Ac Servo System. Rbf Neural Network And Pid Compound Control Strategy

With the development of modern science, AC servo technique has been widely used. AC servo system is a high order, nonlinear, coupling system. A single control strategy can hardly get ideal control effect. The performance of system is highly influenced by the friction, parameter variations and external load disturbances. In order to enhance static and dynamic performances of the system, this dissertation is to study a combined control strategy—PID control and RBF neural network control.In this dissertation, the linear mathematical model and friction nonlinear model of AC servo system are presented. Relay feedback's results on the existence and stability of the limit cycles are established by the servo system's transformatiOn function. A method based on relay feedback technique is presented to identify multiple ,points on the process frequency response, then to pattern ideal closed loop characteristic of AC servo systemt. To overcome the friction and variations in the system, a RBF neural network. controller is designed as a parallel controller, and the stability of system is demonstrated. Meanwhile, a hybrid learning algorithm for RBF network based on improved nearest neighbor-clustering algorithm and gradient descent training, is proposed to identify system model and design the RBF controller.Simulation results show that AC servo system based on proposed mixed control strategy can satisfy the system's need of speediness,Stability and robustness preferably.