Research on the Mechanical Parameter Identification and Controller Performance of Permanent Magnet Motors Based on Sensorless Control

In order to improve the control performance of the position sensorless control system of permanent magnet synchronous motors and to reduce the influence of external uncertainties on the control system, such as inertia ingestion and load disturbance, this paper proposes a novel position sensorless control algorithm for permanent magnet synchronous motors based on an interleaved parallel extended sliding mode observer. Firstly, in order to identify the time-varying moment of inertia, load torque and viscous friction coefficient of the system, a novel interleaved parallel extended sliding mode observer based on a single-observer model is proposed, and a robust activator is designed to reduce the coupling between the parameters to be measured. Then, a new predefined-time sliding mode controller is designed for the face-mounted permanent magnet synchronous motor using sliding film control theory, which improves the response speed and control accuracy of the system. Then, the proposed novel interleaved parallel extended sliding mode observer and predefined-time sliding mode controller are used to design the permanent magnet synchronous motor control system, and the stability of the system is proved using the Lyapunov stability theorem. Finally, through simulation analysis and experimental tests, it is verified that the control strategy proposed in this paper can improve the identification accuracy of the motor parameters, reduce the time of identification, and improve the control accuracy and tracking speed.

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Publication Details

Journal
Actuators
Published
2024-12-19
DOI
https://doi.org/10.3390/act13120525
Citations
2
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.42

Funders

Controls
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article

Research on the Mechanical Parameter Identification and Controller Performance of Permanent Magnet Motors Based on Sensorless Control

Yangxiao Guo, Yun Zhang, Long Wang, Hyun Min et al.
2 citations
Actuators
Sensorless Control of Electric Motors
0.42
article

Research on the Mechanical Parameter Identification and Controller Performance of Permanent Magnet Motors Based on Sensorless Control

Yangxiao Guo, Yun Zhang, Long Wang, Hyun Min, Mingchen Luan, Jiuhong Ruan
article en
2 citations

Abstract

In order to improve the control performance of the position sensorless control system of permanent magnet synchronous motors and to reduce the influence of external uncertainties on the control system, such as inertia ingestion and load disturbance, this paper proposes a novel position sensorless control algorithm for permanent magnet synchronous motors based on an interleaved parallel extended sliding mode observer. Firstly, in order to identify the time-varying moment of inertia, load torque and viscous friction coefficient of the system, a novel interleaved parallel extended sliding mode observer based on a single-observer model is proposed, and a robust activator is designed to reduce the coupling between the parameters to be measured. Then, a new predefined-time sliding mode controller is designed for the face-mounted permanent magnet synchronous motor using sliding film control theory, which improves the response speed and control accuracy of the system. Then, the proposed novel interleaved parallel extended sliding mode observer and predefined-time sliding mode controller are used to design the permanent magnet synchronous motor control system, and the stability of the system is proved using the Lyapunov stability theorem. Finally, through simulation analysis and experimental tests, it is verified that the control strategy proposed in this paper can improve the identification accuracy of the motor parameters, reduce the time of identification, and improve the control accuracy and tracking speed.

ActuatorsVol. 13(12)
Shandong Jiaotong University (CN), Shandong Transportation Research Institute (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 36%
Sensorless Control of Electric Motors
0.42
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