Synthesis of regulators of the belt conveyor speed control system

The paper deals with the problem of synthesis of a conveyor belt speed control system in order to minimize the fluctuations of the belt sections during the start of the conveyor. To calculate the coefficients of the control system regulators, it is necessary to obtain the transfer function of the control object. Various ways of constructing a mathematical model of the conveyor are considered. The construction of a mathematical model of the conveyor as a system with concentrated parameters using computer modeling tools makes it possible to obtain transfer functions of the output parameters of the conveyor. When the conveyor is divided into five concentrated masses, the transfer functions have up to the eighth order of the Laplace operator in the denominator, which complicates the synthesis of the control system. To simplify the transfer function to the second order, various ways of simplification are considered, the authors propose to use the method of reducing closely spaced zeros and poles. This makes it possible to simplify the transfer function to the second order without using a complex mathematical apparatus, which allows using classical methods in the synthesis of the control system. The study of starting the conveyor without a control system and with a control system has shown the effectiveness of this method of simplifying transfer functions. The use of the control system made it possible to reduce the vibrations of the tape from 11% to 4% and reduce the transition time from 30.2 seconds to 21.6.

Keywords: conveyor belt, mathematical modeling Kelvin-Feucht model, traction factor, belt tension simplification of the transfer function, drive drum, concentrated parameters, PID controller, SimInTech.
For citation:

Kotin D. A., Sukhinin S. Е., Ivanov I. A. Synthesis of regulators of the belt conveyor speed control system. MIAB. Mining Inf. Anal. Bull. 2023;(10-1):5—21. [In Russ]. DOI: 10.25018/ 0236_1493_2023_101_0_5.

Issue number: 10
Year: 2023
Page number: 5-21
ISBN: 0236-1493
UDK: 621.337.4:622.647.2
DOI: 10.25018/0236_1493_2023_101_0_5
Article receipt date: 18.04.2023
Date of review receipt: 04.07.2023
Date of the editorial board′s decision on the article′s publishing: 10.10.2023
About authors:

Kotin D. A., Cand. Sci. (Eng.), Associate Professor,, Novosibirsk state technical university, 630073, Novosibirsk, Russia, e-mail: d.kotin@corp.;
Sukhinin S. E., postgraduate student,, Novosibirsk state technical university, 630073, Novosibirsk, Russia, e-mail:;
Ivanov I. A., postgraduate student, assistant,, Novosibirsk state technical university, 630073, Novosibirsk, Russia, e-mail: i.a.ivanov@


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1. Yao Y., Zhang B. Influence of the elastic modulus of a conveyor belt on the power allocation of multi-drive conveyors. PLoS One. 2020, vol. 15, no. 7, p. e0235768. DOI: 10.1371/journal.pone.0235768.

2. Bogomolov A. V., Belostockij V. A., Luk’janov I. M. Traction capacity of the drive drums of the belt elevators. Visnik SevNTU. 2013, no. 137, pp. 303–307. [In Russ].

3. Tarasenko E. A., Shushkov A. S. Research of lining materials of a drive drum of a belt conveyor. Nedelja nauki SPbPU. 2020, pp. 143–146. [In Russ].

4. Reutov A. A. Modeling of stationary modes of operation of multiblock drives of belt conveyors. Sovremennye tehnologii. Sistemnyj analiz. Modelirovanie. 2019, no. 2 (62), pp. 40–47. [In Russ].

5. Zhou Q., Gong H., Du G., Zhang Y., He H. Distributed Permanent Magnet DirectDrive Belt Conveyor SystemandIts Control Strategy. Energies. 2022, vol. 15, no. 22, p. 8699. DOI: 10.3390/en15228699.

6. Zeng F., Yan C., Wu Q., Wang T. Dynamic behaviour of a conveyor belt considering non-uniform bulk material distribution for speed control. Applied Sciences. 2020, vol. 10, no. 13, p. 4436. DOI: 10.3390/app10134436.

7. Yan C., Zeng F., Li Z. Belt Conveyor Speed Control Method Considering Elastic Constraint. 2019 9th International Conference on Education and Social Science (ICESS 2019). 2019, pp. 1168–1172. DOI: 10.25236/icess.2019.222.

8. Dmitrieva V. V., Sizin P. E. The analysis of belt conveyor models at different number of approximating masses. MIAB. Mining Inf. Anal. Bull. 2022, no. 1, pp. 34–46. [In Russ]. DOI: 10.25018/0236_1493_2022_1_0_34.

9. Kotin D. A., Sukhinin S. E., Ivanov I. A. Comparison of different types of startingthe belt conveyor of the coal mines. MIAB. Mining Inf. Anal. Bull. 2022, no. 12−2, pp. 129−142. [In Russ]. DOI: 10.25018/0236_1493_2022_122_0_129.

10. Metelkov V. P., Lieberman Ya. L. On the question of choosing the start-up mode of the conveyor belt. Electrotechnical Systems and Complexes. 2019, no. 2 (43), pp. 54–59. [In Russ]. DOI: 10.18503/2311-8318-2019-2(43)-54−5.

11. Dmitrieva V. V., Sobyanin A. A., Sizin P. E. Modeling soft start of belt conveyor induction motor. MIAB. Mining Inf. Anal. Bull. 2022, no. 6, pp. 77–92. [In Russ]. DOI: 10.25018/0236_1493_2022_6_0_77.

12. Dmitrieva V. V., Sobyanin A. A., Sizin P. E. Modeling of various modes of belt conveyor braking. MIAB. Mining Inf. Anal. Bull. 2022, no. 11, pp. 80–95. [In Russ]. DOI: 10.25018/0236_1493_2022_11_0_80.

13. Li Y., Li L. Research on Segmented Belt Acceleration Curve Based on Automated Mechanical Transmission. Processes. 2022, vol. 10, no. 1, p. 106. DOI: 10.3390/pr10010106.

14. Xiao D., Shan H. Performance evaluation of dual-motor driving system for pipe belt conveyor based on current tracking master-slave control. 2019 Chinese Control And Decision Conference (CCDC), IEEE. 2019, pp. 2540–2545. DOI: 10.1109/CCDC.2019.8833227.

15. Parmar G., Mukherjee S., Prasad R. System reduction using factor division algorithm and eigen spectrum analysis. Applied mathematical modelling. 2007, vol. 31, no. 11, pp. 2542–2552. DOI: 10.1016/j.apm.2006.10.004.

16. Manohar H., Sambariya D. K. Model order reduction of mimo system using differentiation method. 2016 10th International Conference on Intelligent Systems and Control (ISCO), IEEE. 2016, pp. 1–5. DOI: 10.1109/ISCO.2016.7726988.

17. Sambariya D. K., Gupta T. Reduced order model using modified cauer form for multi-input and multi-output LTI systems. 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), IEEE. 2017, pp. 1–6. DOI: 10.1109/ ICOMICON.2017.8279103.

18. Komarasamy R., Albhonso N., Gurusamy G. Order reduction of linear systems with an improved pole clustering. Journal of vibration and control. 2012, vol. 18, no. 12, pp. 1876–1885. DOI: 10.1177/1077546311426592.

19. Alsmadi O. M. K., Abo-Hammour Z. S. A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms. Computational intelligence and neuroscience. 2015, vol. 2015, pp. 27–27. DOI: 10.1155/2015/615079.

20. Chang W. D. Coefficient estimation of IIR filter by a multiple crossover genetic algorithm. Computers & Mathematics with Applications. 2006, vol. 51, no. 9–10, pp. 1437–1444. DOI: 10.1016/j.camwa.2006.01.003.

21. Diab A. Z., Vdovin V. V., Kotin D. A., Anosov V. N., Pankratov V. V. Cascade model predictive vector control of induction motor drive. 2014 12th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE), IEEE. 2014, pp. 669–674. DOI: 10.1109/APEIE.2014.7040771.

22. Nos O. V. Matrix transformations in mathematical Models of an induction motor. 9th International Conference on Actual Problems of Electronic Instrument Engineering, 24−26 September 2008. 2008, vol. 7. pp. 104–107. [In Russ]. DOI: 10.1109/APEIE.2008.4897073.

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