Neural network adaptive tracking control for a class of uncertain switched nonlinear systems

• Published in: Neurocomputing (Amsterdam) 2018-08, Vol.301, p.1-10
• Main Authors: Yin, Qitian, Wang, Mao, Li, Xiaolei, Sun, Guanghui
• Format: Article
• Language: English
• Subjects: Adaptive control; Backstepping control; Lyapunov stability; Neural networks; Switched nonlinear systems
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2018.01.047

•Study the method of the tracking control of the switched uncertain nonlinear systems under arbitrary switching signal controller.•A multilayer neural network adaptive controller with multilayer weight norm adaptive estimation is been designed.•The adaptive law is expand from calculation the second layer weight of neural network to both of the two layers weight.•The controller proposed improve the tracking error performance of the closed-loop system greatly. The paper is concerned with the tracking control problem of the switched nonlinear systems under arbitrary switchings. Multilayer neural networks are used as a tool for modeling nonlinear functions up to a small error tolerance. In order to improve the tracking performance, through the expansion of the traditional neural network controller design, a multilayer neural network adaptive controller with multilayer weight norm adaptive estimation has been designed. Further, the weight norm adaptive laws are not only used to approximate the first layer network but also every layers. The adaptive updated laws of the controller have been derived from the Lyapunove function method, and the adaptive neural network control schemes have been developed to achieve more smaller semi-global ultimate boundedness of all the signals in the closed-loop than ever before. Finally, the simulation examples of two theorems are given to illustrate the effectiveness of the proposed control scheme separately.