Control of Discrete-Time Nonlinear Systems Using Backstepping Technique in the Presence of Saturation and Dead-Zone Constraints

Abstract

This paper proposes the work on the back stepping controller for the class of Discrete-time nonlinear system under amplitude & rate saturation, dead-zone constraints. A robust adaptive neural network (NN) control is developed for a general class of uncertain single-input-single output (SISO) discrete time nonlinear system with unknown system dynamics and input nonlinear ties. Here ( ) function is employed to saturate the amplitude and rate of system input. For input nonlinear ties like saturation and dead zone, discrete time SISO nonlinear system in combination with back stepping and Lyapunov synthesis is proposed for adaptive NN design with guaranteed stability. To reduce the effect of dead-zone a dead-zone compensator or dead-zone inverse is designed. Chebyshev Neural Networks (CNNs) are used to approximate the unknown nonlinear functions in system dynamics. New weight update laws are derived to make this scheme adaptive and show the stability of this scheme. Finally simulation results are presented in this paper to show the effectiveness of proposed scheme.