ID PW
 
* Join the Member of ICROS 
* Need your ID or Password?
 
 
 
Subject Keyword Abstract Author
 
 
Adaptive NN Tracking Control for Periodically Time-varying Nonlinear Switching Systems

Xiaoli Yang, Jing Li*, Jian Wu, and Xiaobo Li
International Journal of Control, Automation, and Systems, vol. 20, no. 12, pp.4037-4049, 2022

Abstract : This paper tackles the tracking control problem for a class of uncertain switching nonlinear systems with periodically time-varying parameters. To handle the unknown nonlinear functions and periodically time-varying parameters simultaneously, the radial basis function neural networks and Fourier series expansion are combined together. At the same time, the command filter approach is utilized to eliminate the “explosion of complexity” problem of the traditional backstepping technique and relax the assumption on the reference signal as well. Compared with the previous results, the filter errors are compensated by introducing the compensated signals and the upper bounds of the approximation errors are estimated as well. A compound adaptive neural network control strategy is proposed to warrant that the closed-loop system is semi-globally ultimately uniformly bounded. Two examples are provided to demonstrate the effectiveness of the reported approach.

Keyword : Command filter, Fourier series expansion, neural network, nonlinear switching system, periodically time-varying parameters.

 
Copyright ⓒ ICROS. All rights reserved.
Institute of Control, Robotics and Systems, Suseo Hyundai-Ventureville 723, Bamgogae-ro 1-gil 10, Gangnam-gu, Seoul 06349, Korea
Homepage http://eng.icros.org | Tel. +82-2-6949-5801 (ext. 3) | Fax. +82-2-6949-5807 | E-mail icros@icros.org