Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification
Marine permanent magnet synchronous propulsion motors have problems, such as low reliability and difficult maintenance in the traditional control. In this paper, a sensorless control system for a permanent magnet synchronous motor (PMSM) based on parameter identification is proposed. According to th...
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Published in | Electronics (Basel) Vol. 11; no. 7; p. 1012 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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MDPI AG
01.04.2022
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Abstract | Marine permanent magnet synchronous propulsion motors have problems, such as low reliability and difficult maintenance in the traditional control. In this paper, a sensorless control system for a permanent magnet synchronous motor (PMSM) based on parameter identification is proposed. According to the mathematical model of the motor in the two-phase synchronous rotating coordinate system, a model reference adaptation system (MRAS) is used to estimate the rotor speed and rotor position of the motor. Because the MRAS is highly dependent on the motor parameters, and they will change with the environment, working state, etc., the Adaline neural network is used to identify the motor parameters online, and then the model parameters in the MRAS are corrected. The simulation results show that the combined control system can reduce the estimated error of the rotor speed by about 50% compared with the traditional method, and reduces the rotor position angle estimation error by 96%. It shows that the combined system can accurately estimate the rotational speed and rotor position of the motor, and it has high identification accuracy for the motor parameters. |
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AbstractList | Marine permanent magnet synchronous propulsion motors have problems, such as low reliability and difficult maintenance in the traditional control. In this paper, a sensorless control system for a permanent magnet synchronous motor (PMSM) based on parameter identification is proposed. According to the mathematical model of the motor in the two-phase synchronous rotating coordinate system, a model reference adaptation system (MRAS) is used to estimate the rotor speed and rotor position of the motor. Because the MRAS is highly dependent on the motor parameters, and they will change with the environment, working state, etc., the Adaline neural network is used to identify the motor parameters online, and then the model parameters in the MRAS are corrected. The simulation results show that the combined control system can reduce the estimated error of the rotor speed by about 50% compared with the traditional method, and reduces the rotor position angle estimation error by 96%. It shows that the combined system can accurately estimate the rotational speed and rotor position of the motor, and it has high identification accuracy for the motor parameters. |
Author | Zhang, Jundong Chen, Dong Huang, Yubo Qi, Jiahao |
Author_xml | – sequence: 1 givenname: Yubo orcidid: 0000-0002-4912-4691 surname: Huang fullname: Huang, Yubo – sequence: 2 givenname: Jundong surname: Zhang fullname: Zhang, Jundong – sequence: 3 givenname: Dong orcidid: 0000-0002-2568-3472 surname: Chen fullname: Chen, Dong – sequence: 4 givenname: Jiahao surname: Qi fullname: Qi, Jiahao |
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Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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SubjectTerms | Accuracy Adaptive control Algorithms Coordinates Design Mathematical models Methods Model reference adaptive control Neural networks Parameter identification Permanent magnets Rotor speed Simulation Synchronous motors Velocity |
Title | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
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