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

Jiashun Huang, Dengguo Xu (), Yahui Li and Yan Ma
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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
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

Mathematics, 2024, vol. 12, issue 8, 1-18

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.

Keywords: adaptive dynamic programming (ADP); optimal tracking control; RBF neural network (RBF-NN); nonlinear systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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