Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains
Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train’s uncertain dynamics and actuator saturation. To address this issue...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9446079/ |
| _version_ | 1818649916417769472 |
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| author | Bin Chen Zhiwu Huang Rui Zhang Weirong Liu Heng Li Jing Wang Yunsheng Fan Jun Peng |
| author_facet | Bin Chen Zhiwu Huang Rui Zhang Weirong Liu Heng Li Jing Wang Yunsheng Fan Jun Peng |
| author_sort | Bin Chen |
| collection | DOAJ |
| description | Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train’s uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach. |
| first_indexed | 2024-12-17T01:41:55Z |
| format | Article |
| id | doaj.art-2a2e3ea5fb3b417bbd7a0a64b8d597ef |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T01:41:55Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2a2e3ea5fb3b417bbd7a0a64b8d597ef2022-12-21T22:08:18ZengIEEEIEEE Access2169-35362021-01-019822338224810.1109/ACCESS.2021.30861119446079Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed TrainsBin Chen0https://orcid.org/0000-0001-8485-1571Zhiwu Huang1https://orcid.org/0000-0002-5485-2562Rui Zhang2Weirong Liu3https://orcid.org/0000-0002-6207-9100Heng Li4https://orcid.org/0000-0001-5592-7004Jing Wang5https://orcid.org/0000-0002-0114-1513Yunsheng Fan6Jun Peng7https://orcid.org/0000-0001-6269-6929School of Automation, Central South University, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Electrical and Computer Engineering, Bradley University, Peoria, IL, USASchool of Automation, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaAutomatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train’s uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach.https://ieeexplore.ieee.org/document/9446079/Automatic train operationmodel predictive controlKoopman operatordata-driven model |
| spellingShingle | Bin Chen Zhiwu Huang Rui Zhang Weirong Liu Heng Li Jing Wang Yunsheng Fan Jun Peng Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains IEEE Access Automatic train operation model predictive control Koopman operator data-driven model |
| title | Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains |
| title_full | Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains |
| title_fullStr | Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains |
| title_full_unstemmed | Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains |
| title_short | Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains |
| title_sort | data driven koopman model predictive control for optimal operation of high speed trains |
| topic | Automatic train operation model predictive control Koopman operator data-driven model |
| url | https://ieeexplore.ieee.org/document/9446079/ |
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