Event-Triggered Neuro-Adaptive Fixed-Time Control for Nonlinear Switched and Constrained Systems: An Initial Condition-Independent Method

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 71; no. 5; pp. 2229 - 2239
• Main Authors: Wang, Xin, Zhou, Yuhao, Luo, Biao, Li, Yushuai, Huang, Tingwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Circuit stability; Constraints; Control systems; Convergence; event-triggered control; fixed-time stability; Learning; Liapunov functions; Neural networks; Nonlinear control; Nonlinear systems; Stability criteria; switched nonlinear systems; Switches; Time-varying constraints; Tracking control; unknown control gains
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2023.3342070

This paper investigates a neuro-adaptive fixed-time tracking control issue for switched nonlinear systems subject to asymmetric time-varying constraints and unknown control gains. Unlike the current study on constraint problems, the system's initial condition is unavailable in this article, which causes specific difficulties in constructing the Barrier Lyapunov Function. A novel shifting function is presented to unify the initial values of all system states. In addition, the system convergence time becomes known and adjustable by utilizing the Nussbaum gain technique and fixed-time stability criterion. An adaptive neural tracking control scheme is proposed based on the learning ability of neural networks and fixed-time theory. To alleviate the computational burden, we present the single learning parameter method such that the number of adaptive laws is reduced significantly. Furthermore, a novel switching threshold mechanism that considers the system errors is developed to balance the communication burden and control performance. Finally, the simulation example illustrates the feasibility of the proposed control strategy.