An observer-based adaptive neural network tracking control for nonlinear systems

In this paper, an observer-based adaptive neural network (OBANN) tracking control scheme is proposed for uncertain nonlinear systems with time-delays and external disturbances. The adaptive neural network model is used to approximate the dynamics of the nonlinear system, while an observer-based control scheme is to stabilize the system. By applying the adaptive neural dynamics, we can on-line tune the weights of the neurons of the neural model and the bounds of the gains of delay states directly using linear analytical results. From Lyapunov criterion and Riccati-inequality, it is shown that the stability of the closed-loop system is guaranteed and the closed loop system signals are uniform ultimate boundedness and achieve H¿ tracking performance. Finally, a numerical example of a two-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.