Chaotic time series prediction of motor system based on WOA-MFRFNN

Abstract The complexity of the operation of permanent magnet synchronous motor (PMSM) has heightened interest in its stability and reliability. Evidence suggests that chaotic oscillatory behavior can occur during PMSM operation, making the prediction of such chaotic motion crucial for maintaining stable motor performance. In this paper, a WOA-MFRFNN (Whale Optimization Algorithm-Multi Functional Recurrent Fuzzy Neural Network) model is proposed to predict chaotic time series of PMSM, where WOA is used to train the network weights and the MFRFNN contains two TSK fuzzy rules for fuzzy neural networks, which are used for the output and the state of network, respectively. The experimental results of WOA-MFRFNN are compared with those of SSA-BP, ROA-RF and iTransformer. The results imply that the WOA-MFRFNN outperforms other methods and the RMSE can reach 5.45 × 10 −4 . It is also shown that the WOA-MFRFNN model exhibits good predictive effect on the real-world PMSM dataset.