Command filter‐based adaptive prescribed performance tracking control for uncertain pure‐feedback nonlinear systems with full‐state time‐varying constraints. (13th April 2021)

• Record Type: Journal Article
• Authors: Zhu, Xinfeng; Ding, Wenwu; Zhang, Tianping
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 11(2021)
• Page Start: 5312
• Page End: 5329
• Publication Date: 2021-04-13
• Subjects: adaptive tracking control -- command filter -- full‐state constraints -- prescribed performance; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5540
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This article is concerned with the issue of adaptive prescribed performance tracking control for a class of pure‐feedback nonlinear systems with full‐state time‐varying constraints. By using the hyperbolic tangent function as nonlinear mapping technique, the obstacle of the full‐state constraints is resolved. A novel adaptive control method is proposed by prescribed performance control with command filtered backstepping design, and the insufficient of the dynamic surface method is conquered by the error compensation mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, it is shown that all signals in the closed‐loop system are semiglobal uniformly ultimately bounded and the output tracking error always converges to the prescribed performance bound. Two simulation examples demonstrate the capability of the proposed control strategy.