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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, Taha A. Taha, Shouket A. Ahmed, Mardianaliza Othman, Muhammad Irwanto Misrun, Gomesh Nair Shasidharan, and Mohd Irwan Yusoff. "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 high level control of optimization problems such as the power swings. Based on simulation results, HVDC and HVAC breaker design are online protection against unwanted incidents happening to the system.

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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, Mahdi Alqahtani, Tasriva Sikandar, Izzeldin Ibrahim Abdelaziz, Md Asraf Ali, and Kenneth Sundaraj. "Fuzzy logic-based improved ventilation system for the pharmaceutical industry." International Journal of Engineering & Technology 7, no. 2 (April 29, 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 MATLAB® fuzzy logic toolbox was used to simulate the performance of the fuzzy inference system. The results show that the efficiency of the system can be improved by manipulating the input-output parameters according to the user’s demands. Compared with conventional heating, ventilation and air-conditioning (HVAC) systems, the proposed ventilation system has the additional feature of the existence of microorganisms, which is a crucial criterion of indoor air quality in pharmaceutical laboratories.

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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 (November 1, 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. Performance of the proposed control method has been compared with conventional control methods such as On/Off, PID, fuzzy logic in terms of accuracy, efficiency and response time. Simulation results show that the proposed adaptive control is effective in the Heating, Ventilating, and Air Conditioning (HVAC) systems temperature control applications. In addition, energy efficiency in HVAC systems has been provided with the proposed control model. Furthermore, thermal analysis of the system has been done to corroborate simulation results.

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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 and air. In this paper, modulating equal percentage globe valves, fans speed, and dampers position have been modeled according to exact flow rates of hot water and air into the building, and a new approach to adapting FLC through the modification of fuzzy rules surface is presented. The novel system is a redesign of an FLC using MATLAB/Simulink, with the results showing an enhancement in thermal comfort levels.

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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 (June 1, 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 regarding occupancy density level estimation is used to provide the proposed controller with random number of users grounded on report were arranged by the university’s facilities operational team. The other division of this paper shows the estimated saved energy by the means of suggested advanced add-on, FUZZY-PID controlling system. The energy savings were divided into summer savings and winter savings. The third division presents economic and environmental analysis of the proposed advanced fuzzy logic controllers of smart buildings in Subtropical Jordan. The economic parameters that were used to evaluate the system economy performance are life-cycle analysis, present worth factor and system payback period. The system economic analysis was done using MATLAB software

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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 (January 22, 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 controlling technique. To design a controller for enhancing the power output from the wind energy system using UPFC. This will be made to feed DC transmission system. Finally integrating the system with long distance DC transmission system and then to the grid so as to make it more reliable and efficient. In this work a coordinated control based on Fuzzy logic for UPFC for cluster of offshore WPP connected to the same HVDC connection is being implemented and analyzed. The study is targeting coordination of reactive power flow and active power flow between HVDC Converter and the WPP cluster while providing offshore AC grid voltage control. Thus it can be drawn from this work that while designing a power control strategy the proposed fuzzy logic based active power controller in UPFC can serve the purpose with better results in terms of active as well as reactive power. This control can also be used in hybrid systems thus making it more reliable controlling method. The

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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 (September 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 (October 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 (January 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 (November 1, 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 (January 6, 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 (January 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 (December 28, 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 (January 21, 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 problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

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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 (January 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 (March 1, 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 (October 22, 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 (June 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 in the Control Layer in order to generate control signals allowing to achieve specified control goals. Novelty of the proposed solutions lays in combination of two technologies, Open Decision Point technology originating from the Software Engineering domain Policy-based Computing that is originating from the Knowledge Engineering domain in application to non-linear control systems.

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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 (December 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 (August 5, 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), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage.

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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 (July 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 using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.

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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 (March 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 (September 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 (May 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 (October 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 (December 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 (February 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 (September 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 the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCONART can on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The proposed learning scheme has been successfully used to control two unstable nonlinear systems. They are the seesaw system and the inverted wedge system.

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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 (September 1, 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 (September 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 (February 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 (October 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 (January 31, 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 (July 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 (February 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 (May 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 (July 1, 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 (June 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 (September 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 (October 1993): 1885–904. http://dx.doi.org/10.1080/00207729308949601.

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