Weighted Multimodel Predictive Function Control for Automatic Train Operation System
Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complic...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/520627 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562629546082304 |
|---|---|
| author | Shuhuan Wen Jingwei Yang Ahmad B. Rad Shengyong Chen Pengcheng Hao |
| author_facet | Shuhuan Wen Jingwei Yang Ahmad B. Rad Shengyong Chen Pengcheng Hao |
| author_sort | Shuhuan Wen |
| collection | DOAJ |
| description | Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-a9751387ce6f4836864695fb9c6afdd1 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-a9751387ce6f4836864695fb9c6afdd12025-02-03T01:22:11ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/520627520627Weighted Multimodel Predictive Function Control for Automatic Train Operation SystemShuhuan Wen0Jingwei Yang1Ahmad B. Rad2Shengyong Chen3Pengcheng Hao4Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, ChinaKey Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, ChinaSchool of Engineering Science, Simon Fraser University, 250-13450, 102 Avenue, Surrey, BC, V3T 0A3, CanadaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaKey Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, ChinaTrain operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.http://dx.doi.org/10.1155/2014/520627 |
| spellingShingle | Shuhuan Wen Jingwei Yang Ahmad B. Rad Shengyong Chen Pengcheng Hao Weighted Multimodel Predictive Function Control for Automatic Train Operation System Journal of Applied Mathematics |
| title | Weighted Multimodel Predictive Function Control for Automatic Train Operation System |
| title_full | Weighted Multimodel Predictive Function Control for Automatic Train Operation System |
| title_fullStr | Weighted Multimodel Predictive Function Control for Automatic Train Operation System |
| title_full_unstemmed | Weighted Multimodel Predictive Function Control for Automatic Train Operation System |
| title_short | Weighted Multimodel Predictive Function Control for Automatic Train Operation System |
| title_sort | weighted multimodel predictive function control for automatic train operation system |
| url | http://dx.doi.org/10.1155/2014/520627 |
| work_keys_str_mv | AT shuhuanwen weightedmultimodelpredictivefunctioncontrolforautomatictrainoperationsystem AT jingweiyang weightedmultimodelpredictivefunctioncontrolforautomatictrainoperationsystem AT ahmadbrad weightedmultimodelpredictivefunctioncontrolforautomatictrainoperationsystem AT shengyongchen weightedmultimodelpredictivefunctioncontrolforautomatictrainoperationsystem AT pengchenghao weightedmultimodelpredictivefunctioncontrolforautomatictrainoperationsystem |