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Relevant bibliographies by topics / Control methodology][Fuzzy logic

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Academic literature on the topic 'Control methodology][Fuzzy logic'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Control methodology][Fuzzy logic"

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Costa Drumond Sousa, Gilberto, Bimal K. Bose, and Marcelo Godoy Simões. "A simulation-implementation methodology of a fuzzy logic based control system." Eletrônica de Potência 2, no. 1 (June 1, 1997): 61–68. http://dx.doi.org/10.18618/rep.1997.1.061068.

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Korchunov, Alexey, Mikhail Chukin, and Aleksandr Lysenin. "Methodology of Developing Mathematical Models with Fuzzy Logic Elements for Quality Indices Control." Applied Mechanics and Materials 436 (October 2013): 374–81. http://dx.doi.org/10.4028/www.scientific.net/amm.436.374.

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Advantages of applying fuzzy logic theory to metal products quality indices control in development of new models and in improvement of acting process operations are shown. It is proved that it is appropriate to determine fuzzy relation as preference relation in process of handling products quality indices in process operations. Elaboration of algorithm of handling mathematical models with fuzzy logic elements to control quality indices is undertaken. Methodology of mathematical models development with fuzzy logic elements for metal products quality indices control is created. Process of metal products quality indices control on the basis of models with fuzzy logic elements is illustrated.

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Touati, Y., and Y. Amirat. "Fuzzy logic controller design methodology for Cartesian robot control." International Journal of Computer Applications in Technology 27, no. 2/3 (2006): 85. http://dx.doi.org/10.1504/ijcat.2006.011135.

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Sharma, Kaushik Das. "A systematic design methodology of PD fuzzy logic controller using cellular fuzzy logic concept." International Journal of Automation and Control 6, no. 3/4 (2012): 231. http://dx.doi.org/10.1504/ijaac.2012.051882.

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Andújar, José M., and Antonio J. Barragán. "A methodology to design stable nonlinear fuzzy control systems." Fuzzy Sets and Systems 154, no. 2 (September 2005): 157–81. http://dx.doi.org/10.1016/j.fss.2005.03.006.

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Preye, Uguta Henry, and Onyejegbu Laeticia Nneka. "An Intelligent Fuzzy Logic System for Network Congestion Control." Circulation in Computer Science 2, no. 11 (December 20, 2017): 23–30. http://dx.doi.org/10.22632/ccs-2017-252-69.

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Network congestion is a major problem in all network environments as such it calls for ways to manage this problem. In this paper, we propose a Fuzzy Regulator Effective Random Early Detection (FRERED) system, which is an intelligent fuzzy logic based controller technique for early stage congestion detection, at the router buffer in the networks. The proposed technique extends the Fuzzy-Based system in the Fuzzy Hybrid ERED algorithm by considering the delay variable in its inference system to ease the problem of parameter initialization and parameter dependency. Unlike the Fuzzy-Based controller in the existing Fuzzy Hybrid ERED system which uses two parameter settings in its inference system that is, the queue size and average queue length in computing the dropping probability of packets. The proposed technique uses the queue size, average queue length and the delay approximation as input variables in computing the packet drop probability. The applied fuzzy logic system yields an output that denotes a packet dropping probability, which in turn controls and prevents congestion in early stage. This was achieved after simulating the proposed technique and the existing Fuzzy-Based controller using Matlab. The results obtained shows that this approach results in less packet drops for about the same link utilization as the existing Fuzzy-Based controller. Therefore, this technique, generally, controls network congestion and improves network performance. The methodology used to achieve this is the object oriented methodology and JAVA programming language was used to develop the system.

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Zeinali, Meysar, and Leila Notash. "FUZZY LOGIC-BASED INVERSE DYNAMIC MODELLING OF ROBOT MANIPULATORS." Transactions of the Canadian Society for Mechanical Engineering 34, no. 1 (March 2010): 137–50. http://dx.doi.org/10.1139/tcsme-2010-0009.

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This paper presents the design and implementation of a systematic fuzzy modelling methodology for the inverse dynamic modelling of robot manipulators. The fuzzy logic modelling methodology is motivated in part by the difficulties encountered in the modelling of complex nonlinear uncertain systems, and by the objective of developing an efficient dynamic model for the real-time model-based control. The methodology is applied to build the fuzzy logic-based inverse dynamic model of a prototyped wire-actuated parallel manipulator with uncertain dynamics. The developed inverse dynamics has been used in a fuzzy model-based adaptive robust controller for the tracking control of the parallel manipulator.

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (June 8, 2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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Kim, Euntai, Heejin Lee, and Dongyon Kim. "Fuzzy Control of a Direct Current Motor System and Stability Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 515–18. http://dx.doi.org/10.20965/jaciii.1999.p0515.

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One of the most common ways of driving electromechanical systems is through the use of a DC motor. In this paper, fuzzy control methodology for a DC motor system using a singleton fuzzy logic controller (FLC) is proposed. As opposed to conventional works, fuzzy control methodology proposed here is guaranteed to be asymptotically stable on the whole. Finally, the validity of the suggested methodology is highlighted via an illustrative example.

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Mendes, Jérôme, Ricardo Maia, Rui Araújo, and Francisco A. A. Souza. "Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules." Applied Sciences 10, no. 17 (August 23, 2020): 5836. http://dx.doi.org/10.3390/app10175836.

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The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

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Dissertations / Theses on the topic "Control methodology][Fuzzy logic"

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Al-Assaf, Y. "Self-tuning control : Theory and applications." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235033.

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Emami, Mohammad Reza. "Systematic methodology of fuzzy-logic modeling and control and application to robotics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28276.pdf.

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Otero, Angel Rafael. "An Information Security Control Assessment Methodology for Organizations." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/266.

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In an era where use and dependence of information systems is significantly high, the threat of incidents related to information security that could jeopardize the information held by organizations is more and more serious. Alarming facts within the literature point to inadequacies in information security practices, particularly the evaluation of information security controls in organizations. Research efforts have resulted in various methodologies developed to deal with the information security controls assessment problem. A closer look at these traditional methodologies highlights various weaknesses that can prevent an effective information security controls assessment in organizations. This dissertation develops a methodology that addresses such weaknesses when evaluating information security controls in organizations. The methodology, created using the Fuzzy Logic Toolbox of MATLAB based on fuzzy theory and fuzzy logic, uses fuzzy set theory which allows for a more accurate assessment of imprecise criteria than traditional methodologies. It is argued and evidenced that evaluating information security controls using fuzzy set theory addresses existing weaknesses found in the literature for traditional evaluation methodologies and, thus, leads to a more thorough and precise assessment. This, in turn, results in a more effective selection of information security controls and enhanced information security in organizations. The main contribution of this research to the information security literature is the development of a fuzzy set theory-based assessment methodology that provides for a thorough evaluation of ISC in organizations. The methodology just created addresses the weaknesses or limitations identified in existing information security control assessment methodologies, resulting in an enhanced information security in organizations. The methodology can also be implemented in a spreadsheet or software tool, and promote usage in practical scenarios where highly complex methodologies for ISC selection are impractical. Moreover, the methodology fuses multiple evaluation criteria to provide a holistic view of the overall quality of information security controls, and it is easily extended to include additional evaluation criteria factor not considered within this dissertation. This is one of the most meaningful contributions from this dissertation. Finally, the methodology provides a mechanism to evaluate the quality of information security controls in various domains. Overall, the methodology presented in this dissertation proved to be a feasible technique for evaluating information security controls in organizations.

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Voutchkov, Ivan I. "A methodology for modelling, optimisation and control of the friction surfacing process." Thesis, University of Portsmouth, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326995.

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The friction surfacing process is a derivative of friction welding and retains all the benefits of that welding process - solid phase, forged microstructures and excellent metallurgical bonds. This work is aimed at the development of mathematical and statistical models for the optimisation of the significant process parameters in order to allow rapid development of new applications using standard CNC equipment. Also the possibility of implementing real-time control systems have been investigated and developed. A friction surfacing database has been configured to allow continuos recording and storage of the useful machine outputs. Later, an infrared pyrometer and thermocouples have also been connected to the data acquisition set-up establishing fully automated information flow from the process. A conversion procedure has been developed to ensure that the experimental results are applicable in industrial environments. Response surface map and the method of visual optimisation have been developed. They are an essential part of the methodology for experimental optimisation of the friction surfacing process. The problem of modelling and optimisation has also been approached using accurate statistical methods. Artificial intelligence in the form of neural networks has been used to improve the accuracy of the derived friction surfacing analytical relationships. For the first time dynamic study of the process has been carried out and CARIMA models have been derived using a modified version of the recursive least squares, to ensure high sensitivity and stability of the identification procedure. New conversion technique has been developed, allowing the use of existing models for materials that have not been used for friction surfacing before, reducing significantly the number of experiments. The idea of using indicator parameters has been introduced for the first time in this research. Such parameters are the force, the torque and the interface temperature and they can be measured on-line. It has been shown that variations of these parameters reflect in the quality of the coating characteristics that cannot be measured on-line. Real-time control has also been considered. An algorithm involving fuzzy logic and self-tuning extremum controller has been developed to continuously monitor and compensate in real-time against the variations in the coating characteristics, and respectively in the indicator parameters. The proposed methodology has been used to design a control system that is capable of maintaining optimal process characteristics. The value of this work is also in reducing the lead-time and hence the cost for determining the optimum parameters for a given coating material on a given substrate geometry. This is an important feature when developing new applications for the friction surfacing process. On the basis of this research a range of new commercial applications have emerged including the manufacture of machine knives for the food, pharmaceutical and packaging industries, repair of car engine valve seats, turbine blades, reclamation of shafts, etc.

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Hoyle, W. J. "Fuzzy logic, control and optimisation." Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6458.

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This thesis examines the utility of fuzzy logic in the field of control engineering. A tutorial introduction to the field of fuzzy control is presented during the development of an efficient fuzzy controller. Using the controller as a starting point, a set of criteria are developed that ensure a close connection between rule base construction and control surface geometry. The properties of the controller are exploited in the design of a global controller optimiser based on a genetic algorithm, and a tutorial explaining how the optimiser may be used to effect automatic controller design is given. A library of software that implements a fast fuzzy controller, a genetic algorithm, and various utility routines is included.

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Ali, Agha Rehmat. "Predicted Speed Control based on Fuzzy Logic for Belt Conveyors : Fuzzy Logic Control for Belt Conveyors." Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-70106.

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In order to achieve energy savings for belt conveyor system, speed control provides one of the best solutions. Most of the traditional belt conveyors used in the industries are based on constant speed for all operational times. Due to the need and advancements in technology, Variable Frequency Drives (VFD) are employed in industries for a number of processes. Passive Speed Control was previously suggested for the proper utilization of VFD to make belt conveyor systems more power e- cient with increased life expectancy and reduced environmental eects including the noise reduction caused by constant speed of operation. Due to certain conditions and nature of operation of belt conveyor systems, it is not desirable to use Passive Speed control where feeding rate is random. Due to the extreme non-linearity of the random feeding rate, an Active speed control for VFD is desired which adjusts belt speed according to the material loading. In this thesis an Active Speed control for VFD is proposed that can achieve energy and cost ecient solutions for belt conveyor systems as well as avoiding half-lled belt operations. The aim of this thesis work is primarily to determine reliability and validity of Active Speed Control in terms of power savings. Besides achieving power savings, it is also necessary to check the economic feasibility. A detailed study is performed on the feasibility of Active Speed Control for random feeding rate according to industrial requirements. Due to the random and non-linearity of the material loading on the belt conveyor systems, a fuzzy logic algorithm is developed using the DIN 22101 model. The developed model achieves Active Speed Control based on the feeding rate and thereby optimizes the belt speed as required. This model also overcomes the risks of material spillage, overloading and sudden jerks caused due to unpredicted rise and fall during loading. The model conserves 20- 23% of the total power utilized compared to the conventional conveyor systems in use. However it is noticed that the peak power of conventional conveyor belt systems is up to 16% less compared to the proposed model. If implemented in dierent industries, based on the operational time and total consumption of electricity, the proposed Active speed control system is expected to achieve economic savings up to 10-12 % .

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Marriott, Jack. "Adaptive robust fuzzy logic control design." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15819.

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Farah, Hassan. "The fuzzy logic control of aircraft." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ43339.pdf.

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Farah, Hassan (Hassan Kahiye) Carleton University Dissertation Engineering Mechanical and Aerospace. "The Fuzzy logic control of aircraft." Ottawa, 1999.

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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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Books on the topic "Control methodology][Fuzzy logic"

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Driankov, Dimiter, Peter W. Eklund, and Anca L. Ralescu, eds. Fuzzy Logic and Fuzzy Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7.

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Jager, René. Fuzzy logic in control. Delft: Techn. Univ, 1995.

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Maria, Bojadziev, ed. Fuzzy sets, fuzzy logic, applications. Singapore: World Scientific Pub. Co., 1995.

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

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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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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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1953-, Freiberger Paul, ed. Fuzzy Logic: The Discovery Of A Revolutionary Computer Technology – And How It Is Changing Our World. New York: Simon & Schuster, 1993.

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

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Book chapters on the topic "Control methodology][Fuzzy logic"

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Vachtsevanos, G., and S. Farinwata. "Fuzzy Logic Control: A Systematic Design and Performance Assessment Methodology." In Fuzzy Logic, 39–62. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_2.

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Glorennec, Pierre Yves. "Adaptive Fuzzy Control." In Fuzzy Logic, 541–51. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_50.

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Palm, R. "On the Compatibility of Fuzzy Control and Conventional Control Techniques." In Fuzzy Logic, 63–115. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_3.

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Liu, Jinkun. "Fuzzy Logic Control." In Intelligent Control Design and MATLAB Simulation, 33–56. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5263-7_4.

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Ben-Ari, Mordechai, and Francesco Mondada. "Fuzzy Logic Control." In Elements of Robotics, 179–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62533-1_11.

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D’Ambrosio, Bruce. "Fuzzy Logic Control." In Qualitative Process Theory Using Linguistic Variables, 5–15. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9671-0_2.

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Mokhtari, Mohand, and Michel Marie. "Fuzzy logic control." In Engineering Applications of MATLAB® 5.3 and SIMULINK® 3, 95–148. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0741-5_3.

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Bien, Zeungnam, and Heegyoo Lee. "Time Weighted Fault Tolerant Control." In Fuzzy Logic, 507–16. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_47.

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Trillas, Enric, and Luka Eciolaza. "An Introduction to Fuzzy Control." In Fuzzy Logic, 175–202. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_8.

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Gerla, Giangiacomo. "Fuzzy Control and Approximate Reasoning." In Fuzzy Logic, 199–220. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9660-2_10.

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Conference papers on the topic "Control methodology][Fuzzy logic"

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Mountain, Jeffrey R. "A Hybrid Fuzzy Logic Approach for 8-Bit Embedded Control Applications." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34965.

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The real-time implementation of fuzzy logic algorithms in embedded systems typically uses two approaches: employ fuzzy specific processing hardware or adapt standard embedded controllers to implement the fuzzy logic inference process. While high speed applications may require using the more sophisticated hardware, most embedded control applications do not have such processing speed demands, nor can they justify the added expense associated with the fuzzy enhanced processing engines. A review of embedded controller fuzzy logic implementations indicates a preference for 16-bit architectures; devoting significant processing resources to perform fuzzification, rule application, and defuzzification during real-time operation. While these approaches remain faithful to the foundations of fuzzy logic control, devoting processor resources to fuzzy specific tasks can limit a controller’s ability to handle peripheral tasks, such as man-machine I/O interface. This paper describes a simplified, hybrid approach suitable for standard 8-bit microcontrollers. The generic nature of the approach allows the methodology to be readily applicable to many single input, single output systems. This paper describes the hybrid fuzzy logic approach, which is placed in context using a proof-of-concept motor speed application. System performance data and notable limitations of the prototyped system are also described.

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Sadeghzadeh, S. M., and M. Ansarian. "New methodology to control of FACTS devices based on ANN and fuzzy logic." In 7th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20062084.

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Shankar, C. U., M. Yuvaraj, and R. Thottungal. "A novel methodology to enhance transient stability using SVC with fuzzy logic control." In IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0288.

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Yu-Rong Li and Jing-Ping Jiang. "The integrated methodology of rough sets theory, fuzzy logic and genetic algorithms for multisensor fusion." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.945673.

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Dhami, S. S., S. S. Bhasin, and P. B. Mahapatra. "Design of a Fuzzy Logic Controller Using ANFIS for Accurate Position Control of a Pneumatic Servo System." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66940.

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A methodology for designing a Sugeno type Fuzzy Logic Controller (FLC) for accurate position control of a pneumatic servo system is presented. Adaptive Neuro Fuzzy Inference System technique is employed to construct a fuzzy inference system whose membership function parameters are tuned using a training data set comprising of input/output signal of the pneumatic servo system with proportional control. Hybrid backpropogation-least square algorithm is used for training of the Fuzzy Inference System (FIS). The resulting FIS optimally projected the behavior of training data set. To obtain the desired steady-state response, the fuzzy inference system is further tuned using the expert knowledge of the input/output response of the system. The system response for various reference inputs is compared quantitatively with that of the system without fuzzy logic controller, and excellent improvement in steady-state response is observed.

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Liu, Rong, Xuanyin Wang, and Xuchu Huang. "Hybrid Fuzzy PID Control of Hydraulic Robot." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32082.

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This paper presents a fuzzy tracking control methodology for hydraulic control system of robot with generalized pulse modulation (GPCM) control by combining the merits of fuzzy logic and conventional linear control theory. The hydraulic control systems with GPCM control have the high nonlinearity and uncertain dynamics, therefore the simple linear or nonlinear differential equations cannot sufficiently represent corresponding practical system. The designed controllers based on the model cannot guarantee the good performance such as stability and robustness. A hybrid of fuzzy and PID control algorithm is proposed as the solution. The control algorithm is separated into two parts: fuzzy control and PID control. The fuzzy controller is used to control the piston when the piston is near the desired position, and PID controller is applied when the piston is far away the target position. The hybrid fuzzy PID is implemented on computer and applied to control the arms of hydraulic robot with GPCM control. The results from the experiments show that the proposed hybrid fuzzy PID control has good performance.

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Zhu, Min, Hua Qu, Ji-Hong Zhao, Wubing Chen, and Khurrum Jalii. "A methodology for quantitative analysis of non-determinacy relationships using fuzzy logic in Bayesian networks." In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2017. http://dx.doi.org/10.1109/itnec.2017.8284857.

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Jayakumar, Arunkumar, Maximiano Ramos, and Ahmed Al-Jumaily. "A Novel Fuzzy Schema to Control the Temperature and Humidification of PEM Fuel Cell System." In ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology collocated with the ASME 2015 Power Conference, the ASME 2015 9th International Conference on Energy Sustainability, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/fuelcell2015-49623.

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The present paper proposes a simple yet effective technique to improve the performance of a practical PEM fuel cell system by tuning the two key operating parameters based on the expert’s rules derived from the literature. The fuzzy rule base is designed to optimally control the temperature and humidification of the two critical parameters governing the fuel cell system performance and dynamics. The modelling of the proposed methodology is presented through the Matlab/fuzzy logic toolbox.

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Badgujar, Kushal Dinkar, and Shripad T. Revankar. "Design of Fuzzy-PID Controller for Hydrogen Production Using HTPBR." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15037.

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Fuzzy logic offers better control over conventional control method. It has capability of fast execution which is useful to develop real time control system. In this work, a design of Fuzzy-proportional integral derivative (PID) controller to control the power of the high temperature pebble bed reactor (HTPBR) is presented. A simplified reactor model consisting of reactor heat balance and point kinetics model with reactivity feedback due to power coefficient of reactivity and Xenon poisoning is used. The reactor is operated at various power levels by using Fuzzy-PID controller. The Fuzzy logic eliminates the necessity of the tuning the gains of PID controller each time by extending the finite sets of the PID controller gains. Hydrogen production is considered using SI cycle. The process heat required for Hydrogen production using SI cycle is supplied by High Temperature Pebble Bed Reactor. A methodology has been developed for the production of Hydrogen. Simulation results are useful for controlling the reactor power in accordance with hydrogen production rate.

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Yaacob, Sazali, Ramachandran Nagarajan, and Kenneth T. T. Kin. "Application of predictive fuzzy logic controller in temperature control of phenol-formaldehyde manufacturing: using MATLAB-SIMULINK methodology." In Intelligent Systems and Advanced Manufacturing, edited by Angappa Gunasekaran and Bhaskaran Gopalakrishnan. SPIE, 2001. http://dx.doi.org/10.1117/12.443121.

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Reports on the topic "Control methodology][Fuzzy logic"

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Combs, James E. Advanced Control Techniques with Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada604019.

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Almufti, Ali. Parallel Hybrid Vehicles using Fuzzy Logic Control. Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada513229.

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Lawson, J. E., M. G. Bell, R. J. Marsala, and D. Mueller. Beta normal control of TFTR using fuzzy logic. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10182059.

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Tang, Yu, and Kung C. Wu. Active structural control by fuzzy logic rules: An introduction. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/448036.

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Tang, Y. Active structural control by fuzzy logic rules: An introduction. Office of Scientific and Technical Information (OSTI), July 1995. http://dx.doi.org/10.2172/123263.

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/15006009.

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