Affiliation:
1. O.M. Beketov National University of Urban Economy in Kharkiv
Abstract
The article considers the option of solving the problem of automatic selection of settings of proportional-integral-differential (PID) regulators to control the thermal process with chaotically changing dynamic properties. Based on analytical and experimental data, it illustrates the influence of various factors on changes in the dynamic properties of electromechanical equipment (EME) when heated during operation. The work of classical and fuzzy PID controllers was investigated using simulation models in Matlab and Simulink environments. The article illustrates the expediency of changing the configuration charts of the PID regulator when controlling the process, which can be carried out with the help of a phasing unit that implements variants of the rules for controlling the signals of the charts. The Control System ToolboxTM application was used to determine the application rules for adjusting electrical values. The attractive nature of the changes in the transition processes reflects the graphical and tabular data. Transient processes in a nonlinear control algorithm (NCA) are characterised by improved parametric characteristics. To increase the accuracy of the chart settings, it is advisable to use data from actual thermal events that determine perturbing variables. Realisation of the above-mentioned intelligent device for automatic setting of the real-time regulator, providing the formation of a more accurate output of the control signal, determines the advantages of the proposed engineering solution and the feasibility of its use at similar technological facilities. In the article, the practical implementation of fuzzy logic with the help of the Arduino family microcontroller is recommended. In addition, the stand-alone NCA unit can be used with an additional actuator for smoother thermal control during the operation of EM components, while maintaining the same basic automation system with a classic PID controller.
Keywords: electromechanics, automation, controller, microcontroller, modelling, electronic model, fuzzy logic, algorithm, controller settings.
Publisher
O.M.Beketov National University of Urban Economy in Kharkiv
Reference14 articles.
1. Yesil, E., Guzelkaya, M., & Eksin, I. (2004). Internal model control based fuzzy gain scheduling technique of pid controllers. Proceedings of the Biannual World Automation Congress: Vol. 17 (pp. 501–506). Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://ieeexplore.ieee.org/document/1439416
2. Yesaulov, S. M., Babichevа, O. F., & Rohozhyna, Kh. O. (2019). Control and modeling parameters for heat diagnostics of power electrical equipment failure. Municipal Economy of Cities. Series: Engineering science and architecture, 5(151), 13–22. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/5438/5362/ [in Ukrainian]
3. Lamamra, K., Batat, F., & Mokhtari, F. (2020). A new technique with improved control quality of nonlinear systems using an optimized fuzzy logic controller. Expert Systems with Applications, 145, 113148. DOI: 10.1016/j.eswa.2019.113148
4. Mann, G. K. I., Hu, B.-G., & Gosine, R. G. (1999). Analysis of direct action fuzzy PID controller structures. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 29(3), 371–388. DOI: 10.1109/3477.764871
5. Yesaulov, S. M., Babichevа, O. F., & Kovalyk, M. M. (2020). Increasing the efficiency of thermal diagnostic control of electric motors. Municipal Economy of Cities. Series: Engineering science and architecture, 4(157), 163−171. DOI: 10.33042/2522-1809-2020-4-157-163-171 [in Ukrainian]