Direct adaptive neural networks control for a class of uncertain nonlinear systems with prespecified tracking performance

In this paper, we propose an adaptive control scheme for a class of uncertain nonlinear systems in strict-feedback form. By combining dynamic surface control technique with neural networks, explosion of complexity in backstepping design is avoided, and only one parameter is needed to be updated. Moreover, by applying performance function and output error transformation, the prespecified tracking performance, i.e., the convergence rate, the allowable maximum overshoot and the steady state error, can be achieved. It is proved that semi-global stability of the closed-loop system can be guaranteed. Finally, simulation results are given to demonstrate the efficiency of the proposed scheme.