Adaptive Neural Tracking Control for Uncertain Switched Nonlinear Non-lower Triangular System with Disturbances and Dead-zone Input

Abstract

This work investigates the adaptive neural tracking control problem for a class of uncertain switched nonlinear non-lower triangular systems with disturbances and dead-zone input. First, radial basis function (RBF) neural networks (NNs) serve as a flexible tool to approximate the unknown nonlinear functions. Then during the controller design, the dynamic surface control (DSC) method is used to avoid the issue of “explosion of complexity”, and only one adaptive law is adopted to reduce the computational burden. What’s more, a few classical mathematical approaches are used to handle the design difficulties caused by dead-zone input, and the proposed controller guarantees the closed-loop system signals are semi-globally uniform ultimate boundedness (SGUUB). Finally, a simulation example is given to illustrate the availability of the proposed control scheme.