Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method
This paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as a higher-order integrator subsystem with fully considering its inherent nonlinear and time-varying ch...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8790691/ |
| _version_ | 1818642834861850624 |
|---|---|
| author | Rui Yang Li-Yi Li Ming-Yi Wang Cheng-Ming Zhang Yi-Ming Zeng-Gu |
| author_facet | Rui Yang Li-Yi Li Ming-Yi Wang Cheng-Ming Zhang Yi-Ming Zeng-Gu |
| author_sort | Rui Yang |
| collection | DOAJ |
| description | This paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as a higher-order integrator subsystem with fully considering its inherent nonlinear and time-varying characteristic. The motion system dynamics is then extended with modeling the force ripple as an extra state. The main idea for the accurate force ripple estimation is to construct an incremental extended state modeling-based Kalman filter (IESM-KF) for reducing the calculation cost as the higher-order dynamics of the force ripple is considered. And also, a simple and practical parameter tuning method for the IESM-KF is proposed with injecting a square-wave current disturbance to the position controller's output under the cascade position-current closed loop. The inevitable time delay of the mechanical system is estimated with the sine-sweep-based model identification and is further considered in the IESM-KF design. Detailed experimental results are given to validate the effectiveness of the force ripple compensation with the IESM-KF and the corresponding parameter tuning method. |
| first_indexed | 2024-12-16T23:49:22Z |
| format | Article |
| id | doaj.art-66f632eb1d484efaa80e1c9fa5049851 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:49:22Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66f632eb1d484efaa80e1c9fa50498512022-12-21T22:11:23ZengIEEEIEEE Access2169-35362019-01-01710833110834210.1109/ACCESS.2019.29336278790691Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning MethodRui Yang0Li-Yi Li1https://orcid.org/0000-0002-0062-1742Ming-Yi Wang2https://orcid.org/0000-0002-6562-4530Cheng-Ming Zhang3https://orcid.org/0000-0003-4920-9481Yi-Ming Zeng-Gu4Department of Electrical Engineering, Harbin Institute of Technology, Harbin, ChinaDepartment of Electrical Engineering, Harbin Institute of Technology, Harbin, ChinaDepartment of Electrical Engineering, Harbin Institute of Technology, Harbin, ChinaDepartment of Electrical Engineering, Harbin Institute of Technology, Harbin, ChinaDepartment of Electrical Engineering, Harbin Institute of Technology, Harbin, ChinaThis paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as a higher-order integrator subsystem with fully considering its inherent nonlinear and time-varying characteristic. The motion system dynamics is then extended with modeling the force ripple as an extra state. The main idea for the accurate force ripple estimation is to construct an incremental extended state modeling-based Kalman filter (IESM-KF) for reducing the calculation cost as the higher-order dynamics of the force ripple is considered. And also, a simple and practical parameter tuning method for the IESM-KF is proposed with injecting a square-wave current disturbance to the position controller's output under the cascade position-current closed loop. The inevitable time delay of the mechanical system is estimated with the sine-sweep-based model identification and is further considered in the IESM-KF design. Detailed experimental results are given to validate the effectiveness of the force ripple compensation with the IESM-KF and the corresponding parameter tuning method.https://ieeexplore.ieee.org/document/8790691/Extended stateforce rippleKalman filterlinear motormodel identificationparameter tuning |
| spellingShingle | Rui Yang Li-Yi Li Ming-Yi Wang Cheng-Ming Zhang Yi-Ming Zeng-Gu Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method IEEE Access Extended state force ripple Kalman filter linear motor model identification parameter tuning |
| title | Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method |
| title_full | Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method |
| title_fullStr | Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method |
| title_full_unstemmed | Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method |
| title_short | Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method |
| title_sort | force ripple estimation and compensation of pmlsm with incremental extended state modeling based kalman filter a practical tuning method |
| topic | Extended state force ripple Kalman filter linear motor model identification parameter tuning |
| url | https://ieeexplore.ieee.org/document/8790691/ |
| work_keys_str_mv | AT ruiyang forcerippleestimationandcompensationofpmlsmwithincrementalextendedstatemodelingbasedkalmanfilterapracticaltuningmethod AT liyili forcerippleestimationandcompensationofpmlsmwithincrementalextendedstatemodelingbasedkalmanfilterapracticaltuningmethod AT mingyiwang forcerippleestimationandcompensationofpmlsmwithincrementalextendedstatemodelingbasedkalmanfilterapracticaltuningmethod AT chengmingzhang forcerippleestimationandcompensationofpmlsmwithincrementalextendedstatemodelingbasedkalmanfilterapracticaltuningmethod AT yimingzenggu forcerippleestimationandcompensationofpmlsmwithincrementalextendedstatemodelingbasedkalmanfilterapracticaltuningmethod |