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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/7/1012 |
| _version_ | 1797439867591852032 |
|---|---|
| author | Yubo Huang Jundong Zhang Dong Chen Jiahao Qi |
| author_facet | Yubo Huang Jundong Zhang Dong Chen Jiahao Qi |
| author_sort | Yubo Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:58:56Z |
| format | Article |
| id | doaj.art-bb73d34c8b824c0dbeb21837cec51f68 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T11:58:56Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-bb73d34c8b824c0dbeb21837cec51f682023-11-30T23:06:09ZengMDPI AGElectronics2079-92922022-03-01117101210.3390/electronics11071012Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter IdentificationYubo Huang0Jundong Zhang1Dong Chen2Jiahao Qi3College of Marine Engineering, Dalian Maritime University, Dalian 116026, ChinaCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, ChinaCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, ChinaCollege of Marine Engineering, Dalian Maritime University, Dalian 116026, ChinaMarine 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.https://www.mdpi.com/2079-9292/11/7/1012permanent magnet synchronous motorsensorless controlmodel reference adaptationparameter identificationAdaline neural network |
| spellingShingle | Yubo Huang Jundong Zhang Dong Chen Jiahao Qi Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification Electronics permanent magnet synchronous motor sensorless control model reference adaptation parameter identification Adaline neural network |
| title | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
| title_full | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
| title_fullStr | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
| title_full_unstemmed | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
| title_short | Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification |
| title_sort | model reference adaptive control of marine permanent magnet propulsion motor based on parameter identification |
| topic | permanent magnet synchronous motor sensorless control model reference adaptation parameter identification Adaline neural network |
| url | https://www.mdpi.com/2079-9292/11/7/1012 |
| work_keys_str_mv | AT yubohuang modelreferenceadaptivecontrolofmarinepermanentmagnetpropulsionmotorbasedonparameteridentification AT jundongzhang modelreferenceadaptivecontrolofmarinepermanentmagnetpropulsionmotorbasedonparameteridentification AT dongchen modelreferenceadaptivecontrolofmarinepermanentmagnetpropulsionmotorbasedonparameteridentification AT jiahaoqi modelreferenceadaptivecontrolofmarinepermanentmagnetpropulsionmotorbasedonparameteridentification |