RBFNN speed compensate controller use in high precision servo systems

Abstract

This paper proposed three control schemes using model reference adaptive control (MRAC) and RBF neural network(RBFNN). Through comparision, it proposed a MRAC scheme with RBFNN compensator for speed control of high precision servo systems. The MRAC scheme is used to give better solutions with online adaptation and guarantee the stablity of the system. In the feedback channel, the parameter K p makes it easier to design the system poles. By using a PI controller before K p , the dynamic performance of the system is improved. As a speed compensate controller, the RBFNN is designed parallel with the model reference control. The RBFNN controller is able to online learn the unknown model dynamics, parameter variation and disturbance 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.