Tula State University, Tula, Russia

Shprekher D. M., Associate Professor, Doctor of Engineering Sciences, shpreher-d@yandex.ru
Zelenkov A. V., Post-Graduate Student

The article considers the control system of shearers designed for breaking and loading rocks onto a scraper conveyor. When cutting a coal seam by a shearer, the external disturbances (coal resistance to cutting), solid inclusions and change in the width of a shearer drum, which vary indefinitely, lead to the deterioration in the quality of transients. A typical cutting process controller in the form of a PI controller with the parameters configured for a specific mode of operation of the shearer is incapable to ensure the optimal functioning of the control system in all modes due to the non-linearity of the controlled object and owing to the random changes in the coal resistance to cutting. To improve the control quality indicators, it is necessary to select the parameters of the PI controller so as to minimize the amplitudes of the current steps of the cutting motor, and thereby to reduce the amplitudes of the torque in the transmission of the cutting drive and to minimize the system settling time. In this paper, we present an adjustment algorithm based on obtaining the values of the controller parameters for each of the possible operating modes of the shearer, identifying the type of a disturbing effect by the response curves of the system available to observation. Furthermore, it is proposed to use of an artificial feed-forward neural network as an operational tool of recognizing a multidimensional response curve in the control loop. The correctness of the obtained results is confirmed by the results of computer modeling.

1. Qin D., Jia H. Hybrid dynamic modeling of shearer drum driving system and the influence of housing topological optimization on the dynamic characteristics of gears. Journal of Advanced Mechanical Design, Systems, and Manufacturing. 2018. Vol. 12, Iss. 1. pp. ID. SM0020.
2. Shevchenko V. G., Kiyashko Yu. I. The technological principles of development of a way of control of a cutter-loader, as mechatronic system. Proceedings of the International Scientific-Technical Conference on Mechatronic Mining Equipment. Donetsk : DonNTU, 2010. pp. 25–34.
3. Babokin G. I., Kolesnikov E. B. Frequency-controlled electric drive of feed mechanisms for cleaning combines. GIAB. 2004. No. 3. pp. 330–331.
4. Aguilar-Mejia O., Minor-Popocatl H., Tapia-Olvera R. Comparison and ranking of metaheuristic techniques for optimization of PI controllers in a machine drive system. Applied Sciences. 2020. Vol. 10, No. 18. ID. 6592.
5. Astrom K. J., Hagglund T. Advanced PID Control. Research Triangle Park, NC : ISA, 2006. 461 p.
6. Glushchenko A. I. Neural network adaptive adjustment of regulators for controlling non-stationary technological objects in metallurgy: Theses of Dissertation of Doctor of Engineering Sciences. Voronezh, 2020. 304 p.

7. Eremenko Y. I., Poleshchenko D. A., Glushchenko A. I., Yarmuratii D. Y. About PID-regulator intellectual parametrs adaptation for control process power consumption decreasing. Nauchnyea vedomosti. Seriya Istoriya. Politologiya. Economica. Informatica. 2013. No. 22. pp. 210–217.
8. Zhang Sh. Study on the innovation of fully mechanized coal shearer technology in China. Journal of China Coal Society. 2010. Vol. 35, No. 11. pp. 1898–1902.
9. Fang X., Zhao J., Hu Y. Tests and error analysis of a self-positioning shearer operating at a manless working face. Mining Science and Technology. 2010. Vol. 20, Iss. 1. pp. 53–58.
10. Kolesnikov E. B. Development and research of the mechanism of forward motion with a frequency-controlled electric drive: Theses of Dissertation of Candidate of Engineering Sciences. Moscow, 1996. 249 p.
11. Morkun V., Morkun N., Tron V., Paraniuk D., Sulyma T. Adaptive control of drilling by identifying parameters of object model under nonstationarity conditions. Mining of Mineral Deposits. 2020. Vol. 14, Iss. 1. pp. 100–106.
12. Emelyanov A. V., Gordeev V. N., Zhabin I. P. Neural networks application for dc motor controller data identification. Izvestiya Tulskogo Gosudarstvennogo Universiteta.Technicheskie nauki. 2017. No. 11-3. pp. 252–262.
13. Jiao H., Wei B. Speed control method of mineral lifting and transportation machinery based on single neuron PID. Journal of Computational Methods in Sciences and Engineering. 2022. Vol. 22, Iss. 4. pp. 1263–1275.
14. Shcherbatov I. A., Artyushin V. A., Dolgushev A. N. Development of neural network auto adjustment unit PID-regulator for energy facilities. Information technologies. Problems and solutions. 2019. No. 1(6). pp. 190–195.
15. Shprekher D. M., Kolesnikov E. B., Zelenkov A. V. Investigation of possibility to stabilize load current of shearer’s cutting electric drive. Conference: 2020 International Russian Automation Conference (RusAutoCon). 2020. pp. 248–254.
16. Shprekher D. M., Babokin G. I., Kolesnikov E. B., Zelenkov A. V. Study loading dynamics for adjustable electric drive of shearer loader. Izvestiya Tulskogo Gosudarstvennogo Universiteta. Technicheskie nauki. 2020. No. 2. pp. 514–525.
17. Shepherd A. Second-Order Methods for Neural Networks. London : Springer-Verlag, 1997. 145 p.
18. Singh M., Sreejeth M., Hussain S. Implementation of Levenberg-Marquadrt Algorithm for Control of Induction Motor Drive. 2018 3^rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2018. pp. 865–869.