Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems
Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of the Industrial Electronics Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10416358/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841526343080083456 |
|---|---|
| author | Zhitai Liu Xinghu Yu Weiyang Lin Juan J. Rodriguez-Andina |
| author_facet | Zhitai Liu Xinghu Yu Weiyang Lin Juan J. Rodriguez-Andina |
| author_sort | Zhitai Liu |
| collection | DOAJ |
| description | Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system. |
| format | Article |
| id | doaj-art-5259d96ba3bb492b98785ff1d185c212 |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-5259d96ba3bb492b98785ff1d185c2122025-01-17T00:00:41ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-015546610.1109/OJIES.2024.335995110416358Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven SystemsZhitai Liu0https://orcid.org/0000-0002-5533-0774Xinghu Yu1https://orcid.org/0000-0001-8181-6199Weiyang Lin2https://orcid.org/0000-0002-0493-1289Juan J. Rodriguez-Andina3https://orcid.org/0000-0002-0919-1793Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, ChinaNingbo Institute of Intelligent Equipment Technology Company, Ltd., Ningbo, ChinaResearch Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, ChinaSchool of Electrical and Information Engineering, Ningbo University of Technology, Ningbo, ChinaRepetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system.https://ieeexplore.ieee.org/document/10416358/Adaptive robust control (ARC)iterative learning observerlinear motor (LM)motion controlrepetitive motion task |
| spellingShingle | Zhitai Liu Xinghu Yu Weiyang Lin Juan J. Rodriguez-Andina Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems IEEE Open Journal of the Industrial Electronics Society Adaptive robust control (ARC) iterative learning observer linear motor (LM) motion control repetitive motion task |
| title | Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems |
| title_full | Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems |
| title_fullStr | Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems |
| title_full_unstemmed | Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems |
| title_short | Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems |
| title_sort | iterative learning observer based high precision motion control for repetitive motion tasks of linear motor driven systems |
| topic | Adaptive robust control (ARC) iterative learning observer linear motor (LM) motion control repetitive motion task |
| url | https://ieeexplore.ieee.org/document/10416358/ |
| work_keys_str_mv | AT zhitailiu iterativelearningobserverbasedhighprecisionmotioncontrolforrepetitivemotiontasksoflinearmotordrivensystems AT xinghuyu iterativelearningobserverbasedhighprecisionmotioncontrolforrepetitivemotiontasksoflinearmotordrivensystems AT weiyanglin iterativelearningobserverbasedhighprecisionmotioncontrolforrepetitivemotiontasksoflinearmotordrivensystems AT juanjrodriguezandina iterativelearningobserverbasedhighprecisionmotioncontrolforrepetitivemotiontasksoflinearmotordrivensystems |