说明：  针对传统的PID控制器参数固 定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特 点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp,ki,kd参数。将算法运用到电机控制系统的PID 参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。
(Traditional PID controller parameters fixed in the control effect caused by the problem of poor design method based on fuzzy RBF Neural Network Intelligence PID controller. The method combines strong reasoning ability of fuzzy control and neural network learning ability of the characteristics of the RBF neural network and fuzzy control are combined to adjust the PID controller parameters online, fix the whole set is adapted to control the object kp, ki, kd parameter. The algorithm is applied to the motor control system PID parameter optimization, the simulation results show that the algorithm based on the design of PID controller to improve the dynamic performance and stability of the motor control system.)