Article

Received 03.09.2025

Revised 12.11.2024

Accepted 26.12.2024

Retrieved from Vol. 28, No. 2, 2025

Pages 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

Creation of a model of automated traction control of shunting locomotives by using artificial intelligence methods

Oleksandr Gorobchenko Denys Zaika

In  the  paper,  a  mathematical  model  of  the  automated  traction  transmission  control  system  of  the shunting locomotive was developed, using methods of fuzzy logic and the method of expert evaluations. The Mamdani algorithm was used for the proposed model. The algorithm includes the knowledge base of  an  intelligent  system,  which  uses  a  production  model  to  formalize  and  represent  knowledge  in memory, combining elements of logical and network management approaches. The resulting automated traction  transmission  control  model  of the  shunting  locomotive  offers  its  optimal  driving  mode for  a specific train and section. The model uses the generated fuzzy knowledge base. The result of the model calculation is a control signal for the movement of the shunting locomotive on 4 motors, using partially the 3rd and fully the 4th and 5th positions of the driver's controller. This mode of movement allows to reduce  fuel  consumption  for  shunting  of  the  locomotive  with  partial  loads  on  the  traction  electric transmission

railway transport; rolling stock; diesel locomotive; traction power transmission; Mamdani method
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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.
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  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.
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  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.
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  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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  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