The paper presents the results of modeling a fuzzy logic controller for an intelligent system for monitoring and managing indoor microclimate conditions. The use of fuzzy logic in programming provides certain advantages, such as simplicity of data input into the control system and the ability to reduce errors inherent in classical microcontrollers. The microprocessor system used is based on the Arduino UNO board model Arduino Rev3, which features the ATMEL ATmega328P microcontroller and is compact, cost-effective, and easy to use. A fuzzy logic controller is utilized in the system for effective microclimate regulation. During the development of the intelligent system, the LabView software environment and Arduino IDE were employed. The study breaks down the system into several components and establishes links between them to enhance the efficiency of the software. Decisionmaking is determined by the system's functional requirements and the selected equipment for implementation.
In any indoor space, a unique microclimate is formed a combination of factors that affect comfort and well-being during occupancy [1]. The main characteristics of the microclimate include: temperature, relative humidity, air quality, air movement speed, thermal (infrared) radiation intensity, carbon dioxide concentration, fresh air intake, dust levels, etc. [2]. The values of microclimate parameters are classified as: minimal thermoregulation strain, and a sense of comfort.
- Permissible: These represent criteria where a person may experience discomfort, applied when optimal standards cannot be met.
For each type of space, sanitary norms and standards are established to regulate permissible microclimate characteristics to ensure optimal living and working conditions. In Ukraine, the main regulatory document governing microclimate parameters in industrial premises is DSN 3.3.6.042-99 "Sanitary Norms for the Microclimate of Industrial Premises", which specifies optimal and permissible microclimate indicators and sets requirements for measurement methods.
Microclimate regulation methods are categorized as passive or active [3]. Passive methods involve architectural and design solutions that naturally regulate microclimate parameters, such as natural ventilation, sun protection designs, or insulation. Active methods involve the use of devices, systems, and technologies to control microclimate parameters actively. These methods have evolved significantly, from basic heating systems and mechanical ventilation to sophisticated intelligent automated systems utilizing fuzzy logic and machine learning.
The task of developing new climate control systems and improving existing ones remains a relevant scientific and practical challenge, as effective management of microclimate parameters ensures a high level of comfort for occupants and energy efficiency in buildings [4]. The application of fuzzy logic provides flexible and smooth control of microclimate parameters, allowing the use of fuzzy verbal categories and adaptive solutions. This improves the efficiency of climate control systems, as fuzzy logic accounts for a wide range of factors and parameters under conditions of incomplete or inaccurate information. Consequently, this positively affects the performance of microclimate control systems, creating an optimally comfortable environment with minimal energy consumption, which is critically important in the context of current energy efficiency and sustainable development requirements.
The goal of this study is to develop hardware, algorithmic, and software components for an indoor microclimate control system. The research tasks include: developing a fuzzy logic controller for the system, creating a system operation algorithm, implementing the hardware component, and designing the system's software architecture. The advantages of using fuzzy logic in the developed system include the ability to adjust the system's actions in real time based on current microclimate parameters and considering individual user preferences. The practical significance of the proposed solutions lies in the compactness, low cost, and ease of use of the developed intelligent system.
Numerous studies have substantiated [
Currently, microprocessor-based systems for indoor climate control are used to monitor and
automatically regulate microclimate parameters [
Advances in computer technology and electronics have enabled the development of climate control systems that process sensor data in real time and send commands to regulate devices, such as thermostats or air conditioners. Achievements in machine learning have allowed the integration of prediction modules into microclimate control systems [12-14]. These prediction modules anticipate changes in microclimate parameters based on intelligent processing of retrospective data and/or weather forecasts.
In cases where microclimate parameters are challenging to predict due to their nonlinear nature, fuzzy logic can lead to more effective decision-making, ultimately improving comfort, energy efficiency, and adaptability of climate control systems. A popular and practically valuable approach is the use of fuzzy logic to create linguistic variables and rules in microclimate control 322 systems. The works in [15-18] describe the design and successful implementation of this approach. The application of fuzzy logic improves the quality of microclimate parameter management compared to binary systems or rigid rule-based controllers, as it allows for processing imprecise input values and converting them into specific commands for electronic devices.
The modern smart home is a dynamic ecosystem where various devices work together to maintain an optimal living environment [15]. To achieve this, we will develop control system. The fuzzy logic controller (FLC) is well-suited for applications requiring a degree of uncertainty, or imprecision, like home environmental control. Unlike traditional binary logic, where an input varying degrees of membership [16].
The construction of control systems using a fuzzy logic controller was carried out in the following order: - selecting inputs and outputs of the control system. - defining membership functions for each input and output variable. - forming a database of fuzzy rules. - selecting and implementing a fuzzy inference algorithm. - analyzing the control process using the developed system.
The design of the fuzzy logic system was performed using the Fuzzy System Designer add-on of the LabView software environment. Experiments were conducted using software developed by the authors that implements the fuzzy system, and were compared with results obtained in the LabView environment.
We start with the primary input parameters which are temperature (0 50°C), CO2 concentration (0 1000 ppm), and humidity (0 100%). The system provides control over the smart house humidifier (on/off), conditioner (on/off), and the state of the heater-cooler (freeze/off/warm). The system monitors indoor temperature, ranging between different conditions from freezing to very warm environments. CO2 levels are important for assessing air quality, and the system measures concentrations between 0 and 1000 parts per million (ppm). High CO2 levels indicate poor air circulation, potentially necessitating adjustments in the ventilation or conditioning system. Relative humidity, measured on a scale from 0% to 100%, is influences comfort, like preventing dry air.
The controller influences three major output controllables. The first one is humidifier on/off: The system turns the humidifier on or off based on the humidity level and overall environmental conditions. The air conditioner can be turned on or off depending on the temperature and CO2 levels. The heater/cooler operates in freeze state when the system cools down the environment. It is off when neither heating nor cooling is activated. It can warm up to increases the temperature to reach comfort levels.
Each input parameter (temperature, CO2, humidity) is characterized by fuzzy sets.
For the given task, comfortable conditions are determined primarily by the humidity and temperature of the indoor air. Measurements of these parameters are fed to the inputs of the control system. The output signals of the system are on and off signals that are sent to the heater, air conditioner, and humidifier. For the fuzzy temperature controller, the input linguistic variable is the room temperature. According to recommendations, the indoor air temperature should be between 18 and 24 °C. If the temperature in the room is below 18 °C, it is considered low and has a low level of comfort for people. If the air temperature is above 24 °C, it is considered overheating. Thus, this input variable has three terms (low, normal, high). The output signals are the on and off signals for the heater and air conditioner operating in cooling mode, so these signals have two terms each (On and Off). The definition of the membership functions was performed in Fuzzy System Designer. The results obtained for the input and two output variables are shown in Figure 1.
Fuzzy set of temperature is following: (Temperature, T={Low, Norm, High}, X=[0 .. 50]) μLow = trapmf (0,0,18,21), μNorm = trapmf (18,20,22,24), μHigh = trapmf (21,24,50,50).
Fuzzy rules combine the fuzzy sets of the input parameters to determine the appropriate output actions:
IF Thermometer is Norm THEN Heater is Off ALSO Conditioner is Off IF Thermometer is High THEN Heater is Off ALSO Conditioner is On IF Temperature is Low THEN Heater is On ALSO Conditioner is Off
In this software environment, it is possible to test the developed fuzzy logic controller (Figure 2).
The test results confirm the correctness of the adopted decisions. The fuzzy temperature controller has one input and two outputs; thus it has a SIMO structure.
For the fuzzy humidity controller, the input linguistic variable is the indoor air humidity. The output linguistic variable is the control signal value sent to the humidifier.
Normal air humidity ranges between 40-60%, high humidity exceeds 60%, and dry air has humidity below 40%. The membership function for the input variable also has three terms: low, normal, high (Figure 3). The membership function for the output variable has two terms (On and Off).
Fuzzy set of Humidity is following: (Humidity, H={Low, Norm, High}, X=[0 .. 100]) μLow = trapmf (0,0,40,48), μNorm = trapmf (38,48,53,62), μHigh = trapmf (53,60,100,100). The obtained fuzzy rule base contains three rules: IF Humidity is High THEN Humidifer is Off IF Humidity is Low THEN Humidifer is On IF Humidity is Norm THEN Humidifer is Off
The test results confirm the correctness of the adopted decisions (Figure 4).
Control signal u can be represented by formula:
where xi input variable. functions: = ( ⋃ ( =1
( ))) .
( ) = ( ) = ℎ ( ) = ( ) = ( ) = ℎ ( ) = ( ( ( ( ( ( ( (
(1, − 18 21 −
3 , 1, 2 ( (1, 10 ( − 21 3 48 −
8 , 1, − 53
7 − 38
) , 0), 24 −
2 , 1) , 0).
) , 0), 62 −
9 , 1) , 0).
) , 0), ) , 0), For the humidity input variable H the following triangular membership functions are defined: To determine the fuzzy output the fuzzy inference is used: ( ) = ( , ℎ ).
The process of converting exact inputs (such as 27°C temperature or 650 ppm CO2) into fuzzy values is done using fuzzification. After applying the fuzzy rules, the resulting fuzzy output converted back into an exact action, such as turning on the air conditioner or activating the humidifier. The popular method of defuzzification is centroid method, which calculates the center of the area under the membership function curve.
The FLC operates within a feedback control system. Sensors continuously monitor the environmental parameters (temperature, humidity, CO2), sending real-time data to the controller. The FLC then processes this data, applies the fuzzy logic rules, and adjusts the operation of the humidifier, air conditioner, and heater-cooler.
The developed flowchart of the system's operation algorithm is shown in Figure 5.
Hi=Ha Yes Yes
Ti>Ta Yes
Heater OFF Conditioner ON Yes
Hi<Ha No No No No
Conditioner OFF
The system architecture involves three layers. The input layer collects data from sensors (temperature, CO2, humidity). The processing layer applies fuzzification, evaluates fuzzy rules, and defuzzifies outputs. The output layer sends control signals to the smart house devices.
Through the application of fuzzy rules and logic, this system can manage the balance between comfort, air quality, and energy consumption, providing a seamless experience for smart home users.
The requirements for the functioning of this system are reduced to ensuring the specified temperature and humidity conditions in the room. To meet these requirements, the system collects temperature and humidity measurements from the room and sends the measurement results to the microcontroller.
The main criteria for software development are execution speed, ease of use, cross-platform capability, simplicity of implementation, and many others [14]. In this case, the project is implemented using an Arduino UNO board, model Arduino Rev3, based on the ATMEL ATmega328P microcontroller. The Arduino hardware board consists not only of the microcontroller but also includes everything necessary to connect to external peripheral devices. In addition, it has a built-in programmer that allows direct programming from a computer. A distinctive feature of these microprocessor devices is the availability of open-source code and a significant number of libraries for the interaction of the embedded microcontroller with other external components. It is widely used for developing interactive objects and receiving data from various sensors or switches. Considering this, Arduino is the most popular embedded platform used in many projects.
The code is developed in the native Arduino IDE, based on a programming language that is a variant of C/C++ for microcontrollers. Arduino projects can be either autonomous or interact with any software running on a computer. In this project, LabView software with a visual graphical programming language was used.
The general view of the front panel and the block diagram of the developed system project are shown in Figures 6 and 7, respectively. concentration in real time. Changes in temperature and humidity can be monitored graphically.
The air in the room should have a CO2 concentration between 0.01-0.05%. An elevated CO2 level ranges from 0.05-0.09%, while a high concentration exceeds 0.09%. If the indoor climate control system detects elevated or high CO2 levels, the exhaust ventilation is activated. In this case, the front panel displays a warning and a danger signal (Figure 8).
The developed system can operate in both automatic and manual modes. Switching between modes is done using an interactive switch on the front panel. In manual mode, the user turns the actuators heater, air conditioner, humidifier, and ventilation on or off (Figure 9). In automatic mode, these operations are performed by the system based on the built-in regulator.
The developed microprocessor system also has a configuration mode, which is activated using an interactive switch on the front panel (Figure 10). In this mode, sensor signals are disconnected from the system inputs and replaced by signals from simulators. The values of the simulated signals can be changed from the front panel. This solution allows modeling of any possible situation at the site and evaluating the system's response, as well as checking and adjusting the regulators.
A simulation of fuzzy logic controllers was conducted for the developed intelligent indoor climate control system. The results highlighted several advantages of this development, including: - Enhanced quality of control. - Low sensitivity to changes in the parameters of the controlled object.
- Simplified synthesis of control systems with fuzzy logic using modern hardware and software support compared to traditional control systems.
The simulation was carried out using LabVIEW a platform and development environment for the visual graphical programming language by National Instruments (USA). Integrating the Arduino platform with this environment provides many opportunities to simplify project implementation:
- A user-friendly package allows lines of code to be converted into a graphical program, hiding the complexities of hardware and software.
- The data flow programming model focuses on code implementation by connecting the inputs and outputs of graphical blocks, enabling an intuitive graphical code approach instead of textual programming.
- It allows for quick and easy creation of a graphical user interface for the developed program. - LabVIEW software includes a wide range of useful add-ons and libraries, opening new possibilities for project implementation.
An intelligent indoor climate control and management system based on fuzzy control has been developed. The integration of the Arduino platform with the LabView programming environment significantly reduces development time and enhances system performance. Software testing has been conducted, demonstrating that the developed software meets the requirements of the technical specifications. A comparison of the results produced by the developed software with those obtained in the LabVIEW environment showed a good match, confirming the adequacy of the intelligent system. In addressing this problem, the primary objective was to ensure human comfort within the indoor environment rather than to conserve energy resources. Nevertheless, through the application of fuzzy climate control, energy savings of at least 9% were achieved.
The authors have not employed any Generative AI tools.