Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation
The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optima...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/20/3842 |
| _version_ | 1798038992188342272 |
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| author | Kai-wei Liu Xing-Cheng Wang Zhi-hui Qu |
| author_facet | Kai-wei Liu Xing-Cheng Wang Zhi-hui Qu |
| author_sort | Kai-wei Liu |
| collection | DOAJ |
| description | The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified. |
| first_indexed | 2024-04-11T21:47:47Z |
| format | Article |
| id | doaj.art-63320ee23b784e8084c6bfd0030c44a2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:47:47Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-63320ee23b784e8084c6bfd0030c44a22022-12-22T04:01:21ZengMDPI AGEnergies1996-10732019-10-011220384210.3390/en12203842en12203842Research on Multi-Objective Optimization and Control Algorithms for Automatic Train OperationKai-wei Liu0Xing-Cheng Wang1Zhi-hui Qu2School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, ChinaThe automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.https://www.mdpi.com/1996-1073/12/20/3842automatic train operationmulti-objective algorithmgapredictive controldirectional mutationgene modification |
| spellingShingle | Kai-wei Liu Xing-Cheng Wang Zhi-hui Qu Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation Energies automatic train operation multi-objective algorithm ga predictive control directional mutation gene modification |
| title | Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation |
| title_full | Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation |
| title_fullStr | Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation |
| title_full_unstemmed | Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation |
| title_short | Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation |
| title_sort | research on multi objective optimization and control algorithms for automatic train operation |
| topic | automatic train operation multi-objective algorithm ga predictive control directional mutation gene modification |
| url | https://www.mdpi.com/1996-1073/12/20/3842 |
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