Adaptive Backstepping Control for a Class of affine Nonlinear Systems Using Radial Basis Function Neural Networks

In this paper, adaptive backstepping control is proposed for a class of affine nonlinear systems with unknown nonlinearity. The weight update laws for the neural networks, which approximate the nonlinearity, have been derived in the sense of Lyapunov function and Barbalet’s lemma. Thus, the stability of the closed loop system is ensured. To show the efficiency of the proposed scheme, a suitable affine nonlinear system is chosen as a case study. Simulation results show that the proposed scheme can achieve good tracking performance and all the signals of the closed loop system are bounded.

کلیدواژه ها:

Adaptive backstepping control ، affine nonlinear systems ، Radial Basis Neural Network(RBNN)