Robust adaptive neural control of uncertain pure-feedback nonlinear systems

Abstract

A robust adaptive neural control design approach is presented for uncertain pure-feedback nonlinear systems. In the control design process, only one neural network is used to approximate the lumped unknown part of the systems, and the problem of complexity growing existing in conventional methods can be eliminated completely. The result of stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals, and the control performance can be guaranteed by an appropriate choice of the control parameters. A simulation example is given to demonstrate the effectiveness of the proposed approach.