Command filter‐based adaptive neural event‐triggered control of MIMO pure‐feedback systems with full‐state time‐varying constraints. (13th June 2022)

• Record Type: Journal Article
• Authors: Zhu, Xinfeng; Huang, Jun; Ding, Wenwu; Zhang, Tianping
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 9(2022)
• Page Start: 2167
• Page End: 2189
• Publication Date: 2022-06-13
• Subjects: command filter -- dynamic surface control -- event‐triggered -- full‐state constraints -- MIMO; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3448
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article is concerned with the issue of adaptive event‐triggered control for a class of pure‐feedback multi‐input multi‐output (MIMO) nonlinear systems with full‐state time‐varying constraints. By using the hyperbolic tangent as nonlinear mapping technique, the uncertain constrained MIMO non‐affine system is changed into a novel unconstrained MIMO system. Dynamic surface control (DSC) strategy is used to solve the issue of "explosion of complexity." Command filter is adopted to overcome the insufficient of the DSC method by the error compensation mechanism. Adaptive event‐triggered control scheme is developed for the transformed non‐affine system based on relative threshold mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. All the signals in the controlled system are proved to be semi‐globally uniformly ultimately bounded by adding to compensation signals into the whole Lyapunov function. Two simulation examples demonstrate the capability of the proposed control strategy.