Стаття

Отримано 03.09.2025

Доопрацьовано 12.11.2024

Прийнято 26.12.2024

Взято з Том 28, № 2, 2025

Сторінки 8 -26

Gorobchenko, O., & Zaika, D. (2025). Creation of a model of automated traction control of shunting locomotives by using artificial intelligence methods. The National Transport University Bulletin: A Scientific and Technical Journal, 28(2), 8-26. https://doi.org/10.32703/2617-9059-2024-44-1

Створення моделі автоматизованого управління тяговою передачею маневрових локомотивів шляхом використання методів штучного інтелекту

Олександр Горобченко Денис Заіка

В роботі розроблено математичну модель автоматизованої системи управління тяговою передачею  маневрового  локомотива,  використовуючи  методи  нечіткої  логіки  та  метод експертних оцінок. Для запропонованої моделі використовується алгоритм Мамдані. Алгоритм включає базу знань інтелектуальної системи, яка для формалізації та представлення знань в пам’яті використовує продукційну модель, поєднуючи елементи логічних та мережевих підходів керування.  Отримана  автоматизована  модель  управління  тяговою  передачею  маневрового локомотива пропонує свій оптимальний режим руху для конкретного поїзда та ділянки. Модель використовує створену нечітку базу даних. Результатом розрахунку моделі є керуючий сигнал для руху маневрового локомотива на 4-х двигунах, використовуючи частково 3-тю та повністю 4-ту та 5-ту позицію контролера машиніста. Такий режим руху дозволяє зменшити витрати палива на маневрову роботу локомотива при часткових навантаження на тягову електричну передачу

залізничний транспорт; рухомий склад; тепловоз; тягова електропередача; метод Мамдані
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  2. Gorobchenko, O., Holub, H., & Zaika, D. (2024). Theoretical basics of the self-learning system of intelligent locomotive decision support systems. Archives of Transport, 71(3), 169-186. https://doi.org/10.61089/aot2024.gaevsp41.
  3. Wang, H., Hao, L., Sharma, A., & Kukkar, A. (2022). Automatic control of computer application data processing system based on artificial intelligence. Journal of Intelligent Systems, 31(1), 177–192. https://doi.org/10.1515/jisys-2022-0007.
  4. Yin, J., Chen, D., & Li, Y. (2016). Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive. Knowledge-Based Systems, 92(C), 78–91. https://doi.org/10.1016/j.knosys.2015.10.016.
  5. Zhou, K., Song, S., Xue, A., You, K., & Wu, H. (2022). Smart train operation algorithms based on expert knowledge and reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 716–727. https://doi.org/10.1109/TSMC.2020.3000073.
  6. Liu, K. W., Wang, X. C., & Qu, Z. H. (2019). Research on multi-objective optimization and control algorithms for automatic train operation. Energies, 12(20), 3842. https://doi.org/10.3390/en12203842.
  7. Wu, Q., Spiryagin, M., & Cole, C. (2020). Train energy simulation with locomotive adhesion model. Railway Engineering Science, 28, 75-84. https://doi.org/10.1007/s40534-020-00202-1.
  8. Cao, Y., Ma, L., & Zhang, Y. (2018). Application of fuzzy predictive control technology in automatic train operation. Cluster Computing, 22, 14135–14144. https://doi.org/10.1007/s10586-018-2258-0.
  9. Gorobchenko, O., & Nevedrov, O. (2020). Development of the structure of an intelligent locomotive DSS and assessment of its effectiveness. Archives of Transport, 56(4), 47–58. https://doi.org/10.5604/01.3001.0014.5517.
  10. Shen, H., & Yan, J. (2017). Optimal control of rail transportation associated automatic train operation based on fuzzy control algorithm and PID algorithm. Automatic Control Computer Sciences, 51(6), 435–441. https://doi.org/10.3103/S0146411617060086.
  11. Zhang, L., Zhang, L., Yang, J., Gao, M., & Li, Y. (2021). Application research of fuzzy PID control optimized by genetic algorithm in medium and low-speed maglev train charger. IEEE Access, 9, 152131-152139. https://doi.org/10.1109/access.2021.3123727.
  12. Gorobchenko, O., & Zaika, D. (2024). Development of a mathematical model for determining traction and energy performance indicators of a maneuvering locomotive. Collection of Scientific Papers UkrSURT, (208), 146–162. https://doi.org/10.18664/1994-7852.208.2024.308485.
  13. Herpratiwi, H., Maftuh, M., Firdaus, W., Tohir, A., Daulay, M. I., & Rahim, R. (2022). Implementation and analysis of fuzzy Mamdani logic algorithm from digital platform and electronic resource. TEM Journal, 11(3), 1028-1033. https://doi.org/10.18421/TEM113-06.
  14. Kisliy, D. M., Desiak, A. Y., Bobyr, D. V., & Bodnar, E. B. (2023). Determination of energy-optimized locomotive control during train acceleration. Science and Transport Progress, 4(104), 25–38. https://doi.org/10.15802/stp2023/298713.
  15. Yin, M., Li, K., & Cheng, X. (2020). A review on artificial intelligence in high-speed rail. Transportation Safety and Environment, 2(4), 247–259. https://doi.org/10.1093/tse/tdaa022.
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  18. Aredah, A., Du, J., Hegazi, M., List, G., & Rakha, H. A. (2024). Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport. Applied Energy, 356. https://doi.org/10.1016/j.apenergy.2023.122411.
  19. Aredah, A., Fadhloun, K., & Rakha, H. A. (2024). Energy optimization in freight train operations: Algorithmic development and testing. Applied Energy, 364. https://doi.org/10.1016/j.apenergy.2024.123111.
  20. Aredah, A. S., Fadhloun, K., & Rakha, H. A. (2024). NeTrainSim: a network-level simulator for modeling freight train longitudinal motion and energy consumption. Railway Engineering Science, 1–19. https://doi.org/10.1007/s40534-024-00331.
  21. Jing, S. H. A. N. G., Yong, L. I. U., & Fan, J. I. A. N. G. (2023). Research and application of locomotive automatic operation technology. Electric Drive for Locomotives, 1, 1–12. https://doi.org/10.13890/j.issn.1000-128X.2023.01.001.
  22. Rodriguez, R., Trovão, J. P. F., & Solano, J. (2022). Fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive. Energy Conversion and Management, 253, 115111. https://doi.org/10.1016/j.enconman.2021.115111.
  23. Kacimi, M. A., Guenounou, O., Brikh, L., Yahiaoui, F., & Hadid, N. (2020). New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules. Engineering Applications of Artificial Intelligence, 89. https://doi.org/10.1016/j.engappai.2019.103417.
  24. Kaczorek, M., & Jacyna, M. (2022). Fuzzy logic as a decision-making support tool in planning transport development. Archives of Transport, 61(1), 51–70. https://doi.org/10.5604/01.3001.0015.8154.
  25. Ciani, L., Guidi, G., Patrizi, G., & Galar, D. (2021). Improving human reliability analysis for railway systems using fuzzy logic. IEEE Access, 9, 128648–128662. http://dx.doi.org/10.1109/ACCESS.2021.3112527.
  26. Butko, T., Babanin, A., & Gorobchenko, A. (2015). Rationale for the type of the membership function of fuzzy parameters of locomotive intelligent control systems. Eastern-European Journal of Enterprise Technologies, 1(3(73)), 4–8. https://doi.org/10.15587/1729-4061.2015.35996.
  27. Gorobchenko, O., & Zaika, D. (2022, February). Review of methods and prospects of using artificial intelligence in railway transport. Innovations and prospects of world science. In: The 6th International scientific and practical conference “Innovations and prospects of world science” (February 2-4, 2022) Perfect Publishing, Vancouver, Canada. 2022. 1072 p. (pp. 184-192). [in Ukrainian].
  28. Gorobchenko, O., Holub, H., & Zaika, D. (2024). Theoretical basics of the self-learning system of intelligent locomotive decision support systems. Archives of Transport, 71(3), 169-186. https://doi.org/10.61089/aot2024.gaevsp41.
  29. Wang, H., Hao, L., Sharma, A., & Kukkar, A. (2022). Automatic control of computer application data processing system based on artificial intelligence. Journal of Intelligent Systems, 31(1), 177–192. https://doi.org/10.1515/jisys-2022-0007.
  30. Yin, J., Chen, D., & Li, Y. (2016). Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive. Knowledge-Based Systems, 92(C), 78–91. https://doi.org/10.1016/j.knosys.2015.10.016.
  31. Zhou, K., Song, S., Xue, A., You, K., & Wu, H. (2022). Smart train operation algorithms based on expert knowledge and reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 716–727. https://doi.org/10.1109/TSMC.2020.3000073.
  32. Liu, K. W., Wang, X. C., & Qu, Z. H. (2019). Research on multi-objective optimization and control algorithms for automatic train operation. Energies, 12(20), 3842. https://doi.org/10.3390/en12203842.
  33. Wu, Q., Spiryagin, M., & Cole, C. (2020). Train energy simulation with locomotive adhesion model. Railway Engineering Science, 28, 75-84. https://doi.org/10.1007/s40534-020-00202-1.
  34. Cao, Y., Ma, L., & Zhang, Y. (2018). Application of fuzzy predictive control technology in automatic train operation. Cluster Computing, 22, 14135–14144. https://doi.org/10.1007/s10586-018-2258-0.
  35. Gorobchenko, O., & Nevedrov, O. (2020). Development of the structure of an intelligent locomotive DSS and assessment of its effectiveness. Archives of Transport, 56(4), 47–58. https://doi.org/10.5604/01.3001.0014.5517.
  36. Shen, H., & Yan, J. (2017). Optimal control of rail transportation associated automatic train operation based on fuzzy control algorithm and PID algorithm. Automatic Control Computer Sciences, 51(6), 435–441. https://doi.org/10.3103/S0146411617060086.
  37. Zhang, L., Zhang, L., Yang, J., Gao, M., & Li, Y. (2021). Application research of fuzzy PID control optimized by genetic algorithm in medium and low-speed maglev train charger. IEEE Access, 9, 152131-152139. https://doi.org/10.1109/access.2021.3123727.
  38. Gorobchenko, O., & Zaika, D. (2024). Development of a mathematical model for determining traction and energy performance indicators of a maneuvering locomotive. Collection of Scientific Papers UkrSURT, (208), 146–162. https://doi.org/10.18664/1994-7852.208.2024.308485.
  39. Herpratiwi, H., Maftuh, M., Firdaus, W., Tohir, A., Daulay, M. I., & Rahim, R. (2022). Implementation and analysis of fuzzy Mamdani logic algorithm from digital platform and electronic resource. TEM Journal, 11(3), 1028-1033. https://doi.org/10.18421/TEM113-06.
  40. Kisliy, D. M., Desiak, A. Y., Bobyr, D. V., & Bodnar, E. B. (2023). Determination of energy-optimized locomotive control during train acceleration. Science and Transport Progress, 4(104), 25–38. https://doi.org/10.15802/stp2023/298713.
  41. Yin, M., Li, K., & Cheng, X. (2020). A review on artificial intelligence in high-speed rail. Transportation Safety and Environment, 2(4), 247–259. https://doi.org/10.1093/tse/tdaa022.
  42. Plissonneau, A., Trentesaux, D., Ben-Messaoud, W., & Bekrar, A. (2021, May). AI-based speed control models for the autonomous train: a literature review. In 2021 Third International Conference on Transportation and Smart Technologies (TST) (pp. 9-15). IEEE. https://doi.org/10.1109/TST52996.2021.00009.
  43. Fernández, P. M., Sanchís, I. V., Yepes, V., & Franco, R. I. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production, 222, 153–162. https://doi.org/10.1016/j.jclepro.2019.03.037.
  44. Aredah, A., Du, J., Hegazi, M., List, G., & Rakha, H. A. (2024). Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport. Applied Energy, 356. https://doi.org/10.1016/j.apenergy.2023.122411.
  45. Aredah, A., Fadhloun, K., & Rakha, H. A. (2024). Energy optimization in freight train operations: Algorithmic development and testing. Applied Energy, 364. https://doi.org/10.1016/j.apenergy.2024.123111.
  46. Aredah, A. S., Fadhloun, K., & Rakha, H. A. (2024). NeTrainSim: a network-level simulator for modeling freight train longitudinal motion and energy consumption. Railway Engineering Science, 1–19. https://doi.org/10.1007/s40534-024-00331.
  47. Jing, S. H. A. N. G., Yong, L. I. U., & Fan, J. I. A. N. G. (2023). Research and application of locomotive automatic operation technology. Electric Drive for Locomotives, 1, 1–12. https://doi.org/10.13890/j.issn.1000-128X.2023.01.001.
  48. Rodriguez, R., Trovão, J. P. F., & Solano, J. (2022). Fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive. Energy Conversion and Management, 253, 115111. https://doi.org/10.1016/j.enconman.2021.115111.
  49. Kacimi, M. A., Guenounou, O., Brikh, L., Yahiaoui, F., & Hadid, N. (2020). New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules. Engineering Applications of Artificial Intelligence, 89. https://doi.org/10.1016/j.engappai.2019.103417.
  50. Kaczorek, M., & Jacyna, M. (2022). Fuzzy logic as a decision-making support tool in planning transport development. Archives of Transport, 61(1), 51–70. https://doi.org/10.5604/01.3001.0015.8154.
  51. Ciani, L., Guidi, G., Patrizi, G., & Galar, D. (2021). Improving human reliability analysis for railway systems using fuzzy logic. IEEE Access, 9, 128648–128662. http://dx.doi.org/10.1109/ACCESS.2021.3112527.
  52. Butko, T., Babanin, A., & Gorobchenko, A. (2015). Rationale for the type of the membership function of fuzzy parameters of locomotive intelligent control systems. Eastern-European Journal of Enterprise Technologies, 1(3(73)), 4–8. https://doi.org/10.15587/1729-4061.2015.35996.

https://doi.org/10.32703/2617-9059-2024-44-1