Neural observer-based adaptive prescribed performance control for uncertain nonlinear systems with input saturation

Abstract

This paper is concerned with the problem of adaptive prescribed performance control for a class of uncertain strict-feedback nonlinear systems with input saturation. A novel indirect neural observer is presented with the ADALINE network incorporated into the conventional sliding mode term. The radial basis function neural network (RBFNN) approximation is utilized to handle the system uncertainties. By using the error transformation function, the tracking error is limited to the given boundary. Then, an auxiliary system is constructed to solve the problem of input saturation. Based on NN approximation and state observation results, a backstepping prescribed performance control scheme is proposed. The boundness of all closed-loop signals and the prescribed tracking performance are proved. Simulation results show the high performance of the designed technique.