A neural composite dynamic surface control for pure‐feedback systems with unknown control gain signs and full state constraints. (28th August 2019)

• Record Type: Journal Article
• Authors: Liu, Wei; Ma, Qian; Lu, Junwei; Xu, Shengyuan; Zhang, Zhengqiang
• Other Names: Edwards Christopher guestEditor.; Simani Silvio guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 29:Number 16(2019)
• Page Start: 5720
• Page End: 5743
• Publication Date: 2019-08-28
• Subjects: composite neural networks approximation -- dynamic surface control -- full‐state constraints -- pure‐feedback systems -- unknown control gain signs; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.4714
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: This paper investigates a composite neural dynamic surface control (DSC) method for a class of pure‐feedback nonlinear systems in the case of unknown control gain signs and full‐state constraints. Neural networks are utilized to approximate the compound unknown functions, and the approximation errors of neural networks are applied in the design of updated adaptation laws. Comparing the proposed composite approximation method with the conventional ones, a faster and better approximation performance result can be obtained. Combining the composite neural networks approximation with the DSC technique, an improved composite neural adaptive control approach is designed for the considered nonlinear system. Then, together with the Lyapunov stability theory, all the variables of the closed‐loop system are semiglobal uniformly ultimately bounded. The infringements of full state constraints can be avoided in the case of unknown control gain signs as well as unknown disturbances. Finally, two simulation examples show the effectiveness and feasibility of the proposed results.