Research on speed control of high-speed trains based on hybrid modeling
With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Transport, Warsaw University of Technology
2023-06-01
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| Series: | Archives of Transport |
| Subjects: | |
| Online Access: | http://aot.publisherspanel.com/gicid/01.3001.0016.3132 |
| _version_ | 1797796757573206016 |
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| author | Tao Hou Li Tang Hongxia Niu Tingyang Zhao |
| author_facet | Tao Hou Li Tang Hongxia Niu Tingyang Zhao |
| author_sort | Tao Hou |
| collection | DOAJ |
| description | With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains. |
| first_indexed | 2024-03-13T03:37:52Z |
| format | Article |
| id | doaj.art-d05a89a9f138498f91797d03b47ff84c |
| institution | Directory Open Access Journal |
| issn | 0866-9546 2300-8830 |
| language | English |
| last_indexed | 2024-03-13T03:37:52Z |
| publishDate | 2023-06-01 |
| publisher | Faculty of Transport, Warsaw University of Technology |
| record_format | Article |
| series | Archives of Transport |
| spelling | doaj.art-d05a89a9f138498f91797d03b47ff84c2023-06-23T15:01:23ZengFaculty of Transport, Warsaw University of TechnologyArchives of Transport0866-95462300-88302023-06-01662778710.5604/01.3001.0016.313201.3001.0016.3132Research on speed control of high-speed trains based on hybrid modelingTao Hou0Li Tang1Hongxia Niu2Tingyang Zhao3School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaWith the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains.http://aot.publisherspanel.com/gicid/01.3001.0016.3132high-speed trainhybrid modelingspeed controlerror compensation |
| spellingShingle | Tao Hou Li Tang Hongxia Niu Tingyang Zhao Research on speed control of high-speed trains based on hybrid modeling Archives of Transport high-speed train hybrid modeling speed control error compensation |
| title | Research on speed control of high-speed trains based on hybrid modeling |
| title_full | Research on speed control of high-speed trains based on hybrid modeling |
| title_fullStr | Research on speed control of high-speed trains based on hybrid modeling |
| title_full_unstemmed | Research on speed control of high-speed trains based on hybrid modeling |
| title_short | Research on speed control of high-speed trains based on hybrid modeling |
| title_sort | research on speed control of high speed trains based on hybrid modeling |
| topic | high-speed train hybrid modeling speed control error compensation |
| url | http://aot.publisherspanel.com/gicid/01.3001.0016.3132 |
| work_keys_str_mv | AT taohou researchonspeedcontrolofhighspeedtrainsbasedonhybridmodeling AT litang researchonspeedcontrolofhighspeedtrainsbasedonhybridmodeling AT hongxianiu researchonspeedcontrolofhighspeedtrainsbasedonhybridmodeling AT tingyangzhao researchonspeedcontrolofhighspeedtrainsbasedonhybridmodeling |