关键词: 通用电机运动控制器(UMAC), RBF神经网络, 自适应性, 鲁棒性, 动静态性能

Abstract: The self-adaptability and robustness of traditional PID control and current fuzzy-PID control adopted by universal motion and automation controller (UMAC) were not strong, and the static-dynamic performance of servosystem controlled by them was not ideal. In this paper, RBF neural network was adopted to automatically adjust PID control parameters, which could strengthen the self-adaptability and robustness of servosystem and improve the controlling characteristics of servo system. This control algorithm was implemented by embedded PLC program of UMAC. The experimental results of step response and sinusoidal tracking response show that the rise time of servo motorposition step response by RBF neural network tuning PID control decreases from 0.164 s by traditional PID control and 0.118 s by fuzzy-PID control to 0.017 s, the peak time decreases from 0.196 s by traditional PID control and 0.131 s by fuzzy-PID control to 0.023 s, and the setting time decreases from 0.216 s by traditional PID control and 0.142 s by fuzzy-PID control to 0.025 s, which mean that the motor responds faster. Meantime, the dynamic following error peak value of motor position sinusoidal response by RBF neural network tuning PID control decreases from 188 counts by traditional PID control and 120 counts by fuzzy-PID control to 39 counts, and the error fluctuation issmall and steady, which mean that the dynamic tracking performance of the motor is significantly improved.

Key words: universal motion and automation controller (UMAC), RBF neural network, self-adaptability, robustness, static-dynamic performance