Reinforcement learning‐based adaptive predefined‐time optimal tracking control for strict‐feedback nonlinear systems

Author:

Chen Yilin1,Pan Yingnan1ORCID,Lu Qing2

Affiliation:

1. College of Control Science and Engineering Bohai University Jinzhou Liaoning China

2. College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing Jiangsu China

Abstract

SummaryIn this paper, an adaptive predefined‐time optimal tracking control method is presented for a class of strict‐feedback nonlinear systems. Different from the existing optimal control results, these reinforcement learning methods only ensure the system is semiglobally uniformly ultimately bounded or finite‐time stable, which cannot achieve the precise control of the convergence time and accuracy. The designed predefined‐time optimal control strategy can guarantee that the convergence time and error accuracy can be predefined by users, while optimizing the performance index function by constructing the identifier‐actor‐critic neural networks. Meanwhile, an improved piecewise continuous function is devised to solve the problem of containing the sign function in the controller under the predefined‐time stability framework, which can both decrease the chattering phenomenon and avoid the possible singularity problem. Moreover, it can be demonstrated that all signals within the closed‐loop systems are predefined‐time stable. Finally, some simulation results confirm the effectiveness of the proposed control strategy.

Funder

National Natural Science Foundation of China

Science and Technology Project of Nantong City

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

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