基于模糊变步长神经网络的永磁同步电机控制系统

Permanent Magnet Synchronous Motor Control System Based on Fuzzy Variable Step Size Neural Network

永磁同步电机在传统工业生产、调速系统中应用较为广泛,但是该电机拥有非线性、强耦合、多变量等特性,使系统的响应能力和抗干扰能力降低。为保证系统平稳的运行,本文将RBF神经网络辨识器应用到永磁同步电机控制系统中,并使用模糊逻辑优化神经网络的学习步长,提高了RBF神经网络的辨识精度。仿真结果表明,这种优化后的神经网络辨识器对永磁同步电机速度控制有着良好的运行性能,比以往的传统PID控制转速超调量更小,更快地趋于平稳。

Permanent magnet synchronous motor has been widely applied in traditional industrial production and the speed control system.However,the motor has the characteristics of nonlinear,strong coupling and multi variable.hich reduces system response ability and anti-interference ability.To ensure the smooth operation of the system,this paper applies RBF neural network identification device to permanent magnet synchronous motor control system,and uses fuzzy logic to optimize the learning step of neural network.As a consequence,it improves the identification precision of the RBF neural network.The simulation results show that he optimized neural network identifier has a good running performance for the speed control of permanent magnet synchronous motor,which has smaller speed overshoot volume and achieves tomooth faster than the traditional PID control.