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。-Fixed for the traditional PID controller parameters lead to poor results in the control issue, based on fuzzy RBF neural network intelligent PID controller design method.

The method combines fuzzy control powers of reasoning and neural network learning ability and fuzzy control with RBF neural network combined line tuning of PID controller parameters, the entire

Identified a group suitable for the control object kp ki kd parameters. PID parameter optimization algorithm is applied to the motor control system, the simulation results show that the PID controller design based on this algorithm improves the dynamic performance and stability of the motor control system.