Adaptive decentralized tracking control of a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network. (December 2019)

• Record Type: Journal Article
• Authors: Han, Yu-Qun; Zhu, Shan-Liang; Duan, De-Yu; Chu, Lei; Yang, Shu-Guo
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 41:Number 16(2019)
• Page Start: 4499
• Page End: 4510
• Publication Date: 2019-12
• Subjects: Decentralized -- tracking control -- large-scale nonlinear system -- dead-zone input -- neural network; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331219861189
• Languages: English
• ISSNs: 0142-3312

Abstract

In this paper, an adaptive decentralized control approach is proposed for a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network. Firstly, the dead-zone outputs are firstly represented as simple linear systems with a static time-varying gain and bounded disturbance by introducing characteristic function. Secondly, in the controller design, neural networks are utilized to approximate the unknown nonlinear functions. Thirdly, an adaptive decentralized tracking control approach is constructed via backstepping design technique. It is shown that the proposed control approach can assure that all the signals of the closed-loop system semi-globally uniformly ultimately bounded and the tracking errors finally converge to a small domain around the origin. The proposed method can get precise tracking results with low computational cost, and have a good real-time performance and convergence. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.