Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach
This study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). The IBM-Dy-SGA effectively balances exploration and exploitat...
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Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002893 |
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| author | Dong Zhan Ai-Qing Tian Shao-Quan Ni |
| author_facet | Dong Zhan Ai-Qing Tian Shao-Quan Ni |
| author_sort | Dong Zhan |
| collection | DOAJ |
| description | This study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). The IBM-Dy-SGA effectively balances exploration and exploitation through a dynamic multi-swarm mechanism, combined with Latin Hypercube Sampling (LHS) initialization and K-means clustering strategies, thereby enhancing population diversity and convergence efficiency. Experimental validation demonstrates that, compared to the Snow Goose Algorithm (SGA), the IBM-Dy-SGA achieves a 10%–30% reduction in average fitness values and a 20% improvement in convergence speed. The IBM-Dy-SGA was further compared with seven other state-of-the-art algorithms on the CEC2017 benchmark test suite in both 10D and 30D, showcasing its significant advantages and validating these results through non-parametric tests. In the context of application for the control of MMAHSR-IPDCS, the IBM-Dy-SGA outperforms traditional methods in optimizing PID parameters, thereby significantly enhancing the precision and stability of platform door control systems. Specifically, the IBM-Dy-SGA reduces overshoot to 0% and shortens the settling time to 1.05 s, representing a 66.7% improvement over traditional methods. The practical application of the IBM-Dy-SGA at Zigong Station successfully resolved platform door alignment issues under varying train models and parking error conditions, demonstrating its effectiveness and reliability in complex and dynamic high-speed rail operations. For instance, in tests involving CRH380A, CRH380AL, CRH380AN, and CRH380D train models, the alignment rate of platform doors with train doors reached 100%, while for the CRH3A model, the alignment rate was 94.36%. This study not only enriches the theoretical application of metaheuristic algorithms in control systems but also provides a practical optimization method for high-speed rail platform door control, which is of great significance for enhancing the safety, efficiency, and reliability of high-speed rail operations. |
| format | Article |
| id | doaj-art-2bc86488c0e649efb2a779ad4333cd5a |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-2bc86488c0e649efb2a779ad4333cd5a2025-08-20T02:08:00ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-08-0116911073810.1016/j.ijepes.2025.110738Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approachDong Zhan0Ai-Qing Tian1Shao-Quan Ni2School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, China; Corresponding author.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, ChinaThis study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). The IBM-Dy-SGA effectively balances exploration and exploitation through a dynamic multi-swarm mechanism, combined with Latin Hypercube Sampling (LHS) initialization and K-means clustering strategies, thereby enhancing population diversity and convergence efficiency. Experimental validation demonstrates that, compared to the Snow Goose Algorithm (SGA), the IBM-Dy-SGA achieves a 10%–30% reduction in average fitness values and a 20% improvement in convergence speed. The IBM-Dy-SGA was further compared with seven other state-of-the-art algorithms on the CEC2017 benchmark test suite in both 10D and 30D, showcasing its significant advantages and validating these results through non-parametric tests. In the context of application for the control of MMAHSR-IPDCS, the IBM-Dy-SGA outperforms traditional methods in optimizing PID parameters, thereby significantly enhancing the precision and stability of platform door control systems. Specifically, the IBM-Dy-SGA reduces overshoot to 0% and shortens the settling time to 1.05 s, representing a 66.7% improvement over traditional methods. The practical application of the IBM-Dy-SGA at Zigong Station successfully resolved platform door alignment issues under varying train models and parking error conditions, demonstrating its effectiveness and reliability in complex and dynamic high-speed rail operations. For instance, in tests involving CRH380A, CRH380AL, CRH380AN, and CRH380D train models, the alignment rate of platform doors with train doors reached 100%, while for the CRH3A model, the alignment rate was 94.36%. This study not only enriches the theoretical application of metaheuristic algorithms in control systems but also provides a practical optimization method for high-speed rail platform door control, which is of great significance for enhancing the safety, efficiency, and reliability of high-speed rail operations.http://www.sciencedirect.com/science/article/pii/S0142061525002893MMAHSR-PDCSLatin hypercube samplingIBM-Dy-SGACEC2017 benchmark test suiteNon-parametric tests |
| spellingShingle | Dong Zhan Ai-Qing Tian Shao-Quan Ni Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach International Journal of Electrical Power & Energy Systems MMAHSR-PDCS Latin hypercube sampling IBM-Dy-SGA CEC2017 benchmark test suite Non-parametric tests |
| title | Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach |
| title_full | Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach |
| title_fullStr | Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach |
| title_full_unstemmed | Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach |
| title_short | Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach |
| title_sort | optimizing pid control for multi model adaptive high speed rail platform door systems with an improved metaheuristic approach |
| topic | MMAHSR-PDCS Latin hypercube sampling IBM-Dy-SGA CEC2017 benchmark test suite Non-parametric tests |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002893 |
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