An adaptive sliding mode control for a class of uncertain nonlinear systems based on neural networks

An adaptive sliding mode controller based on neural network for a class of SISO nonlinear systems is proposed in this paper. The considered system is assumed to be separated as a nominal subsystem and an uncertain subsystem. The controller is composed of a nominal controller and a compensative controller , the former is used to stabilize the nominal subsystem, while the later is an adaptive slide mode controller based on neural network for compensating the uncertain subsystem. The slide mode controller is designed based on Lyapunov stability theory. The neural network is used to approximate the lumped uncertainties of the nonlinear systems. Theoretical analysis and computer simulations indicate that the proposed strategy can not only solve the tracking problem of the uncertain nonlinear systems, but also guarantee the stability of the closed - loop systems.