Output Feedback Tracking Control for a Class of Mimo Nonlinear Minimum Phase Systems Based on Rbf Neural Networks

An adaptive neural feedback tracking control scheme is presented for a class of multi-input multi-output nonlinear minimum phase systems with uncertainties and external disturbances. Gaussian basis RBF neural networks are used to approximate the plant unknown nonlinearities, and a high-gain observer is used to estimate the states which can not be measured. The proposed controller can guarantee that the closed-loop system is stable, all the states are bounded and the tracking errors are uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the proposed method.