Automatic Room Comfort Controller
Article Sidebar
Main Article Content
The Automatic Room Comfort Controller was developed using a fuzzy logic–based control system to regulate indoor environmental conditions. The system uses three primary inputs—temperature, humidity, and occupancy—to generate two control outputs, namely Fan/AC speed and Window Position. The controller was designed and simulated using MATLAB’s Fuzzy Logic Designer, following defined membership functions and IF–THEN rule sets. Simulation results demonstrated that the fuzzy controller was able to adapt system responses according to varying room conditions, maintaining comfort while minimizing excessive energy use. The behavior observed across multiple input test scenarios showed that the system responded smoothly and consistently, avoiding abrupt switching common in traditional fixed threshold HVAC systems. Overall, the fuzzy logic model proved effective for real-time intelligent indoor climate regulation.
Downloads
References
Fajardo, N., & Perez, G. (2025). Automatic Room Comfort Controller: System concept and design framework (Week 2). Bulacan State University.
Fajardo, N., & Perez, G. (2025). Automatic Room Comfort Controller: Simulation and implementation results (Week 3). Bulacan State University.
Al-Sakkaf, A., & Al-Hamadi, A. (2023). Fuzzy logic–based HVAC control system for energy-efficient smart buildings. Journal of Building Engineering, 75, 106458. https://doi.org/10.1016/j.jobe.2023.106458
Goyal, G., Sharma, K., & Singh, A. (2021). Occupancy-driven fuzzy control strategies for improving HVAC efficiency in smart indoor environments. Automation in Construction, 130, 103894.
https://doi.org/10.1016/j.autcon.2021.103894
Nguyen, T. M., & Bui, D. T. (2020). An optimized fuzzy logic controller for improving indoor comfort and reducing energy demand. Applied Soft Computing, 96, 106613. https://doi.org/10.1016/j.asoc.2020.106613
Rahman, M. F., Chowdhury, S., & Kabir, M. H. (2024). Design and simulation of a fuzzy inference system for indoor temperature regulation using MATLAB. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 45–52. https://doi.org/10.18201/ijisae.2024123
Zhang, Y., Ren, H., & Liu, P. (2022). Adaptive thermal comfort control using fuzzy logic and IoT-based sensing in smart buildings. Energy and Buildings, 278, 112512.https://doi.org/10.1016/j.enbuild.2022.112512
Mendel, J. M. (2017). Uncertain rule-based fuzzy systems: Introduction and new directions (2nd ed.). Springer. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.