Original scientific paper

https://doi.org/10.1080/00051144.2023.2170058

Online adaptive optimal tracking control for model-free nonlinear systems via a dynamic neural network

Yuming Yin ; College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of China
Zhiiun Fu ; College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, People’s Republic of China
Yan Lu ; College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, People’s Republic of China

Abstract

This paper presents an online adaptive approximate solution for the optimal tracking control problem of model-free nonlinear systems. Firstly, a dynamic neural network identifier with properly designed weights updating laws is developed to identify the unknown dynamics. Then an adaptive optimal tracking control policy consisting of two terms is proposed, i.e. a steady-state control term is established to ensure the desired tracking performance at the steady state, and an optimal control term is proposed to ensure the optimal tracking error dynamics optimally. The composite Lyapunov method is used to analyse the stability of the closed-loop system. Two simulation examples are presented to demonstrate the effectiveness of the proposed method.

Keywords

Dynamic neural network; nonlinear systems; nonlinear identifier; adaptive control; optimal contro

Hrčak ID:

315761

URI

https://hrcak.srce.hr/315761

Publication date:

13.2.2023.

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