Neural Network Algorithm for Adjusting the PI Controller in the Shearer Control System

The control system of a shearer is considered, which is designed for destroying rock and loading it onto a scraper conveyor. When working out a coal seam by a shearer, external disturbances — the coal resistance to cutting, solid inclu- sions of rock, changes in the width of the screw, which vary indefinitely, lead to a deterioration in the quality of transients. The paper focuses on the shearer control system, the key elements of which are: a movement drive, a cutting drive, a coal face and a standard regulator that provides the system with the desired control quality indicators. A typical cutting current controller in the form of a PI controller with parameters configured for a specific mode of operation of the shearer cannot ensure the optimal functioning of the control system in all modes due to the non-linearity of the control object and the random of changes in the coal resistance to cutting. To improve the control quality indicators, it is necessary to choose the parameters of the PI controller so as to minimize the amplitudes of the current steps of the cutting motor, and therefore reduce the amplitudes of the moment in the transmission of the cutting drive and minimize the system quieting time. In this paper, we propose a tuning algorithm based on obtaining the values of the controller parameters for each of the possible modes of operation of the shearer, identifying the type of disturbing effect by the response curves of the system available to observation. At the same time, the use of an artificial feed forward neural network, acting as an operational means of recognizing a multidimensional response curve in the control loop, is proposed. A neural network of two architectures was used: with a scalar and a vector output function. The curve recognition algorithm satisfies the limitations on the speed of solving the problem of controlling an electromechanical system, since the recognition of a disturbance occurs in a time that does not exceed the output of the process to the maximum of the current step. The correctness of the obtained results was confirmed by the results of computer modeling.

1. Babokin G. I., Shprekher D. M., Kolesnikov E. B. Mathematical modeling of the electric drive of a shearer with a built-in moving system, Izvestija Tulskogo gosudarstvennogo universiteta. Tehnicheskie nauki, 2019, no. 3, pp. 645—651 (in Russian).

2. 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.

3. Voronin V. A., Nepsha F. S. Simulation of the electric drive of the shearer for assessing the energy efficiency of the power supply system, Zapiski Gornogo institute, 2020, no. 246, pp. 633—639 (in Russian).

4. Starikov B. Ya., Azarh V. L., Rabinovich Z. M. Asynchronous electric drive of shearers, Moskow, Nedra, 1981, 288 p. (in Russian).

5. Liu C., Qin D., Liao Y. Electromechanical dynamic analysis for the drum driving system of the long-wall shearer, Advances in Mechanical Engineering, 2015, vol. 7, no. 10, pp. 1—14.

6. Pfeiffer B. M. Towards "plug and control": self—tuning temperature сontroller for PLC, International journal of Adaptive Control and Signal Processing, 2000, no. 14, pp. 519—532.

7. Glushchenko A. I. Neural network adaptive tuning of regulators to control non-stationary technological objects in metallurgy, Doctor of Technical Sciences (Engineering) dissertation, Voronezh, 2020, 304 p. (in Russian).

8. Eremenko Yu. I., Poleshchenko D. A., Glushchenko A. I., YArmuratij D. Yu. On intelligent adaptation of PID controller parameters to reduce energy consumption of a controlled process, Nauchnye vedomosti. Seriya Istoriya. Politologiya. Ekonomika. Informatika. 2013, vol. 22 (165), no. 28/1, pp. 210—217 (in Russian).

9. Astrom K. J., Hagglund T. Advanced PID Control, Research Triangle Park: ISA, The Instrumentation, Systems, and Automation Society, 2006, 461 p.

10. Emel’yanov A. V., Gordeev V. N., Zhabin I. P. The use of neural networks to identify the current parameters of the control object of a direct current electric drive, Izvestiya Tulskogo gosudarstvennogo universiteta. Tekhnicheskie nauki, 2017, vol. 11, no. 3, pp. 252—261 (in Russian).

11. Il’yasov B. G., Darincev O. V., Migranov A. B. Using a neurael ntwork predictor in a microtechnological process control system, Mekhatronika, Avtomatizaciya, Upravlenie, 2005, no. 8, pp. 39—45 (in Russian).

12. Volkov V. G., Dem’yanov D. N. Synthesis and neural network implementation of the PI controller for the adaptive cruise control of a truck, Mekhatronika, Avtomatizaciya, Upravlenie, 2018, no. 11, pp. 707—713 (in Russian).

13. Shcherbatov I. A., Artyushin V. A., Dolgushev A. N. Development of a neural network block for autotuning a PID controller for power facilities, Informacionnye tekhnologii. Problemy i resheniya., 2019, no. 1(6), pp. 190—195 (in Russian).

14. Zmeu K. V., Notkin B. S., D’yachenko P. A. Modelless predictive inverse neurocontrol, Mekhatronika, Avtomatizaciya, Upravlenie, 2006, no. 9, pp. 8—15 (in Russian).

15. Ivashchuk D. G. PID control algorithm based on artificial neural networks, Sovershenstvovanie metodologii poznaniya v celyah razvitiya nauki: sbornik statej Mezhdunarodnoj nauchno-prakticheskoj konferencii, 2018, pp. 16—18 (in Russian).

16. Kravec P. I., Zherebko V. A., Shimkovich V. N. Method of hardware and software implementation of the PID controller of a single neural network on an FPGA, Vestnik Vinnickogo politekhnicheskogo instituta, 2011, no. 3 (96), pp. 148—152 (in Ukr).

17. Shprekher D. M., Kolesnikov E. B., Zelenkov A. V. Investigation of possibility to stabilize load current of shearer’s cutting electric drive, Proceedings — 2020 International Russian Automation Conference, RusAutoCon 2020, Sochi, Russia, pp. 248—254.

18. Shprekher D. M., Babokin G. I., Kolesnikov E. B., Zelenkov A. V. Investigation of the dynamics of loading of the variable electric drive of the shearer, Izvestiya Tulskogo gosudarstvennogo universiteta. Tekhnicheskie nauki, 2020, no. 2, pp. 514—525 (in Russian).

19. Shepherd A. J. Second-Order Methods for Neural Networks, London, Springer-Verlag, 1997, 145 p.