NN-based adaptive dynamic surface control for a class of nonlinear systems with input saturation

In this paper, a new direct robust adaptive neural network controller is present for uncertain nonlinear systems with input saturation and external disturbances. By incorporating dynamic surface control (DSC) technique into a neural network based adaptive control design framework, the control design is achieved based on backstepping technique. By virtue of this technique, the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided. At the same time, the controller singularity problem is avoided completely and the effect of input saturation constrains is considered in this control design. In addition, it is proved that all the signals in the closed-loop system are semi-global uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin. Finally, simulation studies are given to demonstrate the effectiveness of the proposed scheme.