Adaptive neural block control design for a class of nonlinear system

An adaptive controller design scheme is proposed for a class of MIMO nonlinear systems with mismatched uncertainties based on the block control principle. The controller is designed using backstepping control techniques and RBF neural networks. By introducing a modified Lyapunov function, the possible control singularity problem is avoided under the condition that the control function matrices are unknown. All the signals of the system are bounded and exponentially converge to the neighborhood of the origin globally. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.