Adaptive fixed-time neural networks control for pure-feedback non-affine nonlinear systems with state constraints

Li, Yang; Zhu, Quanmin; Zhang, Jianhua; Deng, Zhaopeng

Abstract

A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine nonlinear systems with state constraints according to the feedback signal of the error system. Based on the adaptive backstepping technology, the Lyapunov function is designed for each subsystem. The neural network is used to identify the unknown parameters of the system in a fixed-time, and the designed control strategy makes the output signal of the system track the expected signal in a fixed-time. Through the stability analysis, it is proved that the tracking error converges in a fixed-time, and the design of the upper bound of the setting time of the error system only needs to modify the parameters and adaptive law of the controlled system controller, which does not depend on the initial conditions.

• Journal Article Type: Article
• Acceptance Date: May 20, 2022
• Online Publication Date: May 22, 2022
• Publication Date: May 22, 2022
• Deposit Date: Aug 2, 2022
• Publicly Available Date: Aug 2, 2022
• Journal: Entropy
• Electronic ISSN: 1099-4300
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 5
• Pages: 737
• DOI: https://doi.org/10.3390/e24050737
• Keywords: adaptive control, pure feedback, nonlinear constraint systems, neural network control, non-affine nonlinear systems
• Public URL: https://uwe-repository.worktribe.com/output/9645677
• Publisher URL: https://www.mdpi.com/1099-4300/24/5/737