Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO
In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
|
| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/3328719 |
| _version_ | 1811183572501921792 |
|---|---|
| author | Rongfu Luo Zenghui Wang Yanxia Sun |
| author_facet | Rongfu Luo Zenghui Wang Yanxia Sun |
| author_sort | Rongfu Luo |
| collection | DOAJ |
| description | In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances. |
| first_indexed | 2024-04-11T09:49:02Z |
| format | Article |
| id | doaj.art-d13e1051d0f040fd9b8c8d18c4d13139 |
| institution | Directory Open Access Journal |
| issn | 1687-5605 |
| language | English |
| last_indexed | 2024-04-11T09:49:02Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj.art-d13e1051d0f040fd9b8c8d18c4d131392022-12-22T04:30:53ZengHindawi LimitedModelling and Simulation in Engineering1687-56052022-01-01202210.1155/2022/3328719Optimized Luenberger Observer-Based PMSM Sensorless Control by PSORongfu Luo0Zenghui Wang1Yanxia Sun2Department of Electrical and Electronic Engineering ScienceDepartment of Electrical EngineeringDepartment of Electrical and Electronic Engineering ScienceIn the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances.http://dx.doi.org/10.1155/2022/3328719 |
| spellingShingle | Rongfu Luo Zenghui Wang Yanxia Sun Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO Modelling and Simulation in Engineering |
| title | Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO |
| title_full | Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO |
| title_fullStr | Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO |
| title_full_unstemmed | Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO |
| title_short | Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO |
| title_sort | optimized luenberger observer based pmsm sensorless control by pso |
| url | http://dx.doi.org/10.1155/2022/3328719 |
| work_keys_str_mv | AT rongfuluo optimizedluenbergerobserverbasedpmsmsensorlesscontrolbypso AT zenghuiwang optimizedluenbergerobserverbasedpmsmsensorlesscontrolbypso AT yanxiasun optimizedluenbergerobserverbasedpmsmsensorlesscontrolbypso |