proposes an optimal tracking control scheme through adaptive dynamic programming (ADP) for a class of partially unknown discrete-time (DT) nonlinear systems based on a radial basis function neural network (RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs; the first one is used to construct the identifier, and the other one is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. The optimal feedback control and the cost function are derived via feedforward neural network approximation, and a means of regulating the tracking error is proposed. The critic network and the actor network were trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation within the ADP framework. Simulations were carried out to verify the effectiveness of the optimal tracking control technique using the neural networks.

Suggested Citation

  • Jiashun Huang & Dengguo Xu & Yahui Li & Yan Ma, 2024. "Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network," Mathematics, MDPI, vol. 12(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1146-:d:1373507
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