IJCAS

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.

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