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FUNDAMENTALS OF SYSTEM IDENTIFICATION FOR TEMPERATURE AND RELATIVE HUMIDITY CONTROL IN MODERN GREENHOUSE

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This article investigates the system identification problem for the effective control of temperature and relative humidity in greenhouses under the climate conditions of Uzbekistan, using a linear ARX (AutoRegressive with eXogenous input) model. The aim of this study is to construct a mathematical model that describes the dynamics of microclimate parameters based on real-time experimental measurements and, on this basis, establish the scientific foundations of an automatic control system. The widespread adoption of greenhouses in Uzbekistan, the need to maintaine the stability of agrotechnological processes, efficient use of energy and water resources, andadaptation to global climate changes determine the relevance of this research. The ARX model structure was selected as the research methodology, and MATLAB Toolbox was used for parameter estimation and model adequacy verification. The obtained results provide a scientific and practical basis for the development of modern regulators to ensure precise control of microclimate parameters. The model’s computational simplicity, speed, and integration capability with real-time systems make it effective in practical applications, particularly in Uzbek greenhouses. The use of the linear ARX model is demonstrated as an effective solution to improving the accuracy and reliability of automated temperature and humidity control systems in greenhouses.

AUTHORS

A.Abdullayev

O‘zbekiston Respublikasi Iqtisodiyot va moliya vazirligi

E.Oraqov

O‘zbekiston Respublikasi Iqtisodiyot va moliya vazirligi

Tags

# температура# harorat# temperature# relative humidity# относительная влажность# nisbiy namlik# issiqxona mikroiqlimi# ARX modeli# tizim identifikatsiyasi# mikroiqlim dinamikasi# MATLAB Toolbox.# микроклимат теплицы# ARX-модель# идентификация системы# динамика микро- климата# Greenhouse microclimate# ARX model# System identi�ication# Microclimate dynamics

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References

Adeyemi, O., Grove, I., Peets, S., & Domun, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18(3408). https://doi.org/10.3390/ s18103408

Altes-Buch, Q., Quoilin, S., & Lemort, V. (2019, March). Greenhouses: A Modelica library for the simulation of greenhouse climate and energy systems. In Proceedings of the 13th International Modelica Conference (pp. 54). Regensburg, Germany. https://doi.org/10.3384/ecp19157533

Arnaud, S. E., Calisti, M., & Polydoros, A. (2025). Data-driven greenhouse climate regulation in lettuce cultivation using BiLSTM and GRU predictive control. Computers and Electronics in Agriculture, 215, 108417. https://doi.org/10.1016/j.compag.2025.108417

Chen, S., Liu, A., Tang, F., Hou, P., Lu, Y., & Yuan, P. (2025). A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses. Sensors, 25(5), 1388. https://doi. org/10.3390/s25051388

Chimankare, R. V. (2023). A review study on the design and control of optimised greenhouse environments. Renewable Agriculture Reviews, 18(4), 221–235.

Despommier, D. D. (2011). The vertical farm: Feeding the world in the 21st century. First Picador ed., St. Martin’s Press.

Diaz, G. (2023). Design and evaluation of a greenhouse interface for user-friendly climate monitoring and control. International Journal of Smart Agriculture, 9(3), 102–115.

Kalantari, F., Tahir, O. M., Joni, R. A., & Fatemi, E. (2017). Vertical farming: Creating an accessible and sustainable future. A review. Journal of Landscape Ecology, 10(1), 35–60.

Kittas, C., Katsoulas, N., Bartzanas, T., & Bakker, S. (2013). Greenhouse climate control and energy use. Food and Agriculture Organization of the United Nations.

Labidi, A., Chouchaine, A., & Marni, A. (2021). Intelligent climate control system inside a greenhouse. International Journal of Advanced Computer Science and Applications, 12(2). https://doi. org/10.14569/IJACSA.2021.0120229

Linker, R., Kacira, M., & Arbel, A. (2011). Robust climate control of a greenhouse equipped with variable-pressure fogging system and variable-speed extracting fans. Control Engineering Practice, 19(6), 636–648. https://doi.org/10.1016/j.conengprac.2011.02.003

López-Cruz, I.L., Ramírez-Arias, A., Rojano-Aguilar, A. and Ruiz-García, A. (2008). Modeling of greenhouse climate using evolutionary algorithms. Acta Hortic. 801, 401-408. https://doi. org/10.17660/ActaHortic.2008.801.42

Mahmood, F., Govindan, R., Bermak, A., Yang, D., & Al-Ansari, T. (2023). Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment. Applied Energy, 343, Article 121190. https://doi.org/10.1016/j.apenergy.2023.121190

Mallick, S., Airaldi, F., Dabiri, A., Sun, C., & De Schutter, B. (2024). Reinforcement Learning- based Model Predictive Control for Greenhouse Climate Control. arXiv preprint. arXiv:2409.12789 arxiv.org

Matyakubova, P. M., Ismatullayev, P. R., & Sharipov, Sh. M. (2023). O‘lchash usullari va vositalari (fizik-kimyoviy o‘lchashlar bir qismi) [Methods and tools of measurement (part of physico-chemical measurements)]. Tаshkent.

Morales, M. (2018). Deep reinforcement learning. Manning Publ.

Morcego, B., López, G., & Colomer, M. (2023). Reinforcement learning versus model predictive control on greenhouse climate control. Control Engineering Practice, 138, 105536. https://doi. org/10.1016/j.conengprac.2023.105536

Naagarajan, R. A., Sathyanarayanan, K. K., Bauer, N., & Streif, S. (2025). Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control. Frontiers in Agronomy, 7. https://doi.org/10.3389/fagro.2025.1536998

Platero-Horcajadas, M., Pardo-Pina, S., Cámara-Zapata, J.-M., Brenes-Carranza, J.-A., & Ferrández-Pastor, F.-J. (2024). Enhancing greenhouse ef�iciency: Integrating IoT and reinforcement learning for optimized climate control. Sensors, 24(24), 8109. https://doi.org/10.3390/s24248109 MDPI

Puglisi, G., Vox, G., Schettini, E., Morosinotto, G., & Campiotti, C. (2017). Climate control inside a greenhouse by means of a solar cooling system. In International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant 1227. Beijing, China.

Sen, N. (2018). Automatic Climate Control of a Greenhouse: a Review. ADBU Journal of Electrical and Electronics Engineering (AJEEE).

Singh, N. (2024). IoT-based greenhouse technologies for enhanced crop monitoring and climate control. Journal of Agricultural IoT Studies, 5(2), 45–56.

Tuttelberg, K., Kilter, J., & Uhlen, K. (2017). Comparison of system identification methods applied to analysis of inter-area modes. In Proceedings of the International Conference on Power Systems Transients (IPST2017). Seoul, South Korea.

Urakov, E. E., Musayeva, Z. D., & Rashidov, T. E. (2023). The importance of metrology and standardization in greenhouse climate control. Science and Innovation, Series D, 2(1). https:// scientists.uz/view?id=3604

Van Mourik, S., van ’t Ooster, B., & Vellekoop, M. (2023). Plant performance in precision horticulture: Optimal climate control under stochastic uncertainty. Biosystems Engineering, 232, 34– 48. https://doi.org/10.1016/j.biosystemseng.2023.05.010

Yassin, I. M., Taib, M. N., & Adnan, R. (2013). Recent Advancements & Methodologies in System Identification: A Review. Scientific Research Journal (SCIRJ), 1(1), 14-33. https://www.scirj.org/ papers-0813/scirj-august-2013-edition-03.pdf

Yusuf, A. G. (2025). Optimizing greenhouse microclimate for plant pathology. Plant Pathology and Environment, 12(1), 77–89.