Performance‐enhanced iterative learning control using a model‐free disturbance observer
Abstract This paper proposes a novel performance‐enhanced iterative learning control (ILC) scheme using a model‐free disturbance observer (DOB) to achieve high performance for precision motion systems that encounter non‐repetitive disturbances. As is well known, the performance of the standard ILC (...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-04-01
|
| Series: | IET Control Theory & Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cth2.12096 |
| _version_ | 1818041143852531712 |
|---|---|
| author | Min Li Tingjian Yan Caohui Mao Long Wen Xinxin Zhang Tao Huang |
| author_facet | Min Li Tingjian Yan Caohui Mao Long Wen Xinxin Zhang Tao Huang |
| author_sort | Min Li |
| collection | DOAJ |
| description | Abstract This paper proposes a novel performance‐enhanced iterative learning control (ILC) scheme using a model‐free disturbance observer (DOB) to achieve high performance for precision motion systems that encounter non‐repetitive disturbances. As is well known, the performance of the standard ILC (SILC) is severely degraded by the non‐repetitive disturbances. By introducing DOB into SILC, this paper improves the robustness of the ILC system against non‐repetitive disturbances. In the proposed enhanced ILC (EILC), SILC aims at learning the feedforward signals for a specific reference, while DOB is to compensate for external disturbances. Little or no plant model knowledge is required for SILC. To maintain this advantage after introducing DOB, a model‐free design method for DOB is proposed to release the need for the plant model. Based only on a specific reference and the corresponding feedforward signals learned by SILC, the filter of DOB is optimized via an instrumental‐variable estimate method. Numerical simulation is performed to illustrate the effectiveness and enhanced performance of the proposed control approach. |
| first_indexed | 2024-12-10T08:25:45Z |
| format | Article |
| id | doaj.art-7d12e1831ae7401885c65240c2a37589 |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-12-10T08:25:45Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-7d12e1831ae7401885c65240c2a375892022-12-22T01:56:14ZengWileyIET Control Theory & Applications1751-86441751-86522021-04-0115797898810.1049/cth2.12096Performance‐enhanced iterative learning control using a model‐free disturbance observerMin Li0Tingjian Yan1Caohui Mao2Long Wen3Xinxin Zhang4Tao Huang5School of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan ChinaSchool of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan ChinaSchool of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan ChinaSchool of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan ChinaSchool of Mechanical Engineering and Electronic Information China University of Geosciences (Wuhan) Wuhan ChinaState Key Laboratory of Mechanical Transmissions College of Mechanical Engineering Chongqing University Chongqing ChinaAbstract This paper proposes a novel performance‐enhanced iterative learning control (ILC) scheme using a model‐free disturbance observer (DOB) to achieve high performance for precision motion systems that encounter non‐repetitive disturbances. As is well known, the performance of the standard ILC (SILC) is severely degraded by the non‐repetitive disturbances. By introducing DOB into SILC, this paper improves the robustness of the ILC system against non‐repetitive disturbances. In the proposed enhanced ILC (EILC), SILC aims at learning the feedforward signals for a specific reference, while DOB is to compensate for external disturbances. Little or no plant model knowledge is required for SILC. To maintain this advantage after introducing DOB, a model‐free design method for DOB is proposed to release the need for the plant model. Based only on a specific reference and the corresponding feedforward signals learned by SILC, the filter of DOB is optimized via an instrumental‐variable estimate method. Numerical simulation is performed to illustrate the effectiveness and enhanced performance of the proposed control approach.https://doi.org/10.1049/cth2.12096Simulation, modelling and identificationSpatial variables controlInterpolation and function approximation (numerical analysis)Control system analysis and synthesis methodsStability in control theorySelf-adjusting control systems |
| spellingShingle | Min Li Tingjian Yan Caohui Mao Long Wen Xinxin Zhang Tao Huang Performance‐enhanced iterative learning control using a model‐free disturbance observer IET Control Theory & Applications Simulation, modelling and identification Spatial variables control Interpolation and function approximation (numerical analysis) Control system analysis and synthesis methods Stability in control theory Self-adjusting control systems |
| title | Performance‐enhanced iterative learning control using a model‐free disturbance observer |
| title_full | Performance‐enhanced iterative learning control using a model‐free disturbance observer |
| title_fullStr | Performance‐enhanced iterative learning control using a model‐free disturbance observer |
| title_full_unstemmed | Performance‐enhanced iterative learning control using a model‐free disturbance observer |
| title_short | Performance‐enhanced iterative learning control using a model‐free disturbance observer |
| title_sort | performance enhanced iterative learning control using a model free disturbance observer |
| topic | Simulation, modelling and identification Spatial variables control Interpolation and function approximation (numerical analysis) Control system analysis and synthesis methods Stability in control theory Self-adjusting control systems |
| url | https://doi.org/10.1049/cth2.12096 |
| work_keys_str_mv | AT minli performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver AT tingjianyan performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver AT caohuimao performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver AT longwen performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver AT xinxinzhang performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver AT taohuang performanceenhancediterativelearningcontrolusingamodelfreedisturbanceobserver |