Nonlinear Adaptive Dynamic Surface Control Based on S-function Regulation

A robust and adaptive dynamic surface control approach based on neural networks is presented for a general class of MIMO(multi-input multi-output) nonlinear systems with unknown control gain.Dynamic surface control(DSC) is used to eliminate the shortcoming of calculation explosion in traditional backstepping method.At the same time,the Sfunction is introduced into the adaptive mechanism so that the adaptive laws can regulate the convergence speed of neural networks,which resolve the chattering phenomenon in the initial period of adaptive control.It is shown with Lyapunov stability theory that all signals in the closed loop system are ultimately bounded and the output tracking error converges to an arbitrary small compact set.Simulation results demonstrate the effectiveness of the proposed approach.