Realization of Intelligent Observer for Sensorless PMSM Drive Control

Dwi Sudarno Putra, Seng-Chi Chen (), Hoai-Hung Khong and Chin-Feng Chang
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Dwi Sudarno Putra: Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
Seng-Chi Chen: Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
Hoai-Hung Khong: Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City 70000, Vietnam
Chin-Feng Chang: Fukuta Electric and Machinery Co., Ltd., Taichung City 429, Taiwan

Mathematics, 2023, vol. 11, issue 5, 1-20

Abstract: An observer is a crucial part of the sensorless control of a permanent magnet synchronous motor (PMSM). An observer, based on mathematical equations, depends on information regarding several parameters of the controlled motor. If the motor is replaced, then we need to know the motor parameter values and reset the observer’s parameters. This article discusses an intelligent observer that can be used for several motors with different parameters. The proposed intelligent observer was developed using machine learning methods. This observer’s core algorithm is a modified Jordan neural network. It processes I α , I β , v α , and v β to produce Sin θ and Cos θ values. It is combined with a phase-locked loop function to generate position and speed feedback information. The offline learning process is carried out using data acquired from the simulations of PMSM motors. This study used five PMSMs with different parameters, three as the learning reference sources and two as testing sources. The proposed intelligent observer was successfully used to control motors with different parameters in both simulation and experimental hardware. The average error in position estimated for the simulation was 0.0078 p.u and the error was 0.0100 p.u for the experimental realization.

Keywords: intelligent observer; PMSM drive control; machine learning realization; modified Jordan neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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