A Neural-Network-Based Model Reference Speed Control for High Precision Motion Control Systems

This paper developed a model reference control scheme by introducing a PI controller and RBF neural network (RBFNN) controller for speed control of high precision motion control systems. In the paper the RBF controller is able to online learn the unknown model dynamics, parameter variation and disturbance of the system. The model reference adaptive control (MRAC) scheme is used to give better solutions with online adaptation. By using a PI controller, the dynamic performance of the system is improved. This paper introduced a feedback parameter , which makes it easier to assign the poles of the system. Thus, it is feasible to preserve favorable model-following characteristics under various conditions. The effectiveness of the proposed control scheme is demonstrated by simulation. It is found that the proposed scheme can reduce the plant’s sensitivity to parameter variation and disturbance. High precision performance is obtained when given constant and sine wave disturbance at the same time.