Chaos Prediction of Motor System Based on NG-RC

doi:10.19596/j.cnki.1001-246x.8615

Abstract

Abstract:

In this paper, chaos prediction of motor system is realized based on the next generation reservoir computing, and unknown variable data can be predicted based on existing data. Compared with the traditional reservoir computing, the next generation reservoir computing uses direct connection of data itself and requires smaller training data sets. And the next generation reservoir computing avoids the complex parameter optimization calculation of traditional reserve pool network through high-dimensional conversion, which greatly improves the computing speed. This research result provides a new research idea for chaotic prediction of motor systems.

Key words: next generation reservoir computing, permanent-magnet synchronous motor, chaos prediction

Wenxin CAO, Duqu WEI. Chaos Prediction of Motor System Based on NG-RC[J]. Chinese Journal of Computational Physics, 2023, 40(4): 519-526.

Figures/Tables 7

Fig.1 Reservoir computing model structure (a) a traditional reservoir computing, RC; (b) next generation reservoir computing (nonlinear vector autoregression), NG-RC

Fig.2 Forecasting PMSM system using the NG-RC

Fig.3 Synchronization error of state variables in test phase

Fig.4 Inference PMSM variables id using NG-RC

Table 1 The best results of LSTM、PSO-LSTM、NG-RC

模型	测试数据集大小	RMSE
LSTM	10 000	0.025 4
PSO-LSTM	10 000	0.010 1
NG-RC	400	0.002 1

Fig.5 PMSM return map of id

Table 2 Estimated speed up of the NG-RC for the PMSM forecasting task

	M_warmup	M_train	N_nonlin	N_total	N	σ_r	Speed up
NG-RC	2	400	21	28
LC-RC	1 000	1 000		100	100	0.01~0.05	33~163
Le-RC	0	5 000		300	300	0.02	1.5×10³
L-RC	10⁵	6×10⁴		4 000	2 000	0.02	3.2×10⁶

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