Adaptive Backstepping Control Based on Neural Network for a class of Nonlinear Systems

An adaptive backstepping control method based on neural networks is presented for a class of nonlinear systems.Firstly,with the implicit function theorem and mean value theorem,the dynamic characteristic of model tracking error is educed.Then,multi-layer perception neural network is implemented to compensate error by design of suitable weights,which improves the robust performance of the controller.All signals in the closed-loop system are guaranteed to be ultimately bounded by Lyapunov approach and system tracking errors converge to a small neighborhood,the closed-loop system is proved to be guaranteed stable.A simulation example shows the effectiveness of the control method.