Adaptive neural funnel control for a class of pure-feedback nonlinear systems with event-trigger strategy. Issue 13 (2nd October 2020)

• Record Type: Journal Article
• Authors: Gao, Chuang; Liu, Xiao-Ping; Wang, Huan-Qing; Zhao, Nan-Nan; Wu, Li-Bing
• Is Part Of: International journal of systems science. Volume 51:Issue 13(2020)
• Page Start: 2307
• Page End: 2325
• Publication Date: 2020-10-02
• Subjects: Event-triggered control -- backstepping -- funnel control -- neural network -- pure-feedback systems; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2020.1793237
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : In this paper, an event-trigger-based adaptive funnel control problem is considered for a class of pure-feedback nonlinear systems. Due to the nonaffine variables existed in the virtual controls of pure-feedback systems, the implicit function theorem and mean-value theorem are adopted to guarantee the existence of the ideal virtual controls, which can be effectively approximated by neural networks. Then, a novel event-triggered control strategy is designed to consume less communication resources. The event-triggered condition depends on the amplitudes of the control input, the tracking error and a fixed threshold, which makes the control more flexible in real applications. Furthermore, the proposed control scheme ensures the transient and steady state performance for the tracking errors by constructing a funnel constraint function. Also, the stability analysis proves that all the signals of the closed-loop system are uniformly ultimately bounded. Finally, the feasibility and effectiveness of the proposed control scheme are verified through the simulation.