INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

Automatic Room Comfort Controller

Engr. Norman F. Fajardo¹, Engr. Glazen Carey M. Perez², Dr. Ma. Magdalena V. Gatdula³

^1,2College of Engineering, Graduate School, Bulacan State University, Guinhawa, City of Malolos,

Bulacan, Philippines, 3000

³Graduate School, Bulacan State University, Guinhawa, City of Malolos, Bulacan, Philippines, 3000

DOI: https://doi.org/10.51583/IJLTEMAS.2025.1411000048

Received: 10 November 2025; Accepted: 20 November 2025; Published: 08 December 2025

ABSTRACT

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.

Keywords: Fuzzy Logic, HVAC Automation, Indoor Comfort Control, Mamdani FIS, Smart Environment

Systems

INTRODUCTION

Indoor environmental comfort plays a critical role in energy efficiency, occupant well-being, and the operational

behavior of automated HVAC systems. Traditional thermostat-based systems typically rely on fixed setpoints or

binary switching logic, which may result in inconsistent environmental regulation and unnecessary energy

consumption. As energy demand and building automation systems continue to evolve, intelligent and adaptive

approaches to environmental control are increasingly necessary.

Fuzzy logic control systems provide an alternative to rigid binary decision-making by simulating human

reasoning and linguistic assessment rather than using strict numeric thresholds (Zadeh, 1965). In the context of

HVAC automation, fuzzy systems offer smoother transitions, better energy optimization, and adaptive real-time

responses to environmental fluctuations (Mendel, 2017).

This project explores the development and simulation of an Automatic Room Comfort Controller utilizing a

Mamdani-type fuzzy inference system. The controller considers temperature, humidity, and occupancy levels as

inputs and generates actuation levels for Fan/AC and Window Position. The system was conceptualized during

Week 2 and implemented and tested during Week 3 using MATLAB Fuzzy Logic Designer. Test results

demonstrated that the intelligent controller consistently balanced comfort and energy efficiency across multiple

scenarios.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

Fuzzy Logic Designer Overview

METHODOLOGY

This study followed a three-stage methodology: system design, implementation, and simulation testing. The

system design process involved defining variables, linguistic labels, and fuzzy control rules based on room

comfort standards. Temperature (15–35°C), humidity (20–90%), and occupancy (0–10 persons) were selected

as system inputs, while Fan/AC (0–100%) and Window Position (0–100%) served as outputs. Membership

functions were defined using linguistic descriptors including Cold, Comfortable, Warm, and Hot for temperature;

Dry, Comfortable, Humid, and Very Humid for humidity; and Empty, Few, or Many for occupancy. The rule

base formulation followed typical HVAC decision heuristics, such as increasing cooling when both temperature

and humidity are high and reducing ventilation during low occupancy conditions. The Mamdani fuzzy inference

model and centroid defuzzification method were used to generate crisp outputs.

During implementation, the fuzzy logic system was constructed using MATLAB’s Fuzzy Logic Designer

module. Membership function editors, rule editors, and surface mapping tools were used to configure and

visualize system behavior. Simulation was performed with at least three varying environmental test cases. Each

test included recording input values, expected outputs, actual outputs from the system (centroid values), and

observed system response patterns such as stability, adaptiveness, and smoothness of control. Results

demonstrated that the system responded with low output power at comfortable environmental levels while

increasing ventilation and cooling during high temperature and humidity conditions, validating its intended

intelligent behavior.

Membership Function Editor (for each variable)

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

System Design

The design stage involved defining the system inputs and outputs, specifying linguistic labels, and constructing

the fuzzy rule base. The system included three inputs: temperature (15–35 °C), humidity (20–90 %), and

occupancy (0–10 people). The outputs consisted of Fan/AC level and Window Position, both expressed as a 0–

100% control scale. Membership functions were created for each variable using descriptive linguistic classes

such as Cold, Comfortable, Warm, and Hot for temperature. The Mamdani fuzzy inference model was selected,

and the centroid method was used for defuzzification.

Implementation

The fuzzy system was implemented in MATLAB using the Fuzzy Logic Designer interface.

Membership functions were configured and validated, followed by encoding of rule logic based on designed IF–

THEN conditions. The Rule Viewer and Surface Viewer tools were used to visualize the system’s control

responses before running simulations.

Simulation And Testing

Three simulation scenarios were evaluated to examine how the controller responded to variations in

environmental conditions. Each test included recorded input values, expected outputs, MATLAB-generated

outputs, and observed behavior. The test results demonstrated the controller’s adaptive and stable behavior under

different temperature and humidity conditions.

RESULTS AND DISCUSSION

The Automatic Room Comfort Controller was evaluated using three simulation scenarios to assess its

performance under varying environmental conditions. The system processed temperature, humidity, and

occupancy inputs and generated two control outputs: Fan/AC level and Window Position. The results

demonstrated how the fuzzy logic model responded proportionally rather than switching abruptly, which is

typical of traditional HVAC systems.

The simulation results show a proportional change in the Fan/AC and Window Position outputs depending on

the input conditions:

 Test 1: At 22°C, 45% humidity, and low occupancy, the system generated a low Fan/AC output (25%) and

minimally opened the window (14.5%), maintaining comfort with low energy usage.

 Test 2: At 30°C, 70% humidity, and high occupancy, the system increased the Fan/AC level to 75% and

opened the window halfway (50%), demonstrating a strong adaptive response.

 Test 3: At 16°C, 30% humidity, and no occupancy, the controller maintained moderate system activation,

resulting in a Fan/AC level of 50% and a window position of 50%.

Overall, the simulation results show that the Automatic Room Comfort Controller performs effectively and

aligns with its intended design objectives. The fuzzy logic system successfully demonstrated:

 Adaptiveness: Outputs adjusted based on environment instead of fixed setpoints.

 Energy Efficiency: Lower outputs were produced under comfortable or unoccupied conditions.

 Stability: No abrupt or erratic switching occurred between states.

 Interpretability: Rule-based reasoning allowed the system to behave in a logical, predictable manner.

These results support findings in prior literature stating that fuzzy logic improves comfort regulation and system

responsiveness in intelligent HVAC applications (Mendel, 2017; Zadeh, 1965). The behavior observed in

simulations suggests that this controller is viable for real-world implementation, especially in smart building and

energy-efficient automation systems.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

CONCLUSION

The development and simulation of the Automatic Room Comfort Controller demonstrated that fuzzy logic is

an effective approach for regulating indoor temperature, humidity, and ventilation in real time. The Mamdani-

based fuzzy inference model successfully produced smooth and adaptive control outputs, avoiding abrupt

switching behavior commonly seen in conventional HVAC controllers. Simulation results across three test

conditions showed that the system adjusted cooling and ventilation proportional to environmental changes,

improving comfort while promoting energy-efficient operation.

The findings confirm that fuzzy logic control can enhance indoor environmental management and may serve as

a basis for advanced smart building automation. Future work may include hardware implementation using

sensors and microcontrollers, integration with IoT platforms, and evaluation under real-world environmental

variability to further validate system performance.

REFERENCES

1. Fajardo, N., & Perez, G. (2025). Automatic Room Comfort Controller: System concept and design

framework (Week 2). Bulacan State University.

2. Fajardo, N., & Perez, G. (2025). Automatic Room Comfort Controller: Simulation and implementation

results (Week 3). Bulacan State University.

3. 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

4. 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.

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About The Authors

Engr. Norman F. Fajardo is an Electronic Engineer currently employed at Globe Telecom, where he applies his

expertise in telecommunications and electronic systems. He is also pursuing graduate studies at Bulacan State

University, focusing on advanced research in his field. With a strong background in electronics and

communication technologies, he is committed to integrating practical industry experience with academic

knowledge to contribute to innovative solutions in the telecom sector. His professional and academic endeavors

reflect a dedication to continuous learning and technological advancement.

Engr. Glazen Carey M. Perez is a Mechatronics Engineer and Engineering Instructor at La Consolacion

University Philippines, where she teaches and mentors students in various engineering disciplines. She is

currently pursuing graduate studies at Bulacan State University, focusing on advancing her knowledge in

engineering research and applications. With a strong foundation in mechatronics and practical teaching

experience, she is dedicated to bridging academic theory with real-world engineering solutions. Her professional

and academic pursuits demonstrate a commitment to innovation, continuous learning, and the development of

future engineering professionals.

www.ijltemas.in

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

Dr. Ma. Magdalena V. Gatdula is a Computer Engineer and the University Registrar of Bulacan State University,

previously serving as Dean of the College of Engineering and the College of Information and Communications

Technology. She oversees major institutional processes, including academic records management, student

credentialing, and system development for university operations. A dedicated educator and academic leader, she

is actively involved in research as a panelist and adviser. Her work reflects a strong commitment to improving

institutional efficiency, strengthening academic integrity, and supporting innovation within the university’s

administrative and academic landscape.

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