| 李瑞琦， 边火， 杨树炳， 张华.基于RBF神经网络的永磁同步电机控制系统[J].轻工机械,2022,40(4):52-56 |
| 基于RBF神经网络的永磁同步电机控制系统 |
| Permanent Magnet Synchronous Motor Control System Based on RBF Neural Network |
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| DOI：10.3969/j.issn.1005 2895.2022.04.009 |
| 中文关键词: 永磁同步电机 速度控制 径向基函数神经网络 PID控制 |
| 英文关键词:PMSM（Permanent Magnet Synchronous Motor） speed control RBF（Radical Basis Function） neural network PID control |
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| 中文摘要: |
| 由于永磁同步电机控制系统具有非线性等特点，而使传统PID人工调节参数过程过于繁琐，且无法根据电机的运行状态改变参数，为了提高控制精度、增强控制系统的自适应能力，课题组以电流环PI控制为基础，结合径向基（RBF）神经网络对永磁同步电机进行在线辨识，根据辨识得到的灵敏度信息整定PID控制参数，建立参考模型。在MATLAB软件中利用Simulink中建立了PMSM模型，通过对比PID、RBF PID在启动环节和负载变化时的速度变化，验证了改进BRF PID控制的有效性。仿真结果表明RBF PID控制具有更快的响应，更好的抗干扰能力。 |
| 英文摘要: |
| Due to the nonlinear characteristics of the permanent magnet synchronous motor control system, the traditional PID parameter adjustment process is too cumbersome, and the parameters cannot be changed according to the motor running state. In order to improve the precision of control and enhance the adaptive ability of the control system, based on the current loop PI control, combined with radial basis (RBF) neural network, the permanent magnet synchronous motor was identified online. The PID control parameters were adjusted according to the sensitivity information identified and a reference model was established. The PMSM model was established in Simulink in MATLAB software. The effectiveness of the improved BRF PID control was verified by comparing the speed changes of PID and RBF PID in the starting link and load changes. Simulation results show that RBF PID has faster response and better anti interference ability. |
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