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Journal articles on the topic 'Fuzzy logic; HVAC control systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Hussain, Abadal Salam T., F. Malek, S. Faiz Ahmed, et al. "Operational Optimization of High Voltage Power Station Based Fuzzy Logic Intelligent Controller." Applied Mechanics and Materials 793 (September 2015): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.793.100.

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This paper discusses the use of the intelligent microcontroller and also discusses the results from the simulation application of fuzzy logic theory to the control of the high voltage direct and alternation current (HVDC)& (HVAC) power station systems. The application considered their implementation in both low and high level control systems in HVDC& HVAC power station systems. The results for the fuzzy logic based controller shows many improvements compared to the conventional HVDC& HVAC control system. The fuzzy logic based controller concept was further successfully extended to

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2

Morales Escobar, L., J. Aguilar, Alberto Garces-Jimenez, Jose Antonio Gutierrez De Mesa, and Jose Manuel Gomez-Pulido. "Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings." IEEE Access 8 (2020): 16111–26. http://dx.doi.org/10.1109/access.2020.2966545.

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3

Rahman, Sam Matiur, Mohammad Fazle Rabbi, Omar Altwijri, et al. "Fuzzy logic-based improved ventilation system for the pharmaceutical industry." International Journal of Engineering & Technology 7, no. 2 (2018): 640. http://dx.doi.org/10.14419/ijet.v7i2.9985.

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Indoor air quality in pharmaceutical industry plays a vital role in the production and storing of medicine. Stable indoor environment including favorable temperature, humidity, air flow and number of microorganisms requires consistent monitoring. This paper aimed to develop a fuzzy logic-based intelligent ventilation system to control the indoor air quality in pharmaceutical sites. Specifically, in the proposed fuzzy inference system, the ventilation system can control the air flow and quality in accordance with the indoor temperature, humidity, air flow and microorganisms in the air. The MATL

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4

Ayaz, Murat, Volkan Aygül, Ferhat Düzenli˙, and Erkutay Tasdemi˙rci˙. "Comparative Study on Control Methods for Air Conditioning of Industrial Paint Booths." Advanced Science, Engineering and Medicine 11, no. 11 (2019): 1053–59. http://dx.doi.org/10.1166/asem.2019.2454.

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It is of great importance that each product in industrial production facilities is to be produced in the same quality and standard. Especially in the automotive industry, the painting process needs to be done under certain environmental conditions according to the paint properties used. Therefore, the temperature, humidity and air quality values of the paint booth are very important for a quality painting operation. In this study, adaptive control has been proposed to control of one-zone heating-ventilation system for the paint booths. The system has been modelled by using the Matlab/Simulink.

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5

Abdo-Allah, Almahdi, Tariq Iqbal, and Kevin Pope. "Modeling, Analysis, and Design of a Fuzzy Logic Controller for an AHU in the S.J. Carew Building at Memorial University." Journal of Energy 2018 (August 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/4540387.

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Proper functioning of heating, ventilation, and air conditioning (HVAC) systems is important for efficient thermal management, as well as operational costs. Most of these systems use nonlinear time variances to handle disturbances, along with controllers that try to balance rise times and stability. The latest generation of fuzzy logic controllers (FLC) is algorithm-based and is used to control indoor temperatures, CO2 concentrations in air handling units (AHUs), and fan speeds. These types of controllers work through the manipulation of dampers, fans, and valves to adjust flow rates of water

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6

Baniyounes, Ali M., Yazeed Yasin Ghadi, Maryam Mahmoud Akho Zahia, Eyad Adwan, and Kalid Oliemat. "Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (2021): 1283. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1283-1292.

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This article is divided into three parts: the first presents a simulation study of the effect of occupancy level on energy usage pattern of Engineering building of Applied Science Private university, Amman, Jordan. The simulation was created on simulation mechanism by means of EnergyPlus software and improved by using the building’s data such as building’s as built plan, occupant’s density level based on data about who utilize the building throughout operational hours, energy usage level, Heating Ventilating and air conditioning (HVAC) system, lighting and its control systems and etc. Data reg

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7

Rathore, Dhanvanti, and N. K. Singh. "A New Fuzzy Based UPFC Topology for Active Power Enhancement in an offshore Wind Farm." SMART MOVES JOURNAL IJOSCIENCE 7, no. 1 (2021): 1–10. http://dx.doi.org/10.24113/ijoscience.v7i1.335.

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The stability of a power system is the ability of a power system to restore an operating state of equilibrium for a given initial operating condition after it has been subjected to a physical disturbance, most of the variables of the system being limited so that almost the entire system remains intact. To create a MATLAB SIMULINK model of odd shore wind energy system having power being transmitted through DC transmission system. The first model will have no power flow controller and second model will have artificial intelligence based contr

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8

Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. I." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 404–18. http://dx.doi.org/10.1109/21.52551.

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9

Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. II." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 419–35. http://dx.doi.org/10.1109/21.52552.

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10

Ihara, H. "Fuzzy Logic for Control Systems." IFAC Proceedings Volumes 25, no. 22 (1992): 251–55. http://dx.doi.org/10.1016/s1474-6670(17)49658-3.

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11

RAGOT, JOSÉ, and MICHEL LAMOTTE. "Fuzzy logic control." International Journal of Systems Science 24, no. 10 (1993): 1825–48. http://dx.doi.org/10.1080/00207729308949598.

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12

Piskunov, Alexandre. "Fuzzy implication in fuzzy systems control." Fuzzy Sets and Systems 45, no. 1 (1992): 25–35. http://dx.doi.org/10.1016/0165-0114(92)90088-l.

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13

Chen,, Guanrong, Trung Tat Pham,, and NM Boustany,. "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems." Applied Mechanics Reviews 54, no. 6 (2001): B102—B103. http://dx.doi.org/10.1115/1.1421114.

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14

Ganchev, Ivan, Albena Taneva, Krum Kutryanski, and Michail Petrov. "Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems." IFAC-PapersOnLine 52, no. 25 (2019): 299–304. http://dx.doi.org/10.1016/j.ifacol.2019.12.539.

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15

HE, M., W. CAI, and S. LI. "Multiple fuzzy model-based temperature predictive control for HVAC systems." Information Sciences 169, no. 1-2 (2005): 155–74. http://dx.doi.org/10.1016/j.ins.2004.02.016.

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16

Khooban, Mohammad Hassan, Davood Nazari Maryam Abadi, Alireza Alfi, and Mehdi Siahi. "Optimal Type-2 Fuzzy Controller For HVAC Systems." Automatika 55, no. 1 (2014): 69–78. http://dx.doi.org/10.7305/automatika.2014.01.219.

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17

Alcalá, Rafael, Jesús Alcalá-Fdez, María José Gacto, and Francisco Herrera. "Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems." Applied Intelligence 31, no. 1 (2007): 15–30. http://dx.doi.org/10.1007/s10489-007-0107-6.

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18

Zhao, Jing, and Yu Shan. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System." Energies 13, no. 3 (2020): 530. http://dx.doi.org/10.3390/en13030530.

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The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the

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19

Kohn-Rich, Sylvia, and Henryk Flashner. "Robust fuzzy logic control of mechanical systems." Fuzzy Sets and Systems 133, no. 1 (2003): 77–108. http://dx.doi.org/10.1016/s0165-0114(02)00212-9.

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20

Camilleri, Flavia, and Reza Katebi. "FUZZY LOGIC CONTROL OF INTEGRATED WASTEWATER SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 161–66. http://dx.doi.org/10.3182/20050703-6-cz-1902.02198.

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21

., M. Shaharurrizal B. M. W., Farrah D. Herman ., A. Arunagiri ., and Stella Morris . "Fuzzy Logic Simulation to Process Control Systems." Information Technology Journal 1, no. 3 (2002): 272–79. http://dx.doi.org/10.3923/itj.2002.272.279.

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22

Lin, Chih-Min, Yi-Jen Mon, and Jiann-Hwa Maa. "ECOLOGICAL SYSTEMS CONTROL BY FUZZY LOGIC CONTROLLER." Asian Journal of Control 2, no. 4 (2008): 274–80. http://dx.doi.org/10.1111/j.1934-6093.2000.tb00032.x.

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23

Pelc, Mariusz. "Context-aware Fuzzy Control Systems." International Journal of Software Engineering and Knowledge Engineering 24, no. 05 (2014): 825–56. http://dx.doi.org/10.1142/s0218194014500326.

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In this paper an example of a hierarchical context-aware run-time reconfigurable control system is presented. The context-awareness is resulting from using policy-based computing as a technology allowing the control system to replace its decision making logic in run-time in response to changing environment conditions. The proposed solution allows system experts to specify policies (AGILE policies) used in the Supervision Layer for the purpose of making decisions regarding the most appropriate controller configuration and on the other side, they can specify policies (Fuzzy Logic policies) used

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24

Gering, Stefan, and Jürgen Adamy. "Fuzzy control of continuous-time recurrent fuzzy systems." Fuzzy Sets and Systems 254 (November 2014): 126–41. http://dx.doi.org/10.1016/j.fss.2014.02.001.

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25

TRILLAS, E. "ON LOGIC AND FUZZY LOGIC." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, no. 02 (1993): 107–37. http://dx.doi.org/10.1142/s0218488593000073.

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This paper mainly consists of a review of some basic tools of Inexact Inference, its reduction to classical logic and its cautious use of Fuzzy Logic. Those tools are the concept of Conditional Relation, its greatest case of Material Conditional and the concept of Logical-States as possible worlds of "true" elements. Some recent results characterizing Monotonic Preorders are also introduced, in both the Classical and Fuzzy cases. Everything lies on the semantic level of Logic and is presented in a naive mathematical style.

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26

Hussain, Sajid, Hossam A. Gabbar, Daniel Bondarenko, Farayi Musharavati, and Shaligram Pokharel. "Comfort-based fuzzy control optimization for energy conservation in HVAC systems." Control Engineering Practice 32 (November 2014): 172–82. http://dx.doi.org/10.1016/j.conengprac.2014.08.007.

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27

Dounis. "Special Issue “Intelligent Control in Energy Systems”." Energies 12, no. 15 (2019): 3017. http://dx.doi.org/10.3390/en12153017.

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The editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic),

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28

Mohammadian, Masoud. "Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems." International Journal of Fuzzy System Applications 6, no. 3 (2017): 105–23. http://dx.doi.org/10.4018/ijfsa.2017070105.

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Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems us

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29

Kohn-Rich, Sylvia, and Henryk Flashner. "Robust fuzzy logic tracking control of mechanical systems." Journal of the Franklin Institute 338, no. 2-3 (2001): 353–70. http://dx.doi.org/10.1016/s0016-0032(00)00093-4.

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30

Vachtsevanos, George. "Large-scale systems: modeling, control, and fuzzy logic." Automatica 37, no. 9 (2001): 1500–1502. http://dx.doi.org/10.1016/s0005-1098(01)00108-x.

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31

Porter, B., and H. Moi. "Input-Decoupling Fuzzy-Logic Control of Manufacturing Systems." IFAC Proceedings Volumes 30, no. 6 (1997): 1337–42. http://dx.doi.org/10.1016/s1474-6670(17)43547-6.

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32

Castillo, Oscar, Leticia Amador-Angulo, Juan R. Castro, and Mario Garcia-Valdez. "A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems." Information Sciences 354 (August 2016): 257–74. http://dx.doi.org/10.1016/j.ins.2016.03.026.

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33

Liu, Huaping, Fuchun Sun, and Yenan Hu. "control for fuzzy singularly perturbed systems." Fuzzy Sets and Systems 155, no. 2 (2005): 272–91. http://dx.doi.org/10.1016/j.fss.2005.05.004.

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34

Sala, Antonio, Thierry Marie Guerra, and Robert Babuška. "Perspectives of fuzzy systems and control." Fuzzy Sets and Systems 156, no. 3 (2005): 432–44. http://dx.doi.org/10.1016/j.fss.2005.05.041.

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35

Lü, Hong, Lei Jia, Shulan Kong, and Zhaosheng Zhang. "Predictive functional control based on fuzzy T-S model for HVAC systems temperature control." Journal of Control Theory and Applications 5, no. 1 (2007): 94–98. http://dx.doi.org/10.1007/s11768-005-5301-7.

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36

LIN, CHENG-JIAN, and CHIN-TENG LIN. "ADAPTIVE FUZZY CONTROL OF UNSTABLE NONLINEAR SYSTEMS." International Journal of Neural Systems 06, no. 03 (1995): 283–98. http://dx.doi.org/10.1142/s0129065795000214.

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This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayer connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on th

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37

Talebi, Ashkan, and Alireza Hatami. "Online fuzzy control of HVAC systems considering demand response and users’ comfort." Energy Sources, Part B: Economics, Planning, and Policy 15, no. 7-9 (2020): 403–22. http://dx.doi.org/10.1080/15567249.2020.1825557.

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38

Chaouch, Haithem, Celal Çeken, and Seçkin Arı. "Energy management of HVAC systems in smart buildings by using fuzzy logic and M2M communication." Journal of Building Engineering 44 (December 2021): 102606. http://dx.doi.org/10.1016/j.jobe.2021.102606.

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39

MARCHALLECK, NICHOLAS, and ABRAHAM KANDEL. "FUZZY LOGIC APPLICATIONS IN TRANSPORTATION SYSTEMS." International Journal on Artificial Intelligence Tools 04, no. 03 (1995): 413–32. http://dx.doi.org/10.1142/s0218213095000206.

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The purpose of this paper is to provide a survey of state of the art fuzzy logic applications in the field of transportation, illustrating the usefulness, and the promising future of the fuzzy approach. The majority of the discussion covers the area of fuzzy control. A wide range of Fuzzy Logic Controllers (FLCs) is discussed, ranging from traffic, to aircraft controllers. Although the majority of applications are to surface transportation, surveys of several aerospace applications are also given.

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40

Nall, L. O., and R. J. Hathaway. "Fuzzy Systems Toolbox, Fuzzy Logic Toolbox [Software Review]." IEEE Transactions on Fuzzy Systems 4, no. 1 (1996): 82. http://dx.doi.org/10.1109/tfuzz.1996.481848.

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41

Feng, G., S. G. Cao, and N. W. Rees. "Stable adaptive control of fuzzy dynamic systems." Fuzzy Sets and Systems 131, no. 2 (2002): 217–24. http://dx.doi.org/10.1016/s0165-0114(01)00236-6.

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42

Cigánek, Ján, Filip Noge, and Štefan Kozák. "Modeling and Control of Mechatronic Systems Using Fuzzy Logic." International Review of Automatic Control (IREACO) 7, no. 1 (2014): 45. http://dx.doi.org/10.15866/ireaco.v7i1.1291.

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43

Kuljača, Ognjen, Sejid Tešnjak, and Zoran Vukić. "Fuzzy Logic Based Control of Isolated Termo Power Systems." IFAC Proceedings Volumes 32, no. 2 (1999): 7306–11. http://dx.doi.org/10.1016/s1474-6670(17)57246-8.

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44

Jagannathan, S., M. W. Vandegrift, and F. L. Lewis. "Adaptive fuzzy logic control of discrete-time dynamical systems." Automatica 36, no. 2 (2000): 229–41. http://dx.doi.org/10.1016/s0005-1098(99)00143-0.

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45

Filev, Dimitar, and Fazal U. Syed. "Applied intelligent systems: blending fuzzy logic with conventional control." International Journal of General Systems 39, no. 4 (2010): 395–414. http://dx.doi.org/10.1080/03081071003696066.

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46

Sobaih, Prof Abdul Azim, and EL-Khatib Kamal. "Fuzzy logic control of nonlinear systems with parametric uncertainties." Menoufia Journal of Electronic Engineering Research 16, no. 2 (2006): 157–70. http://dx.doi.org/10.21608/mjeer.2006.64857.

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47

Xu, Sendren Sheng-Dong, Hao Ying, Pablo Carbonell, Ching-Hung Lee, and Wei-Sheng Wu. "Fuzzy Logic Applications in Control Theory and Systems Biology." Advances in Fuzzy Systems 2013 (2013): 1. http://dx.doi.org/10.1155/2013/504728.

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48

Toumodge, S. "Large-Scale Systems: Modeling, Control, and Fuzzy Logic [Bookshelf]." IEEE Control Systems 18, no. 3 (1998): 84. http://dx.doi.org/10.1109/mcs.1998.687623.

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49

Shi, Xuesen, Yuyao Shen, and Yongqing Wang. "Fuzzy Logic Control for Doppler Search in DSSS Systems." IEEE Transactions on Fuzzy Systems 28, no. 9 (2020): 2232–43. http://dx.doi.org/10.1109/tfuzz.2019.2932676.

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50

ZHANG, JIANWEI. "Applications of fuzzy logic control in autonomous robot systems†." International Journal of Systems Science 24, no. 10 (1993): 1885–904. http://dx.doi.org/10.1080/00207729308949601.

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