Abstract: A robust adaptive tracking control scheme is presented for a class of multi-input multi-output (MIMO) nonlinearminimum phase systems with unknown mathematical models, uncertainties and external disturbances. Gaussian based radial basis function (RBF) neural networks are used to approximate the plant’s unknown nonlinearities, and a high-gain observer is used to estimate the unmeasured states of the system. The proposed robust adaptive controller can guarantee that: the closed-loop system is stable; all the states are bounded; the tracking errors are uniformly ultimately bounded; and the ultimate bound can be made arbitrarily small. Simulation results demonstrate the effectiveness of the proposed method.

Key words: MIMO, nonlinear minimum phase systems, robust adaptive control, output feedback, RBF neural network, high-gain observer