开关磁阻电机模糊神经网络PID转速控制

Fuzzy Neural Network PID Speed Control of Switched Reluctance Motor

针对开关磁阻电机调速系统难以控制的问题,提出了基于模糊FCMAC神经网络的PID控制方法,该方法的主要思想是将马丹尼直接推理法与CMAC神经网络相结合,构成模糊FCMAC神经网络,实时调整PID控制参数。仿真结果表明,与传统的PID控制方法相比较,该方法大大改善了开关磁阻电机调速系统的动、静态性能,且无需精确的数学模型,控制精度高,超调量小,对干扰有较高的鲁棒性。

Aiming at the problem that the switched reluctance drive is difficult to control, this paper proposes a new approach based on fuzzy FCMAC neural network PID control. The main idea of the new approach is to constitute fuzzy FCMAC neural network with the real-time adjustment of PID control parameters through combining Ma Danni direct-rationalistic method with CMAC neural network. The simulation results show that compared with the traditional PID control method, the proposed control method greatly improves dynamic and static performance of SRD, and that it does not require accurate mathematical model and has high control accu racy, small overshoots and high robustness to disturbances.