Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 2; pp. 1559 - 1571
• Main Authors: Zhang, Haoyan, Zhao, Xudong, Wang, Huanqing, Zong, Guangdeng, Xu, Ning
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actor–critic (AC) neural networks (NNs) architecture; Adaptive control; adaptive optimal control; Adaptive systems; Artificial neural networks; Closed loops; Control systems; Cost function; hierarchical sliding-mode surface (HSMS); Machine learning; Neural networks; Nonlinear control; Nonlinear systems; Optimal control; Perturbation; Perturbation methods; Sliding mode control; Standardization; switched nonlinear systems; Switches; Uncertainty; unknown perturbation
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3183991

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.