Command Filter and Universal Approximator Based Backstepping Control Design for Strict-Feedback Nonlinear Systems With Uncertainty

This paper presents an improved backstepping control implementation scheme for a n-dimensional strict-feedback uncertain nonlinear system based on command filtered backstepping and adaptive neural network backstepping. In this approach, n command filters and one neural network are applied to reconstruct the approximations of unknown nonlinearities, which are related to the system uncertainties including the system's unmodeled dynamics and external disturbances. Then, one can use the negative feedback of these approximations to compensate the system uncertainties. Moreover, convex optimization and soft computing technique are adopted to design the update law of the weights of the neural network, and Lyapunov stability criterion is used to prove the stability of the closed-loop system. Finally, simulation results are given to show the effectiveness of the proposed methods.