Fast finite‐time adaptive neural fault‐tolerant tracking control for multi‐input multi‐output systems with full‐state constraints. (23rd June 2022)

• Record Type: Journal Article
• Authors: Wang, Huanqing; Shen, Liya; Wang, Ding; Niu, Ben; Zhao, Xudong
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 9(2022)
• Page Start: 2269
• Page End: 2288
• Publication Date: 2022-06-23
• Subjects: actuator faults -- adaptive neural control -- fast finite‐time control -- full‐state constraints -- MIMO nonlinear systems; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3454
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article investigates the problem of fast finite‐time adaptive neural fault‐tolerant tracking control for multi‐input multi‐output (MIMO) nonlinear systems with full‐state constraints and actuator faults. The radial basis function neural networks are introduced to deal with unknown nonlinear functions. In addition, an additive transformation and one‐to‐one mapping method are employed to deal with the control problem of MIMO nonlinear systems with full‐state constraints. Based on the fast finite‐time stability theory and adaptive backstepping technique, which guarantees all the closed‐loop system signals are bounded, the tracking error eventually converges to a small neighborhood of the origin in a fast finite‐time and full‐state constraints are not violated. Finally, simulation results demonstrate the feasibility of the proposed control scheme.