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This paper developed a control scheme of neural network based on feedforward and PD (proportional and derivative) control for high-precision flight simulator. A radial basis-function neural network (RBFNN) controller was used to learn and to compensate the unknown model dynamics, parameter variation and disturbance of the system on-line. The iterative algorithm of RBFNN parameters is got by Lyapunov stability theory. The effectiveness of the proposed control scheme is evaluated by simulation results and a real-time flight simulator system experiment. It is found that the proposed scheme can reduce the plant's sensitivity to parameter variation and disturbance and high precision performance of flight simulator can be obtained.