Fuzzy controller for temperature regulation: HOME I/O, EcoStruxure, and MATLAB integration
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13821Keywords:
Fuzzy Control, Intelligent Control, Industrial automation, Building control systems, Control software standardsAbstract
This paper addresses temperature regulation in buildings through the design and implementation of a Mamdani-type fuzzy controller, aimed at improving energy efficiency and control action smoothness. The novelty of the proposal lies in a co-simulation architecture that integrates three levels: the HOME I/O 3D virtual environment, the MATLAB/Simulink computing engine, and Schneider Electric's EcoStruxure Automation Expert industrial platform. This integration enables closing the control loop using the IEC 61499 standard and the OPC-UA communication protocol. The results demonstrate that, compared to a traditional PI control, the fuzzy solution provides a more resilient dynamic response and a control signal without aggressive peaks, facilitating technology transfer from simulation environments to real industrial controllers.
References
Alcalá, R., Benítez, J. M., Casillas, J., Cord´on, O., Herrera, F., 2003. Fuzzy control of hvac systems optimized by genetic algorithms. Applied Intelligence 18 (2), 155–177. DOI: 10.1023/A:1021986309149
Camacho, P. G., Fuentes, F., Escaño, J. M., 2026. Fuzzy logic controller for room temperature regulation: A co-simulation study with home i/o and matlab. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Coello, G. G., 2026. Integración de controlador borroso en plc bajo la norma iec 61499 para la regulación térmica de una habitación.
International Electrotechnical Commission (IEC), 2000. IEC 61131-7:2000 programmable controllers – part 7: Fuzzy control programming. https://webstore.iec.ch/en/publication/4556, international Standard.
Langner, F., Kovačević, J., Spatafora, L., Dietze, S., Waczowicz, S., Çakmak, H. K., Matthes, J., Hagenmeyer, V., 2025. Experimental evaluation of model predictive control and fuzzy logic control for demand response in buildings. Applied Energy 401, 126666. DOI: 10.1016/j.apenergy.2025.126666
Meana-Llorián, D., González García, C., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., Garcia-Fernandez, N., 2017. Iofclime: The fuzzy logic and the internet of things to control indoor temperature regarding the outdoor ambient conditions. Future Generation Computer Systems 76, 275–284. DOI: 10.1016/j.future.2016.11.020
PLCopen, 2000. Iec 61131-7. https://www.plcopen.org/standards/logic/iec-61131-7/.
Realgames, 2025. Home i/o simulation of a smart house and surrounding environment. URL: https://realgames.co/home-io/
Riera, B., Emprin, F., Annebicque, D., Colas, M., Vigário, B., 2016. Home i/o: a virtual house for control and stem education from middle schools to universities. In: IFAC-PapersOnLine. Vol. 49. Elsevier B.V., pp. 168–173. DOI: 10.1016/j.ifacol.2016.07.172
Schneider Electric, 2026. Ecostruxure™ automation expert. https://www.se.com/es/es/product-range/23643079-ecostruxure-automation-expert/.
Vallabha, G., 2026. Real-Time Pacer for Simulink. MATLAB Central File Exchange, retrieved January 20, 2026. URL: https://es.mathworks.com/matlabcentral/fileexchange/29107-real-time-pacer-for-simulink
Zadeh, L. A., 1965. Fuzzy sets. Information and Control 8 (3), 338–353. DOI: 10.1016/S0019-9958(65)90241-X
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Copyright (c) 2026 Gloria G. Coello, Ramón A. García, Pablo G. Camacho, Francisco Fuentes, Juan M. Escaño
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
