Event‐triggered adaptive output‐feedback control for nonlinear state‐constrained systems using tangent‐type nonlinear mapping. Issue 5 (7th July 2021)

• Record Type: Journal Article
• Authors: Wei, Yan; Zhou, Pingfang; Xie, Weicheng; Tang, Jiwei; Duan, Dengping
• Is Part Of: Asian journal of control. Volume 24:Issue 5(2022)
• Page Start: 2189
• Page End: 2201
• Publication Date: 2021-07-07
• Subjects: event‐triggered control -- neural network -- nonlinear mapping -- nonlinear systems -- output‐feedback; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2599
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: This paper investigates the problem of event‐triggered adaptive output‐feedback control for multi‐input and multi‐output (MIMO) uncertain nonlinear systems with time‐varying full state constraints. A tangent‐type nonlinear mapping function is proposed to transform the state‐constrained system into a new one free of constraints, such that the time‐varying full state constraints are never violated. Radial basis function neural networks are introduced to compensate for the unknown functions. A state observer is established to estimate the unmeasured states. A suitable event‐triggering rule is presented to determine when to transmit control laws. Through Lyapunov analyses, all closed‐loop signals are proved to be semiglobally uniformly ultimately bounded, and time‐varying full state constraints are never violated. Finally, simulations are presented to evaluate the efficacy of the proposed approach.