=Paper=
{{Paper
|id=Vol-3106/Paper_6
|storemode=property
|title=IoT Control Systems base on Fuzzy PWM-controller
|pdfUrl=https://ceur-ws.org/Vol-3106/Paper_6.pdf
|volume=Vol-3106
|authors=Roman Ponomarenko,Anastasiia Demchuk
|dblpUrl=https://dblp.org/rec/conf/intsol/PonomarenkoD21
}}
==IoT Control Systems base on Fuzzy PWM-controller==
https://ceur-ws.org/Vol-3106/Paper_6.pdf
IoT Control Systems base on Fuzzy PWM-controller
Roman Ponomarenko and Anastasiia Demchuk
Taras Shevchenko National University of Kyiv, Volodymyrska str.,60, Kyiv, 01033, Ukraine
Abstract
The paper investigates the use of fuzzy data converters based on intelligent systems of fuzzy
inference in the Internet of Things systems. An approach has been developed for converting
fuzzy input parameters read from sensors into a PWM signal based on the Takagi-Sugeno
fuzzy algorithm. Based on the suggested method an IoT-based smart fan was developed to
demonstrate the characteristics of the proposed method for intelligent control of Internet of
Things devices that support a PWM signal. Finally, the findings of the experimental data
were utilized to simulate the performance of the fuzzy model, using three parameters:
temperature, relative humidity, and carbon dioxide concentrations (CO2) vis-à-vis the PWM
signal output. The proposed method shows the simplicity of training a smart fan control
system and makes the possibility of efficient energy consumption.
Keywords 1
Control systems, fuzzy inference, Internet of Things, fuzzy PWM-controller, Smart fan.
1. Introduction
Nowadays intelligent systems continue to rapidly flood the world. Although IoT technology is still
thought to be about smart homes and greenhouses, actually an IoT system can be any object that
contains sensors, software, or other technologies to connect and communicate with other objects over
the Internet. So, the range of such objects is huge - from ordinary household items to complex
industrial tools. According to Oracle, there are already more than 7 billion devices connected to the
IoT, and that number continues to grow. And by 2025, experts predict that this number will cross the
mark of 22 billion devices [1].
Among the devices connected to the IoT, which are quite popular in household use, are smart
ventilation systems. It is quite an outdated notion that fans may be inefficient in comparison with air
conditioners. However, the ceiling fans are much more healthier and energy-efficient. Additionally,
they constantly improve and become smarter allowing remote control by your smartphone. For
instance, Google and Amazon successfully created a line of smart fans which work with voice
assistants and can be controlled remotely from the smartphone through the Internet. They allow users
to control the temperature and humidity of the air while away from home, as well as customize
climate control to their liking. Therefore, today there is a great need to develop and improve effective
methods of intelligent control for IoT ventilation systems. One of the methodologies for the
intellectualization of control systems is the development of fuzzy inference (FIS) systems, which are
based on fuzzy logic, the founder of which is L. Zade [2]. It is to FIS-systems will be the main
attention in this work in the development of intelligent ventilation systems.
It should also be noted that such systems, in addition to the higher identified advantages, have
disadvantages, one of which is energy consumption [3–5]. Heating, ventilation, and air-conditioning
systems predominate in energy usage in commercial buildings. The numbers variate between 40
percent and 70 percent of the total building electricity consumption [6]. The International Energy
Agency predicts that the demand for rooms with cooling systems will increase three-fold between
II International Scientific Symposium «Intelligent Solutions» IntSol-2021, September 28–30, 2021, Kyiv-Uzhhorod, Ukraine
EMAIL: ponomarenkoroman@knu.ua (R. Ponomarenko); demchuka@fit.knu.ua (A. Demchuk)
ORCID: 0000-0001-9681-2297 (R. Ponomarenko); 0000-0002-8450-0672 (A. Demchuk)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
55
2010 and 2050 [7]. Therefore, the development of IoT-based platforms in the field of smart
ventilation needs to be improved in terms of resource consumption. We must develop new techniques
for improving the energy efficiency of ventilation systems to achieve the reduction in overall building
energy consumption and as a result decrease operating electricity costs.
IoT smart fan systems still rely on the traditional methods of control strategies such as cycling or
on/off control, staging, modulation, or proportional control [8]. These may be quite obsolete,
inaccurate, and inefficient for energy consumption in smart fan systems. The novel idea is to apply
fuzzy logic in early IoT-based fans to assess the environmental factors like temperature, humidity,
carbon dioxide concentrations, etc and convert them by Takagi-Sugeno [9] algorithm into Pulse width
modulation (PWM) signal to control the speed of motors, heat output of heaters, and the other
parameters in an energy-efficient and quieter manner [10]. Thus, the proposed smart inverter fan
based on fuzzy logic will provide comfortable levels of cooling and optimized electricity
consumption.
The main contribution of the paper is:
To consider the Takagi-Sugeno algorithm for input data conversion in IoT-based fans with
a fuzzy expert system.
To propose the use of Takagi-Sugeno systems for converting input data from sensors into
an output PWM signal for efficient energy consumption in smart ventilation systems.
The experimental results demonstrate dependencies between input and output variables of
a smart fan controller based on the fuzzy converter, which transforms quality indicators
into PWM signal, which assume its high performance and cost-effectiveness.
The paper is structured as follows: Section 1 introduces the existing methods of control strategies
in smart ventilation systems and describes the energy consumption problem. Section 2 introduces the
fundamentals of fuzzy control systems and describes the Takagi-Sugeno algorithm. In Section 3,
Takagi-Sugeno system for converting output data from sensors into an output PWM signal is
proposed to provide efficient energy consumption in smart air ventilation systems. In Section 4, the
experiment results are demonstrated. Finally, Section 5 concludes the research paper.
2. Fundamentals of fuzzy control systems
Intelligent fuzzy systems are actively used to solve a wide class of problems in many areas of
industry and life (medicine, production, safety, management, etc.). Belonging to the class of
intellectual, fuzzy systems are designed to solve highly specialized tasks of the creative direction.
Such tasks may include decision-making systems, expert systems, artificial intelligence systems,
testing, assessment, classification systems, etc. [11-16].
2.1. Fuzzy inference
Control systems based on fuzzy converters of input data into output data are used in cases where
the following features take place:
The system operates with qualitative values and characteristics
There is incomplete data about the modeled object and its environment
The investigated object is extremely difficult to model and find ideal solutions
There is a non-linear dependence of the input-output data
Decision making by the system is based on the knowledge and experience of experts in a
specific problem area
Figure 1 shows a general scheme of a fuzzy inference system, which is equally suitable for all
fuzzy algorithms [11] (Takagi-Sugeno, Mamdani, Larsen ...). As can be seen in the figure, many input
values 𝑋 = {𝑥𝑖 : 𝑖 = ̅̅̅̅̅
1, 𝑛} are converted into a set of output conclusions 𝑌 = {𝑦 𝑗 : 𝑗 = ̅̅̅̅̅̅
1, 𝑚 }, using an
inference algorithm on a fuzzy knowledge base. The knowledge base, typical for any fuzzy inference
algorithm, consists of blocks of fuzzy rules 𝐵 = {𝑅𝑤 , 𝑤 = ̅̅̅̅̅
1, 𝐿} products of IF-THEN statements (1).
𝑅𝑤 : 𝐼𝐹 x 𝑖𝑠 𝐴 𝑇𝐻𝐸𝑁 y 𝑖𝑠 𝐵, (1)
56
where x ⊆ X, y ⊆ Y, 𝐴 – input linguistic variable, 𝐵 – output linguistic variable respectively.
Fuzzy inference algorithms may differ in the way
(presence) of defuzzification
𝑥1 𝑦1
Fuzzy
… Fuzzy Rule …
Inference
Base
𝑥𝑛 Algorithm
𝑦𝑚
Depending on the fuzzy inference algorithm, fuzzy
production rules can have a different structure (the
consequent can be a fuzzy value or a precise number)
Figure 1: The general scheme of fuzzy data converter
In general, the functioning of fuzzy systems consists of the following stages [11]:
1. Fuzzification of input variables
2. Activation of fuzzy production rules
3. Aggregation of rule subconclusions (in consequent)
4. Accumulation of sub-conclusions of the consequent of fuzzy rules (carried out only for those
systems, the consequents of which are fuzzy values)
5. Defuzzification of output values (or a procedure similar to defuzzification, if the consequents
of the rules are clear numbers)
Let us take a closer look at the fuzzy logical conclusion of Takagi-Sugeno [9], since for research in
this work it is of the greatest interest in the context of fuzzy data converters in IoT systems. The main
feature of Takagi-Sugeno systems is the ability to transform qualitative indicators (fuzzified values)
into quantitative ones since the subconclusions of fuzzy rules consist of functional dependencies on a
set of input data (primary, non-fuzzified) and generate precise numbers.
The Takagi-Sugeno fuzzy rule is:
𝑛
(2)
𝑗 𝑗 𝑗 𝑗 𝑗
𝑅𝑤 : 𝐼𝐹 x 𝑖𝑠 𝐴𝑤 𝑇𝐻𝐸𝑁 𝑦𝑤 = 𝑘𝑤0 + ∑ 𝑘𝑤𝑖 𝑥𝑖 ,
𝑖=1
where j – system-generated output number, w – fuzzy rule number, n – number of input variables,
𝒋
𝒌𝒘𝟎 – free coefficient. The consequent of a rule is essentially a weighted summation of non-fuzzified
input prerequisites.
An output conclusion for every 𝑦𝑗 (see Figure 1) according to the Takagi-Sugeno algorithm, it is
the finding of the weighted average of rule subconclusions in a fuzzy knowledge base (3):
𝑗 𝑗 𝑗 𝑗 𝑗 (3)
𝑗
∑𝐿𝑤=1[⋀𝑛𝑖=1 𝜇𝑤 (𝑥𝑖 )](𝑘𝑤0 + ∑𝑛𝑖=1 𝑘𝑤𝑖 𝑥𝑖 ) ∑𝐿𝑤=1 𝜇𝑤 (x)𝑦𝑤
𝑦 = 𝑗 = 𝑗 ,
∑𝐿𝑤=1[⋀𝑛𝑖=1 𝜇𝑤 (𝑥𝑖 )] ∑𝐿𝑤=1 𝜇𝑤 (x)
where ⋀ − is the operation of taking the minimum, 𝜇(х) – is the membership function of the input
value to a fuzzy term.
57
3. Control of the output PWM signal in IoT systems based on intelligent fuzzy
converters (fuzzy PWM-controller)
PWM-signal [10, 17] (pulse width modulation) – one of the varieties of a digital signal and is
designed to control the power (speed) on output devices based on periodic on / off voltage on the port
(Figure 2). One of the tasks related to the operation of the microcontroller in IoT systems is to control
the PWM signal, and often its value depends on the input parameters read from other sensors
(temperature, smoke sensor, proximity, etc.).
The total power consumption at the PWM output is calculated as:
𝐸𝑟 + 𝐸𝑑𝑟 + 𝐸𝑓 + 𝐸𝑟𝑐
𝑃𝑜𝑢𝑡 = , (4)
𝑇𝑝𝑤𝑚
where 𝐸𝑟 – the energy consumption during the time of transition from Low to High mode, 𝐸𝑑𝑟 –
the energy consumption during the current supply time to maintain High mode, 𝐸𝑓 – the energy
consumption during the time of transition from High to Low mode, 𝐸𝑟𝑐 – the energy consumption
during the time for current regeneration in order to maintain Low mode, 𝑇𝑝𝑤𝑚 – total operating time
of the PWM signal (0..255).
Thus, there is often a non-linear relationship between the input parameters and the PWM output
signal in such systems. It should be noted that the read-out data from the sensors are very often
qualitative indicators and require the introduction of fuzzy linguistic values for their further operation
and classification.
The paper proposes the use of Takagi-Sugeno systems for converting input data from sensors into
an output PWM signal. A characteristic feature of this approach is the ability to convert input data
from several sensors, realizing a nonlinear input-output dependence (a model of a converter of quality
data from sensors into a PWM signal is shown in Figure 3). This approach allows the implementation
of intelligent IoT systems with high energy savings due to the use of PWM as a control signal.
Signal
255 (𝑇𝑝𝑤𝑚 ) 255 255
HIGH
LOW
t (mc)
Figure 2: Scheme of the digital PWM output signal
In Figure 3, the input parameters are represented by a vector of fuzzy (fuzzified) values, which are
qualitative characteristics read from the sensors:
𝑋̃ = {𝑥
̃𝑖 , 𝑖 = ̅̅̅̅̅
1, 𝑛},
𝑥𝑖 𝑥𝑖 𝑥𝑖
𝑥̃𝑖 = { + +⋯+ },
𝜇𝑡𝑒𝑟𝑚 1 (𝑥𝑖 ) 𝜇𝑡𝑒𝑟𝑚 2 (𝑥𝑖 ) 𝜇𝑡𝑒𝑟𝑚 𝑄 (𝑥𝑖 )
where + is a union operation, 𝑋̃ – vector of fuzzy input values.
Subconclusions of fuzzy rules (Takagi-Sugeno fuzzy rules), form the weighted values of the PWM
output signal from 0 to 255, which then go through the procedure for finding the weighted average
value, which, accordingly, will also lie in the range [0..255].
58
𝑥1
ൗ𝜇(𝑥 )
1
𝑦 1 = 𝑘01 + 𝑘11 𝑥1 + ⋯ + 𝑘𝑛1 𝑥𝑛 [0..255] Weighted
𝑥2 average
ൗ𝜇(𝑥 ) 𝑦 2 = 𝑘02 + 𝑘12 𝑥1 + ⋯ + 𝑘𝑛2 𝑥𝑛 [0..255]
2 PWM-
𝑦 3
= 𝑘03 + 𝑘13 𝑥1 + ⋯ + 𝑘𝑛3 𝑥𝑛 [0..255] ∑𝐿𝑖=1 𝜇𝑖 (x)𝑦 𝑖
value
∑𝐿𝑖=1 𝜇 𝑖 (x) [0..255]
… …
𝑥𝑛 𝑦 𝐿 = 𝑘0𝐿 + 𝑘1𝐿 𝑥1 + ⋯ + 𝑘𝑛𝐿 𝑥𝑛 [0..255]
ൗ𝜇(𝑥 )
𝑛
Figure 3: Model of the converter of fuzzy (quality) data from sensors to a PWM signal (range of
values [0..255])
4. Smart fan based on the fuzzy PWM-controller for convert of quality
indicators into PWM signal
4.1. Problem definition of smart ventilation systems
In this paper, a model of an intelligent fan based on fuzzy control has been developed using the
proposed in this article method of fuzzy transformation of quality indicators into a PWM signal. The
use of a fuzzy controller allows the ventilation system to work in different modes of operation,
adjusting to the external environment, and also taking into account the individual requirements of the
customer, the climate of a particular region, medical contraindications, etc. Based on a fuzzy
knowledge base, this system makes it possible to formalize the customer's requirements in the form of
an expert set of rules, makes it possible to make the operating modes more varied.
It is also worth mentioning that efficient energy consumption, sustainability, environment-friendly
– terms that should be taken into consideration in the modern scientific world. The heating,
ventilation, and air conditioning systems can be the largest energy consumers in the building. The
different approaches to modeling these systems and providing them with additional controllers can
change the situation for the better.
The most common methods to solve the above problems are the classic use of proportional-
integral-differential (PID) controllers and Computational Intelligence techniques [18].
The advantage of using intelligent control methods in ventilation systems over the classic ones is
the ability to regulate the room temperature at partial load, minimize system steady-state error. In
recent studies [19–20] there was an attempt to use fuzzy logic to model the cooling process of
ventilation systems. However, existing systems do not take into account that the resistance of
transistors and resistors also leads to additional energy loss, because they burn part of it as a heat.
Such systems are constantly running at full speed, making a lot of noise and consuming a lot of
energy. The solution may be to use PWM, which varies the speed of the motors of the devices, so they
consume only as much energy as they need [10].
4.2. The architectural design of the Smart ventilation system
Basic fuzzy-based architectural model of smart ventilation system consists of (Figure 4):
1. Sensor components:
59
1.1. Sensor temperature
1.2. Sensor relative humidity
1.3. Gas sensor
2. Micro Controller Unit – intermediate component, which receives transmitted information
from the sensors for processing of the collected data
3. Cloud API
Sensor Micro Controller Cloud
temperature Unit API
Sensor relative
humidity
Fuzzy Inference
System Remote control
Gas sensor
Figure 4:Block diagram of the fuzzy-based smart ventilation system
4.3. Fuzzy Smart Fan Controller Model
To model the edge values (terms) of linguistic variables, we use sigmoidal membership functions:
1
𝜇(𝑥) = , (5)
1 + exp[−𝑏(𝑥 − 𝑐)]
where 𝑏 – the number characterizing the slope of the graph (the larger the 𝑏, the greater the slope), 𝑐 –
inflection point of the function (𝜇 (𝑐) = 0,5).
Furthermore, we will use the bell-shaped membership functions to model the mean values of fuzzy
terms:
1
𝜇(𝑥) = , (6)
|𝑥 − 𝑐|2𝑏
1+ 𝛼
where c – central (modal) value at which 𝜇(𝑐) = 1, 𝑏 ≥ 0 – number characterizing the slope of the
graph (similar to sigmoidal membership functions), 𝛼 > 0 – the distance from the center c to the
inflection points of the function, where at 𝑏 = 0,5 the 𝜇(𝑐 ± 𝛼 ) = 0,5 is fulfilled.
Methods for constructing fuzzy membership functions are considered in [21].
Linguistic variable "temperature" and its membership function graph (Figure 5):
1 1 1
𝑇 = {𝜇𝐿𝑂𝑊 (𝑥) = + 𝜇𝑀𝐸𝐷𝐼𝑈𝑀 (𝑥) = 2 + 𝜇𝐻𝐼𝐺𝐻 (𝑥) = }.
1+𝑒 0,5𝑥−6,25 |𝑥 − 25| 1 + 𝑒 −0,5𝑥+18,75
1+ 30
60
𝜇(𝑡)
1
0,5
LOW MEDIUM HIGH
0
0 12,5 25 37,5 50 𝑡℃
Figure 5: Membership functions of the linguistic variable "temperature"
Linguistic variable "carbon dioxide concentration" and its membership function graph (Figure 6):
1 1 1
𝐶𝑂2 = {𝜇𝐸𝑋 (𝑥) = + 𝜇𝐺𝑂𝑂𝐷 (𝑥) = + 𝜇𝐻𝐸𝐴𝑉𝑌 (𝑥) = }.
1 + 𝑒 0,03𝑥−24 |𝑥 − 850|1,5 1 + 𝑒 −0,05𝑥+50
1+ 1000
𝜇(𝑝𝑝𝑚)
1
0,5
HEAVY
0
400 700 1000 1300 1600 𝐶𝑂2(𝑝𝑝𝑚)
Figure 6: Membership functions of the linguistic variable "carbon dioxide concentration"
Linguistic variable "relative humidity" and its membership function graph (Figure 7):
1 1
𝑅𝐻 = {𝜇𝐿𝑂𝑊 (𝑥) = 0,1𝑥−5
+ 𝜇𝐻𝐼𝐺𝐻 (𝑥) = −0,1𝑥+5
}.
1+𝑒 1+𝑒
Table 1 presents a block of Takagi-Sugeno fuzzy production rules for controlling fan power
through a PWM signal, consisting of 18 fuzzy rules. It is also important to note that the maximum
number of rules of a fuzzy system can be found according to (7).
𝑁
𝑅𝑢𝑙𝑒𝑀𝑎𝑥𝑁𝑢𝑚 = ∏ 𝑡𝑒𝑟𝑚𝑠(𝐿𝑖 ),
𝑖=1 (7)
61
where 𝑡𝑒𝑟𝑚𝑠 – returns the number of fuzzy terms of a linguistic variable 𝐿𝑖 , 𝑁 – number of input
variables.
𝜇(𝑅𝐻)
1
0,5
LOW HIGH
0
0 25 50 75 100 𝑅𝐻(%)
Figure 7: Membership functions of the linguistic variable "relative humidity"
Table 1
Takagi-Sugeno fuzzy rule block for Smart fan control
𝑅𝑖 𝑥1 (𝑡℃) 𝑥2 (𝑝𝑝𝑚) 𝑥3 (𝑅𝐻(%) 𝑦𝑖 (𝑃𝑊𝑀)
𝑅1 : LOW EXCELLENT LOW 𝑦1 = 0,5𝑥1 + 0,1𝑥2 + 1,5𝑥3
𝑅2 : LOW EXCELLENT HIGH 𝑦2 = 0,25𝑥1 + 0,1𝑥2 − 0,15𝑥3
𝑅3 : LOW GOOD LOW 𝑦3 = 0,6𝑥1 + 0,15𝑥2 + 1,7𝑥3
𝑅4 : LOW GOOD HIGH 𝑦4 = 0,5𝑥1 + 0,12𝑥2 + 0,2𝑥3
𝑅5 : LOW HEAVY LOW 𝑦5 = 0,7𝑥1 + 0,1𝑥2 + 1,6𝑥3
𝑅6 : LOW HEAVY HIGH 𝑦6 = 0,5𝑥1 + 0,1𝑥2 + 0,7𝑥3
𝑅7 : MEDIUM EXCELLENT LOW 𝑦7 = 0,6𝑥1 + 0,15𝑥2 + 1,4𝑥3
𝑅8 : MEDIUM EXCELLENT HIGH 𝑦8 = 0,3𝑥1 + 0,12𝑥2 − 0,1𝑥3
𝑅9 : MEDIUM GOOD LOW 𝑦9 = 0,7𝑥1 + 0,17𝑥2 + 1,7𝑥3
𝑅10 : MEDIUM GOOD HIGH 𝑦10 = 0,4𝑥1 + 0,14𝑥2 + 0,3𝑥3
𝑅11 : MEDIUM HEAVY LOW 𝑦11 = 0,7𝑥1 + 0,11𝑥2 + 1,8𝑥3
𝑅12 : MEDIUM HEAVY HIGH 𝑦12 = 0,8𝑥1 + 0,1𝑥2 + 0,9𝑥3
𝑅13 : HIGH EXCELLENT LOW 𝑦13 = 0,8𝑥1 + 0,2𝑥2 + 1,7𝑥3
𝑅14 : HIGH EXCELLENT HIGH 𝑦14 = 0,7𝑥1 + 0,15𝑥2 + 0,8𝑥3
𝑅15 : HIGH GOOD LOW 𝑦15 = 0,9𝑥1 + 0,14𝑥2 + 2𝑥3
𝑅16 : HIGH GOOD HIGH 𝑦16 = 0,7𝑥1 + 0,14𝑥2 + 0,5𝑥3
𝑅17 : HIGH HEAVY LOW 𝑦17 = 𝑥1 + 0,11𝑥2 + 1,7𝑥3
𝑅18 : HIGH HEAVY HIGH 𝑦18 = 0,95𝑥1 + 0,11𝑥2 + 0,34𝑥3
4.4. Simulation Experimental Setup
Table 2 and Figure 8 show the experimental results obtained during the simulation of the
intelligent fuzzy Smart Fan system, which converts the output metrics from the sensors into a PWM
62
signal in the range from 0 to 255. Among the advantages of this system, it is worth to mention the
high operating speed (no defuzzification), as well as the simplicity of training the system, which
essentially boils down to finding the weights of the input parameters in the consequent of fuzzy rules
and which, if necessary, can also be produced by a subject matter expert.
Table 2
Experimental results of operation of the Smart Fan fuzzy control system
№ 𝑡℃ 𝑝𝑝𝑚 𝑅𝐻 (%) 𝑃𝑊𝑀
1 23 400 70 77
2 28 820 40 208
3 14 1200 85 198
4 37 940 29 203
5 40 730 50 201
6 15 570 65 130
7 25 1000 90 195
8 30 1300 80 234
9 42 700 45 198
10 21 1400 33 234
Figure 8: Graph of dependencies between input and output variables of a fuzzy Smart fan controller
5. Conclusions
A fuzzy PWM controller based on the Takagi-Sugeno fuzzy inference algorithm is proposed. In
this paper, a method of fuzzy control of the output PWM signal based on the Takagi-Sugeno fuzzy
inference algorithm is presented. An IoT system of an intelligent fan has been developed based on the
approach of converting fuzzy input parameters read from sensors into a PWM signal to control the fan
screw rotation speed. A mathematical model of an intelligent fan based on fuzzy control has been
developed, as well as its hardware architecture. In this approach, the model operates with three input
parameters, namely, temperature, relative humidity and carbon dioxide concentrations (CO2) under
different operating conditions vis-à-vis the PWM signal output. Experimental studies have been
carried out that demonstrate the characteristics of the proposed methods for intelligent control of
Internet of Things devices that support a PWM signal, using the example of a Smart Fan.
6. References
[1] Oracle - What Is the Internet of Things (IoT)? 2021. URL: https://www.oracle.com/internet-of-
things/what-is-iot/
63
[2] L. Zadeh, The concept of a linguistic variable and its application to approximate reasoning.
American Elsevier Publishing Company, 1973.
[3] Air Conditioning. URL: https://www.energy.gov/energysaver/home-cooling-systems/air-
conditioning
[4] Air conditioning in nearly 100 million U.S. homes, 2011. URL:
https://www.eia.gov/consumption/residential/reports/2009/air-conditioning.php
[5] D. Westphalen and S. Koszalinski. Energy Consumption Characteristics of Commercial Building
HVAC Systems. Chillers, Refrigerant Compressors, and Heating Systems 1 (2001)
[6] N. Bhagwat, S.N. Teli, P. Gunaki and V.S. Majali. Review paper on energy efficiency
technologies for heating, ventilation and air conditioning (HVAC). International Journal of
Scientific & Engineering Research 6 (12) (2015) 106–116.
[7] IoT and Machine Learning to Reduce Energy Use in Cooling Systems, 2018. URL:
https://www.ibm.com/blogs/research/2018/07/reduce-energy-cooling/
[8] Fundamentals of HVAC Controls. URL: http://people.eecs.berkeley.edu/~culler/cs294-
f09/m197content.pdf
[9] Takagi T. and Sugeno M. “Fuzzy identification of systems and its application to modeling and
control.” IEEE Trans. of Systems, Man and Cybernetics 15(1) (1985): 116–132.
[10] N. Brown. Introduction To PWM: How Pulse Width Modulation Works, 2021. URL:
https://www.kompulsa.com/introduction-pwm-pulse-width-modulation-works/
[11] F. M. McNeill and E. Thro (1994) Fuzzy logic. A practical approach. - Academic press. - 312 p.
[12] S. Yershov and R. Ponomarenko, Software architecture of hierarchical fuzzy inference.
International Conference of Programming – UkrPROG'2018. In: CEUR Workshop Proceedings,
CEUR-WS.org, 2–3, 2018, pp. 99–108.
[13] S. Yershov and R. Ponomarenko, Parallel Fuzzy Inference Method for Higher Order Takagi–
Sugeno Systems. Cybernetics and Systems Analysis 54(6) (2018) 170–180.
[14] R. Ponomarenko. Systems for checking and testing the quality of knowledge based on fuzzy
inference. CEUR Workshop Proceedings (CEUR-WS.org) “VII International conference
“Information Technology and Interactions” (IT&I-2020) December 02-04”, 2021, 65-74, ISSN
1613-0073. URL: http://ceur-ws.org/Vol-2845/Paper_7.pdf
[15] P. Lizunov, A. Biloshchytskyi, A. Kuchansky, Y. Andrashko and S. Biloshchytska. Improvement
of the method for scientific publications clustering based on N-gram analysis and fuzzy method
for selecting research partners. Eastern-European Journal of Enterprise Technologies. 4(4-100)
(2019) 6–14 doi: 10.15587/1729-4061.2019.175139
[16] N. Baccar and R. Bouallegue. (2016) Interval type 2 fuzzy localization for wireless sensor
networks. EURASIP J. Adv. Signal Process. 2016, 42 doi: 10.1186/s13634-016-0340-4
[17] A. Patel, A.R. Patel, D.R. Vyas and K.M. Patel. Use of PWM Techniques for Power Quality
Improvement. International Journal of Recent Trends in Engineering 1(4) (2009) 99–102.
[18] A.-H. Attia, S.F. Rezeka and A.M. Saleh. Fuzzy logic control of air-conditioning system in
residential buildings. Alexandria Engineering Journal 54 (3) (2015) 395–403. doi:
10.1016/j.aej.2015.03.023
[19] S.K. Dash, G. Mohanty and A. Mohanty. Intelligent Air Conditioning System using Fuzzy Logic.
International journal of scientific and engineering research. 3 (12) (2012) 1–6.
[20] M. Fayaz and D.-H. Kim. An efficient actuator control mechanism using fuzzy logic on
embedded system. International Journal of Control and Automation 11(6) (2018) 35-44 doi:
10.14257/ijca.2018.11.6.04
[21] A. Sancho-Royo and J.L. Verdegay. Methods for the Construction of Membership Functions.
International Journal of Intelligent Systems 14 (12) (1999) 1213–1230 doi:10.1002/(SICI)1098-
111X(199912)14:123.0.CO;2-5
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