Adaptive Neural Network H∞ tracking control for a class of uncertain nonlinear systems

Abstract

An adaptive neural network H∞ tracking control architecture with state observer is proposed for a class of non-affine nonlinear systems with external disturbance and unavailable states. The controller consists of an equivalent controller and H∞ controller. H∞ controller is designed to attenuate the effect of external disturbance and approximation errors of the neural network, and a state observer is used to estimate the system output derivatives which are unavailable for measurement. The overall control scheme and the parameters update laws based on Lyapunov theory can guarantee asymptotic convergence of the tracking error to zero and attenuate the effect of the disturbance to a prescribed level. Simulation results illustrate the effectiveness of the scheme.