Abstract: A novel model-following nonlinear adaptive robust control strategy with neural network dynamic compensation is presented for a class of complex nonlinear systems. A neural network is used as the compensator to eliminate unmodelled dynamics and nonlinear coupling effects. It improves dynamic performance and steady accuracy of the system; even if there exists disturbance, the output of the system can accurately track those of the reference model. Robust stability of the closed-loop system under this control law is proved by Lyapnov theory. Simulation results reveal that this method has good tracking performance and robustness. Further more, the control algorithm is very simple and efficient and easy for on-line control.