Adaptive backstepping control for nonlinear systems using RBF neural networks

In this paper, a neural network (NN) control approach is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By a special design scheme, the approach avoids the controller singularity problem perfectly. All the signals in the closed loop are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proved to converge to a small neighborhood of the desired trajectory. The control performance of the closed loop system under the controller can be guaranteed by suitably choosing the design parameters. Simulation results show the effectiveness of the approach.