Cognitive Model for Control of a Peltier Thermoelement

Main Article Content

Maksim Vladimirovich Bobyr
Artem Andreevich Aseev

Abstract

The article presents an ontological model of a control system for a Peltier thermoelectric element. The ontology describes the structure of the system by identifying objects, transformation processes within these objects, and the attributes of the relationships between them. Based on the developed ontological model, a cascade control system has been designed, integrating a PID controller, a fuzzy-digital filter, and an exponential-averaging filter, with its cognitive behavior governed by fuzzy logic rules. Improvement of the dynamic characteristics of transient processes in the Peltier element control system is achieved through the application of the mathematical and ontological solutions specified in the model. The cascade control system reduces the amplitude of the first harmonic of the control signal by 12% and decreases the transient response time by 31.9%.

Article Details

How to Cite
Bobyr, M. V., and A. A. Aseev. “Cognitive Model for Control of a Peltier Thermoelement ”. Russian Digital Libraries Journal, vol. 29, no. 3, June 2026, pp. 976-97, doi:10.26907/1562-5419-2026-29-3-976-997.

References

1. Grebeshkov A., Shebalov R., Gorshkov S., Mushtak O. Ontological modelling of enterprises: technologies and methods // Book, TriniData LLC & Ural Federal University. 2019. P. 104–116. ISBN: 978-5-7996-2580-1.
2. Krieken E., Acar E., Harmelen F. Analyzing Differentiable Fuzzy Logic Operators // Artificial Intelligence. 2022. Vol. 302, 103602. https://doi.org/10.1016/j.artint.2021.103602
3. Jalahi A., Linke M., Weltzien C., Mahajan P. Developing an Arduino-based control system for temperature-dependent gas modification in a fruit storage container // Computers and Electronics in Agriculture. 2022. Vol. 198, 107126. https://doi.org/10.1016/j.compag.2022.107126
4. Loche-Moinet F., Theolier L., Woirgard E. Electro-thermo-mechanical modelling of a SiC MOSFET transistor under non-destructive short-circuit // Microelectronics Reliability. 2023. Vol. 150, 115143. https://doi.org/10.1016/j.microrel.2023.115143
5. Leva A., Zamuner M. Model Parametrisation and Rule Selection for Problem-tailored PID Autotuning // IFAC-PapersOnLine. 2024. Vol. 58, Issue 7. P. 43–48. https://doi.org/10.1016/j.ifacol.2024.08.008
6. Bassi S.J., Gbenga E.D., Abidemi A., Oyewola D., Mohammed B.K. Metaheuristic Algorithms for PID Controller Parameters Tuning: Review, Approaches and Open Problems // Heliyon. 2022. Vol. 8, Issue 5, e09399. https://doi.org/10.1016/j.heliyon.2022.e09399.
7. Keviczky L., Bányász C. Adaptive Iterative Method to Improve the Robustness of PID Regulators // IFAC-PapersOnLine. 2022. Vol. 55, Issue 12. P. 149–155. https://doi.org/10.1016/j.ifacol.2022.07.303
8. Signe R.K., Motto F.B. Fuzzy-PID controller based sliding-mode for suppressing low frequency oscillations of the synchronous generator // Heliyon. 2024. Vol. 10, Issue 15, e35035. https://doi.org/10.1016/j.heliyon.2024.e35035
9. Xian W., Qi Q., Liu W., Liu Y., Li D., Wang Y. Control of quadrotor robot via optimized nonlinear type-2 fuzzy fractional PID with fractional filter: Theory and experiment // Aerospace Science and Technology. 2024. Vol. 151, 109286. https://doi.org/10.1016/j.ast.2024.109286.
10. Outanoute M., Selmani A., Oubehar H., Snoussi A., Guerbaoui M., Ed-Dahhak A., Lachhab A., Bouchikhi B. Self Tuning Fuzzy-PID Controller in Real Time Greenhouse Temperature Control // Conference: The Third International Conference on Optimization and Applications (ICOA 2017) At: Meknes, Morocco; 2017.
11. Bobyr M.V., Milostnaya N.A., Bulatnikov V.A. The fuzzy filter based on the method of areas’ ratio // Applied Soft Computing. 2022. Vol. 117, 108449. https://doi.org/10.1016/j.asoc.2022.108449
12. Ma Z., Pan T., Tian J. Deep reinforcement learning optimized double exponentially weighted moving average controller for chemical mechanical polishing processes // Chemical Engineering Research and Design. 2023. Vol. 197. P. 419–433. https://doi.org/10.1016/j.cherd.2023.07.049
13. Bobyr M., Titov V., Belyaev A. Fuzzy System of Distribution of Braking Forces on the Engines of a Mobile Robot // MATEC Web of Conferences. 2016. Vol. 79, 01052. https://doi.org/10.1051/matecconf/20167901052
14. Barelli L., Bidini G., Arce R. Fuzzy Logic Regulator for the Performance Improvement and the Energy Consumption Reduction of an Industrial Chiller // Conference: ASME 2003 International Mechanical Engineering Congress and Exposition, 2008, 41910. https://doi.org/10.1115/IMECE2003-41910
15. Bobyr M., Arkhipov A., Emelyanov S., Milostnaya N. A method for creating a depth map based on a three-level fuzzy model // Engineering Applications of Artificial Intelligence. 2023. Vol. 117, 105629. https://doi.org/10.1016/j.engappai.2022.105629
16. Bobyr M.V., Arkhipov A.E., Yakushev A.S. Shade recognition of the color label based on the fuzzy clustering // Informatics and Automation. 2021. Vol. 20 (2). P. 407–434. https://doi.org/10.15622/ia.2021.20.2.6
17. Bobyr M.V., Milostnaya N.A., Nolivos K.A. Combination of a fuzzy-digital filter and a PID controller in the problem of controlling a thermoelement // Mechatronics, automation, control. 2022. Vol. 23. https://doi.org/ 10.17587/mau.23.473-480