A DSC approach to adaptive neural network tracking control for pure-feedback nonlinear systems

Abstract

In this paper, by incorporating the dynamic surface control technique into a neural network based adaptive control design framework, we develop a backstepping based adaptive control design approach for uncertain non-affine pure-feedback nonlinear systems. By using the dynamic surface control technique, the problem of “explosion of complexity” inherent in existing methods are eliminated effectively. Stability analysis shows that the uniform ultimate boundedness of all the signals in the closed-loop system can be guaranteed, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness of the proposed scheme.