Modeling operation control of chain conveyor drives using soft computing

The article discusses an approach to soft computing of operation control for chain conveyor drives in the coal mining industry. The proposed approach is based on the analysis of mechanization and automation practices in the coal industry in Vietnam. Within the framework of this approach, it is emphasized that it is important to expand the application range of mining machinery and, primarily, chain conveyors, to enhance efficiency of coal production. The article addresses the main constraints of the effective control over chain conveyor motor drives under conditions of various workloads at haulage points in coal mines, presents the chain conveyor control scheme and compares the existing optimization methods of smooth start of chain conveyor drives. The developed method of control is based on the fuzzy logic for the automated tension adjustment of two haulage chains of the conveyor. The velocity control mechanism of the permanent magnet synchronous motor (PMSM) uses multi-purpose optimization algorithms including a set of parameters of a proportional–integral–derivative controller (PID controller): particle array technique (PAT), optimized bacterial foraging (BF) and fuzzy logic (FL). These parameters ensure high quality of transient processes by the control options. The implemented comparative analysis of these mechanisms proves reliability of the research results.

Le Din Hieu, Agabubaev A. Modeling operation control of chain conveyor drives using soft computing. MIAB. Mining Inf. Anal. Bull. 2022;(3):130-142. [In Russ]. DOI: 10.25018/0236_1493_2022_3_0_130.

Le Din Hieu¹, Graduate Student, e-mail: hieuhuech@gmail.com,
A. Agabubaev¹, Senior Lecturer, e-mail: agabubaev.a@misis.ru,
¹ National University of Science and Technology «MISiS», 119049, Moscow, Russia.

A. Agabubaev, e-mail: agabubaev.a@misis.ru.

1. Le Dinh Hieu, Temkin I. O., Do Lich Thanh, Agabubaev A. Optimization of the control of the start-up modes of the scraper conveyor based on the analysis of simulation results. Caspian Journal: Management and High Technologies. 2020, no. 2 (50), pp. 10—21. DOI: 10.21672/20741707.2020.50.2.010-021.

2. Angeline P. Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. Proceedings of the 7th Annual Conference on Evolutionary Programming. 1998, pp. 601—610. DOI: 10.1007/BFb0040811.

3. Francesco G., Riccardo Z., Marco M., Davide C. Genetical swarm optimization of multihop routes in wireless sensor networks. Applied Computational Intelligence and Soft Computing. 2010, vol. 2010, pp. 1—14. DOI: 10.1155/2010/523943.

4. Chen J., Mohammad N. O., Morteza A., Xin Y. Knowledge-based particle swarm optimization for PID controller tuning. 2017 IEEE Congress on Evolutionary Computation (CEC). 2017, pp. 1819—1826. DOI: 10.1109/CEC.2017.7969522.

5. Das S., Biswas A., Dasgupta S., Abraham A. Bacterial foraging optimization algorithm: theoretical foundations, analysis and applications. Foundations of computational intelligence. 2009, vol. 3, pp. 23—55. DOI: 10.1007/978-3-642-01085-9_2.

6. Electric W. WEG Electric Innovation the W22 Magnet Drive System. Available at: https:// est-aegis.info/2018/03/weg-electric-innovation-w22-magnet-drive-system/, 2018.

7. Hieu L., Temkin I. Application of adaptive PSO and Adaptive fuzzy logic controllers to speed control PMSM motor servo systems. MATEC Web of Conferences. 2018, vol. 220, no. 5, article 08003. DOI: 10.1051/matecconf/201822008003.

8. Temkin I., Deryabin S., Konov I. Soft computing models in an intellectual open-pit mines transport control system. Procedia Computer Science. 2017, vol. 120, pp. 411—416. DOI: 10.1016/ j.procs.2017.11.257.

9. James K., Russell E. Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks. 1995, vol. 4, pp. 1942—1948. DOI: 10.1109/ICNN.1995.488968.

10. Majidian A., Saidi M. H. Comparison of fuzzy logic and neural network in life prediction of boiler tubes. International Journal of Fatigue. 2007, vol. 29, no. 3, pp. 489—498. DOI: 10.1016/j.ijfatigue.2006.05.001.

11. Polikarpova M., Lindh P., Tapia J., Pyrhönen J. Application of potting material for a 100 kW radial flux PMSM. 2014 International Conference on Electrical Machines (ICEM). 2014. pp. 2146—2151. DOI: 10.1109/ITEC.2017.7993349.

12. Sivanandam S., Deepa S. Genetic algorithms. Introduction to genetic algorithms. Springer, 2008, pp.15—37.

13. Temkin I., Deryabin S., Konov I., Kim M. Possible architecture and some neuro-fuzzy algorithms of an intelligent control system for open pit mines transport facilities. Frontiers in Artificial Intelligence and Applications. 2019, vol. 320, pp. 412—420. DOI: 10.3233/FAIA190205.

14. Ehsan V., Shahram M., Saeed T. Improved cuckoo search algorithm for feedforward neural network training. International Journal of Artificial Intelligence & Applications. 2011, vol. 2, no. 3, pp. 36—43. DOI: 10.5121/ijaia.2011.2304.

15. Vinacomin Mechanical products: information site. Available at: http://tapchicongthuong. vn/bai-viet/san-pham-co-khi-cua-vinacomin-34531.htm

16. Yao X., Liu Y., Lin G. Evolutionary programming made faster. IEEE Transactions on Evolutionary computation. 1999, vol. 3, no. 2, pp. 82—102. DOI: 10.1109/4235.771163.

17. Yang X.-S., Deb S. Cuckoo search via Lévy flights. 2009 World congress on nature & biologically inspired computing (NaBIC). 2009, pp. 210—214. DOI: 10.1109/NABIC.2009.5393690.

18. Yang X.-S., Deb S. Multiobjective cuckoo search for design optimization. Computers & Operations Research. 2013, vol. 40, no. 6, pp. 1616—1624. DOI: 10.1016/j.cor.2011.09.026.

19. Eshchin E. K. Modeling and control of the dynamic state of scraper conveyors. Vestnik Kuzbasskogo gosudarstvennogo tekhnicheskogo universiteta. 2015, no. 2 (108), pp. 118—122.

20. Eshchin E. K. Management of dynamic loading of downhole scraper conveyors. Journal of Mining Institute. 2019, vol. 239, pp. 570—575.

21. Kazanin O. I., Le Van Hau The state and prospects of development of underground coal mining technologies in the Quang Ninh basin of Vietnam. MIAB. Mining Inf. Anal. Bull. 2014, no. 5, pp. 15—20.

22. Kozhushko G. G., Lukashuk O. A. Raschet i proektirovanie lentochnykh konveyerov. Uchebno-metodicheskoe posobie [Calculation and design of belt conveyors. Educational and methodological manual], Ekaterinburg, Izd-vo Ural'skogo universiteta, 2016, 232 p. [In Russ].