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Relevant bibliographies by topics / Fuzzy logic; HVAC control systems

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Academic literature on the topic 'Fuzzy logic; HVAC control systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Fuzzy logic; HVAC control systems"

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Joergensen, Dorte Rich. "Automated commissioning of building control systems." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244525.

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Daneshpooy, Alireza. "Artificial neural network and fuzzy logic control for HVDC systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23593.pdf.

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Lin, Yuetong. "MODULAR CONSTRUCTION OF FUZZY LOGIC CONTROL SYSTEMS." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/193845.

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This dissertation presents a novel approach to combining wavelet networks and multi-layer feedforward network for fuzzy logic control systems. Most of the existing methods focus on implementing the Takagi-Sugano fuzzy reasoning model and have demonstrated its effectiveness. However, these methods fail to keep the knowledge structure, which is critical in interpreting the learning process and providing insights to the working mechanism of the underlying systems. It is our intention here to continue the previous research by the PARCS group in this area by utilizing individual subnets to implement decision-making process with the fuzzy logic control systems based on the Mamdani model. Center Average defuzzification has seen its implementation by a neural network so that a succinct network structure is obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time. Offline orthogonal least squares method is used for training the wavelet subnets and the overall systems is updated using the steepest descent algorithm. Simulation results have shown the efficacy of this new approach in applications including system modeling and time series prediction.

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Mathur, Garima. "Fuzzy logic control for infant-incubator systems." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153768682.

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Cook, Brandon M. "Multi-Agent Control Using Fuzzy Logic." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447688633.

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Fung, Yun-hoi. "Linguistic fuzzy-logic control of autonomous vehicles /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19660583.

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Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.

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El-Deen, M. M. G. Naser. "Adaptive fuzzy logic control for solar buildings." Thesis, Northumbria University, 2002. http://nrl.northumbria.ac.uk/2084/.

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Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adaptive control method for space heating in buildings with significant solar input. A simulation model of a building space to assess the performance of different control strategies is developed. A lumped parameter model based on an optimisation technique has been proposed and validated. It is shown that this model gives an improvement over existing low order modelling methods. A detailed model of a hot water heating system and related control devices is developed and evaluated for the specific purpose of control simulation. A PI-based fuzzy logic controller is developed in which the error and change of error between the internal air temperature and the user set point temperature is used as the controller input. A conventional PD controller is also considered for comparison. The parameters of the controllers are set to values that result in the best performance under likely disturbances and changes in setpoint. In a further development of the fuzzy logic controller, the Predicted Mean Vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. The controller then adjusts the air temperature set point in order to satisfy the required comfort level given the prevailing values of other comfort variables contributing to the comfort sensation. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. The need to develop an adaptive capability in the fuzzy logic controller to account for lagging influence of solar heat gain is established and a new adaptive controller has therefore been proposed. The development of a "quasi-adaptive" fuzzy logic controller is developed in two steps. A feedforward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then modified to have two inputs: the first input being the error between the setpoint temperature and the internal air temperature and the second the predicted future internal air temperature. When compared with a conventional method of control the proposed controller is shown to give good tracking of the setpoint temperature, reduced energy consumption and improved thermal comfort for the occupants by reducing solar overheating. The proposed controller is tested in real time using a test cell equipped with an oil- filled electric radiator, temperature and solar sensors. Experimental results confirm earlier findings arrived at by simulations, in that the proposed controller achieves superior tracking and reduces afternoon solar overheating, when compared with a conventional method of control.

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Baxter, Jeremy. "Fuzzy logic control of an automated guided vehicle." Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5817/.

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This thesis describes the fuzzy logic based control system for an automated guided vehicle ( AGV ) designed to navigate from one position and orientation to another while avoiding obstacles. A vehicle with an onboard computer system and a beacon based location system has been used to provide experimental confirmation of the methods proposed during this research. A simulation package has been written and used to test control techniques designed for the vehicle. A series of navigation rules based upon the vehicle's current position relative to its goal produce a fuzzy fit vector, the entries in which represent the relative importance of sets defined over all the possible output steering angles. This fuzzy fit vector is operated on by a new technique called rule spreading which ensures that all possible outputs have some activation. An obstacle avoidance controller operates from information about obstacles near to the vehicle. A method has been devised for generating obstacle avoidance sets depending on the size, shape and steering mechanism of a vehicle to enable their definition to accurately reflect the geometry and dynamic performance of the vehicle. Using a set of inhibitive rules the obstacle avoidance system compiles a mask vector which indicates the potential for a collision if each one of the possible output sets is chosen. The fuzzy fit vector is multiplied with the mask vector to produce a combined fit vector representing the relative importance of the output sets considering the demands of both navigation and obstacle avoidance. This is operated on by a newly developed windowing technique which prevents any conflicts produced by this combination leading to an undesirable output. The final fit vector is then defuzzified to give a demand steering angle for the vehicle. A separate fuzzy controller produces a demand velocity. In tests carried out in simulation and on the research vehicle it has been shown that the control system provides a successful guidance and obstacle avoidance scheme for an automated vehicle.

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馮潤開 and Yun-hoi Fung. "Linguistic fuzzy-logic control of autonomous vehicles." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29812690.

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Books on the topic "Fuzzy logic; HVAC control systems"

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Silva, Clarence W. De. Intelligent control: Fuzzy logic applications. Boca Raton: CRC Press, 1995.

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Tat, Pham Trung, ed. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. Boca Raton, Fla: CRC Press, 2001.

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G, Chen. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. Boca Raton, FL: CRC Press, 2000.

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McNeill, Daniel. Fuzzy logic. New York: Simon & Schuster, 1993.

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Dimiter, Driankov, Eklund Peter W. 1962-, and Ralescu Anca L. 1949-, eds. Fuzzy logic and fuzzy control: IJCAI '91 workshops on fuzzy logic and fuzzy control, Sydney, Australia, August 24, 1991 : proceedings. Berlin: Springer-Verlag, 1994.

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1949-, Ryan Michael, and Power James, eds. Using fuzzy logic: Towards intelligent systems. New York: Prentice Hall, 1994.

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Nguyen, Hung T. Fuzzy Systems: Modeling and Control. Boston, MA: Springer US, 1998.

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Jamshidi, Mohammad. Large-scale systems: Modeling, control, and fuzzy logic. Upper Saddle River, NJ: Prentice Hall, 1997.

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Ruan, Da. Fuzzy Logic Foundations and Industrial Applications. Boston, MA: Springer US, 1996.

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Stephen, Yurkovich, ed. Fuzzy control. Menlo Park, Calif: Addison-Wesley, 1998.

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Book chapters on the topic "Fuzzy logic; HVAC control systems"

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Pedrycz, W. "Fuzzy dynamic systems." In Fuzzy Logic and Fuzzy Control, 35–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7_17.

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Bai, Ying, and Zvi S. Roth. "Fuzzy Logic Control Systems." In Classical and Modern Controls with Microcontrollers, 437–511. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01382-0_7.

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Castillo, Oscar, and Patricia Melin. "Fuzzy Logic." In Soft Computing for Control of Non-Linear Dynamical Systems, 5–27. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1832-1_2.

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Aracil, J., A. García-Cerezo, A. Barreiro, and A. Ollero. "Stability Analysis of Fuzzy Control Systems Based on the Conicity Criterion." In Fuzzy Logic, 487–96. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_45.

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Yu, Shuanghe, Xinghuo Yu, and Man Zhihong. "Indirect Adaptive Fuzzy Control of Nonlinear Systems with Terminal Sliding Modes." In Fuzzy Logic, 263–76. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_19.

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Castillo, Oscar, Patricia Ochoa, and Jose Soria. "Fuzzy Logic Systems." In Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control, 5–8. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62133-9_2.

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Liu, Desheng, Zhiru Xu, Qingjun Shi, and Jingguo Zhou. "Fuzzy Immune PID Temperature Control of HVAC Systems." In Advances in Neural Networks – ISNN 2009, 1138–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_129.

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Katic, Dusko, and Miomir Vukobratovic. "Fuzzy Logic Approach in Robotics." In Intelligent Control of Robotic Systems, 71–112. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0317-8_3.

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Botros, M. "Applications of Fuzzy Logic in Mobile Robots Control." In Fuzzy Systems Engineering, 163–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11339366_7.

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Fazel Zarandi, M. H., and H. Mosadegh. "Robotics and Control Systems." In Fuzzy Logic in Its 50th Year, 283–308. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31093-0_13.

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Conference papers on the topic "Fuzzy logic; HVAC control systems"

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Berouine, A., E. Akssas, Y. Naitmalek, F. Lachhab, M. Bakhouya, R. Ouladsine, and M. Essaaidi. "A Fuzzy Logic-Based Approach for HVAC Systems Control." In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820356.

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MUÑOZ, ANDREAS, MATILDE SANTOS, and VICTORIA LÓPEZ. "DESIGN OF INTELLIGENT CONTROL FOR HVAC SYSTEM USING FUZZY LOGIC." In The 11th International FLINS Conference (FLINS 2014). WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814619998_0071.

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Khan, Muhammad Waqas, Mohammad Ahmad Choudhry, and Muhammad Zeeshan. "Multivariable adaptive Fuzzy logic controller design based on genetic algorithm applied to HVAC systems." In 2013 3rd IEEE International Conference on Computer, Control & Communication (IC4). IEEE, 2013. http://dx.doi.org/10.1109/ic4.2013.6653748.

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Khan, Muhammad Waqas, Mohammad Ahmad Choudhry, and Muhammad Zeeshan. "An efficient design of genetic algorithm based Adaptive Fuzzy Logic Controller for multivariable control of HVAC systems." In 2013 5th Computer Science and Electronic Engineering Conference (CEEC). IEEE, 2013. http://dx.doi.org/10.1109/ceec.2013.6659435.

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Reena, K. E. Mary, Abraham T. Mathew, and Lillykutty Jacob. "Real-time occupancy based HVAC control using interval type-2 fuzzy logic system in intelligent buildings." In 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2017. http://dx.doi.org/10.1109/iciea.2017.8282888.

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Lv Hongli, Duan Peiyong, and Jia Lei. "One Novel Fuzzy Controller Design for HVAC Systems." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597690.

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Kang, H., and G. Vachtsevanos. "Adaptive fuzzy logic control." In IEEE International Conference on Fuzzy Systems. IEEE, 1992. http://dx.doi.org/10.1109/fuzzy.1992.258648.

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Kelkar, Nikhal, Tayib Samu, and Ernest L. Hall. "Fuzzy logic control of an AGV." In Intelligent Systems & Advanced Manufacturing, edited by David P. Casasent. SPIE, 1997. http://dx.doi.org/10.1117/12.290287.

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Virk, G. S. "Fuzzy logic control of building management systems." In UKACC International Conference on Control. Control '96. IEE, 1996. http://dx.doi.org/10.1049/cp:19960616.

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Ciganek, J., and F. Noge. "Fuzzy logic control of mechatronic systems." In 2013 International Conference on Process Control (PC). IEEE, 2013. http://dx.doi.org/10.1109/pc.2013.6581428.

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