Dwi Sudarno Putra, Seng-Chi Chen, Hoai-Hung Khong, Fred Cheng
Information about rotor positions is critical when controlling a permanent-magnet synchronous motor (PMSM). This information can be gathered using a sensor or through an estimation without using a sensor. This article discusses a machine learning technique for estimating rotor positions. The proposed machine learning observer was constructed using a modified Elman neural network as the main algorithm. The network was trained offline with training data obtained from PMSM field-oriented control simulations and was tested using a validation data set. The PMSM control simulation results revealed that the rotor position estimated through machine learning was comparable with the simulated rotor position; the average error was 0.0127 per unit position. Further-more, the machine learning model was implemented in an experimental PMSM-control hardware platform. Both the simulation and experimental results indicate that the proposed machine learning observer has an acceptable performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan, 71005, Taiwan; Department of Automotive Engineering, Universitas Negeri Padang, Padang, 25132, Indonesia; Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, 70000, Viet Nam; Imeier Green Technology Co., Ltd., New Taipei City, 23511, Taiwan
