基于BP双层神经网络MRAS下PMSM的转速辨识研究

Speed Identification of Permanent Magnet Synchronous Motor Based on BP Double-layer Neural Network MRAS

针对无速度传感器下传统模型参考自适应(MRAS)方法在低速区转速负载发生突变后速度估计准确度下降的问题,利用双层神经网络超强的在线估计以及自适应能力,提出一种基于误差反向传播(BP)双层人工神经网络(ANN)与MRAS相结合的转速辨识方法,实现了对低速范围下转速响应动态性能的改善。通过Matlab仿真以及PMSM驱动控制实物平台,对ANN-MRAS观测器与传统MRAS观测器进行对比分析,结果表明:在转速及负载转矩发生突变后,该方法仍能保持较好动态性能,具有较强的鲁棒性。

Aiming at the problem that the accuracy of the speed estimation of the traditional model reference adaptive system(MRAS)method without speed sensor decreases after the speed load mutation in the low speed range,a speed identification method based on the combination of back propagation(BP)double-layer artificial neural network(ANN)and MRAS was proposed using the super online estimation and adaptive ability of the double-layer neural network,which improved the dynamic performance of the speed response in the low speed range.Through Matlab simulation and PMSM drive control physical platform,the ANN-MRAS observer and the traditional MRAS observer were compared and analyzed.The results show that the proposed method can still maintain good dynamic performance and has strong robustness after sudden changes in speed and load torque.