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Relevant bibliographies by topics / Fuzzy Control

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Academic literature on the topic 'Fuzzy Control'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Fuzzy Control"

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HIROTA, Kaoru. "Fuzzy control." Journal of the Robotics Society of Japan 9, no. 2 (1991): 232–37. http://dx.doi.org/10.7210/jrsj.9.232.

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Dery, D. "Fuzzy Control." Journal of Public Administration Research and Theory 12, no. 2 (2002): 191–216. http://dx.doi.org/10.1093/oxfordjournals.jpart.a003529.

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Liu, Derong, and Huaguang Zhang. "Fuzzy control." Automatica 39, no. 6 (2003): 1115–16. http://dx.doi.org/10.1016/s0005-1098(03)00064-5.

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Babuska, Robert, and Ebrahim Mamdani. "Fuzzy control." Scholarpedia 3, no. 2 (2008): 2103. http://dx.doi.org/10.4249/scholarpedia.2103.

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Qiu, Peihua. "Fuzzy Modeling and Fuzzy Control." Technometrics 50, no. 3 (2008): 408–9. http://dx.doi.org/10.1198/tech.2008.s901.

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FOULLOY, LAURENT, and SYLVIE GALICHET. "FUZZY SENSORS FOR FUZZY CONTROL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (1994): 55–66. http://dx.doi.org/10.1142/s0218488594000067.

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This paper introduces sensors employing a fuzzy numeric to symbolic interface. The fundamental design considerations for this kind of fuzzy symbolic sensor, or fuzzy sensor, are formally presented. Then, the use of these components for fuzzy control is discussed and illustrated.

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Cios, Krzystof. "Fuzzy control and fuzzy systems." Neurocomputing 10, no. 1 (1996): 97–98. http://dx.doi.org/10.1016/s0925-2312(96)90014-4.

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Harris, C. J. "Fuzzy control & fuzzy systems." Automatica 28, no. 2 (1992): 443. http://dx.doi.org/10.1016/0005-1098(92)90135-3.

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HIROTA, Kaoru. "Fuzzy Reasoning and Fuzzy Control." Journal of the Society of Mechanical Engineers 93, no. 856 (1990): 202–8. http://dx.doi.org/10.1299/jsmemag.93.856_202.

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Foulloy, L., and S. Galichet. "Fuzzy control with fuzzy inputs." IEEE Transactions on Fuzzy Systems 11, no. 4 (2003): 437–49. http://dx.doi.org/10.1109/tfuzz.2003.814831.

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Dissertations / Theses on the topic "Fuzzy Control"

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Gormandy, Brent Anthony. "Fuzzy model predictive control." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248858.

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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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Chowdhury, Mina Munir-ul Mahmood. "Evolutionary and reinforcement fuzzy control." Thesis, University of Glasgow, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299747.

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Layne, Jeffery Ray. "Fuzzy model reference learning control." Connect to resource, 1992. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1159541293.

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Moore, Christopher G. "Indirect adaptive fuzzy controllers." Thesis, University of Southampton, 1992. https://eprints.soton.ac.uk/250154/.

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Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and time--varying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, self--learning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as a possible solution to this problem. This controller is then developed by combining on--line adaptive modelling with model causality inversion and on--line controller design. The resulting controller is an analogue of the indirect adaptive algebraic controller. A major advantages of this method is the separation of model convergence and control loops enabling the two aspects to be analysed separately. Demonstration of this work has been achieved by a series of simulation tests using a variety of vehicle models. A conventional front wheel steer road vehicle model has been used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the properties of the controller. To test the controller with realistic demand signals, a static rule-based piloting system has also been developed. These simulations have demonstrated i) the successful control of systems with little a priori vehicle knowledge ii) ability to adapt to continuous and sudden parametric changes in the process iii) good noise rejection properties iv) good disturbance rejection properties and v) ability to adapt to stationary loop non--linearities.

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Hu, Jian-Quan. "Adaptive fuzzy predictive control using a neuro-fuzzy model with application to sintering." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265575.

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Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.

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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.

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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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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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Ellis, Susan Marie. "Fuzzy control and an evaluation of the self-organizing fuzzy controller." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/45944.

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<p>Fuzzy control is a rule based type of control that aims to imitate the human's ability to express a control policy using linguistic rules, and to reason using those rules to control a system. Fuzzy control is nonlinear and not dependent on a precise mathematical description of the plant, and is therefore more easily applied to systems such as industrial processes that are hard to model. An overview is given of the fuzzy controller, along with descriptions of applications and theoretical approaches to designing and analyzing the controller.</p> <p>The selfâ organizing controller is able to generate or modify its rules in real time based on the system performance. It was tested to determine how well it was able to learn a quality control policy. The selfâ organizing controller was found to exhibit poor steady state performance, and to be equally likely to learn poor control as to learn good control. It was not found to eliminate the need for careful tuning of the controller parameters and gains.</p><br>Master of Science

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Books on the topic "Fuzzy Control"

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Hampel, Rainer, Michael Wagenknecht, and Nasredin Chaker, eds. Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3.

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Stephen, Yurkovich, ed. Fuzzy control. Addison-Wesley, 1998.

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Kahlert, Jörg, and Hubert Frank. Fuzzy-Logik und Fuzzy-Control. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6.

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Kahlert, Jörg, and Hubert Frank. Fuzzy-Logik und Fuzzy-Control. Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-83760-8.

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

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Faghih, Nezameddin, and Nazak Nobari. Fuzzy Quality Control. Navid, 2004.

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Vukadinovic, Dinko. Fuzzy control systems. Nova Science Publishers, 2011.

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G, Chen. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2000.

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

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Jantzen, Jan. Foundations of Fuzzy Control. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535608.

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Book chapters on the topic "Fuzzy Control"

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Zadeh, Lotfi A. "From Computing with Numbers to Computing with Words — From Manipulation of Measurements to Manipulation of Perceptions." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_1.

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Šajda, Jozef. "Using Fuzzy Parasets in Problem-Solving Under Uncertainty." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_10.

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Sustal, Jiri Georg. "On the Change of the Distribution Shape of Randomized Fuzzy Variables by Filtering over Compatibility Degrees." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_11.

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Liu, Jun, Da Ruan, Zhenming Song, and Yang Xu. "On Optimization with Fuzzy Constraint Conditions." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_12.

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Mikut, Ralf, Jens Jäkel, and Lutz Gröll. "Inference Methods for Partially Redundant Rule Bases." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_13.

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Pivoňka, Petr. "Analysis and Design of Fuzzy PID Controller Based on Classical PID Controller Approach." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_14.

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Rotach, V. "Expert Methods in the Theory of Automatic Control." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_15.

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Arakeljan, Edik, Mark Panko, and Vasili Usenko. "Comparative Analysis of Classical and Fuzzy PID Algorithms." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_16.

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Sekaj, Ivan. "Non-Fuzzy Knowledge-Rule-Based Controllers and their Optimisation by Means of Genetic Algorithms." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_17.

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Kluska, Jacek, and Lesław Gniewek. "A New Method of Fuzzy Petri Net Synthesis and its Application for Control Systems Design." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_18.

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Conference papers on the topic "Fuzzy Control"

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Zamani, Iman, and Masoud Shafie. "Fuzzy impulsive control with application to chaos control." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277129.

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Koshiyama, Adriano S., Tatiana Escovedo, Marley M. B. R. Vellasco, and Ricardo Tanscheit. "GPFIS-Control: A fuzzy Genetic model for Control tasks." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891733.

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Patrascu, Monica, and Ioan Dumitrache. "Hybrid geno-fuzzy controller for seismic vibration control." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334605.

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Krapez, Aleksandar, Branimir Seselja, and Andreja Tepavcevic. "Fuzzy Pexider equations and applications to fuzzy control." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251258.

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Kramer, Klaus-Dietrich, Annedore Söchting, and Thomas Stolze. "Fuzzy Control Teaching Models." In InSITE 2016: Informing Science + IT Education Conferences: Lithuania. Informing Science Institute, 2016. http://dx.doi.org/10.28945/3422.

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Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool), and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI) applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

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Trollope, James E., Leszek Koszalka, Iwona Pozniak-Koszalka, and Keith J. Burnham. "A fuzzy logic approach for vehicle collision energy distribution." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915159.

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Lovato, Agnaldo Volpe, and Jose Carlos M. Oliveira. "Airplane level changes using fuzzy control." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584274.

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Pan, Yongping, Rongjun Chen, Hongzhou Tan, and Meng Joo Er. "Asymptotic stabilization via adaptive fuzzy control." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622359.

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Vivas-Lopez, Carlos Alberto, Ruben Morales-Menendez, Ricardo Ramirez-Mendoza, Olivier Sename, and Luc Dugard. "Chassis Control based on Fuzzy Logic." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737771.

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Pastuizaca Fernandez, Maria Nela. "Fuzzy theory and quality control charts." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015675.

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Reports on the topic "Fuzzy Control"

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Wit, Jeffrey S., Carl D. Crane, Armstrong III, and II David G. Fuzzy Control for Autonomous Ground Vehicles. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387087.

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

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Almufti, Ali. Parallel Hybrid Vehicles using Fuzzy Logic Control. Defense Technical Information Center, 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), 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), 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), 1995. http://dx.doi.org/10.2172/123263.

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Feng, Nenglian, Keqiang Li, Ying Cheng, and Xiaomin Lian. Fuzzy-Based Velocity Control of the Automotive Cruising (Second Report). SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0304.

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Bechtel, James H. An Innovative Knowledge-Based System Using Fuzzy Cognitive Maps for Command and Control An Innovative Knowledge-Based System Using Fuzzy Cognitive Maps for Command and Control. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada381723.

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Stanton, Kevin. Jitter and Wander Reduction for a SONET DS3 Desynchronizer Using Predictive Fuzzy Control. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1163.

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.

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