Robust adaptive neural network control of a class of perturbed uncertain pure-feedback nonlinear systems with dead-zone input

Abstract

A robust adaptive neural control design approach is presented for a class of perturbed pure-feedback nonlinear systems with unknown dead-zone input. In the controller design, the implicit function theorem and the mean value theorem were employed to overcome the problem of controller circular construction. And by using the technique of single neural network online approximation, the complexity of the controller design was reduced significantly. The result of stability analysis shows that the uniform ultimate boundedness of all the closed-loop system signals can be guaranteed, and the steady-state tracking error can be made arbitrarily small by choosing the control parameters appropriately. A simulation example was given to demonstrate the effectiveness of the proposed approach.