An Optimal PID Controller for Linear Servo-System Using RBF Neural Networks

An optimal PID controller using radical basis function (RBF) for the so-called direct-drive permanent magnet linear synchronous motor (PMSM) is proposed in this paper. The control system is designed using two neural networks. One neural network with single neuron is used for the realization of the PID controller; the other three-layered RBF neural network is used to identify PMSM system to provide the sensitivity information to the neural controller. Also, to guarantee the stability and tracking performance of the control system, a modified gradient descendent (MGD) method for the weights tuning of the neural controller is derived with the introduction of a modified optimal quadric performance index. Thus, the proposed control scheme is capable of reconciling the conflict between the tracking performance and anti-disturbance ability of controller for the linear servo system. Finally, the simulation validation results have shown that the proposed method presents a good tracking performance and satisfactory robustness against the parametric variation and ambient disturbances.