RESEARCH ARTICLE
Event-triggered adaptive neural asymptotic tracking of uncertain constrained nonlinear systems without feasibility condition
Xiao-Yue Jiang,
Yuan-Xin Li,
Jinzi Yang,
Shaocheng Tong,
Xiao-Yue Jiang
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorCorresponding Author
Yuan-Xin Li
College of Science, Liaoning University of Technology, Jinzhou, China
Correspondence Yuan-Xin Li, College of Science, Liaoning University of Technology, Jinzhou, China.
Email: [email protected]
Search for more papers by this authorJinzi Yang
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorShaocheng Tong
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorXiao-Yue Jiang,
Yuan-Xin Li,
Jinzi Yang,
Shaocheng Tong,
Xiao-Yue Jiang
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorCorresponding Author
Yuan-Xin Li
College of Science, Liaoning University of Technology, Jinzhou, China
Correspondence Yuan-Xin Li, College of Science, Liaoning University of Technology, Jinzhou, China.
Email: [email protected]
Search for more papers by this authorJinzi Yang
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorShaocheng Tong
College of Science, Liaoning University of Technology, Jinzhou, China
Search for more papers by this authorFunding information: the Distinguished Young Scientific Research Talents Plan in Liaoning Province, JQL201915402; XLYC1907077; the Funds of National Science of China, 61773188; 61973146; 62173172
Summary
In this article, an event triggered control problem is investigated for uncertain nonlinear systems with full state constraints. First, the original-constrained system is converted into an equivalent totally unconstrained one by using a state dependent function. Then, an event-triggered adaptive neural-network controller is designed to reduce the communication burden. In addition, rigorous theoretical analyses have been conducted to show that the proposed controller can ensure that all signals in the closed-loop control system are bounded and that the asymptotic convergence with zero tracking error is achieved. In the meanwhile, the state constrains are not violated. Finally, the effectiveness of the presented control scheme is verified through the simulation results.
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