Relevant bibliographies by topics / Fuzzy-logic control

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy-logic control'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Fuzzy-logic control"

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Johnston, R. "Fuzzy logic control." Microelectronics Journal 26, no. 5 (July 1995): 481–95. http://dx.doi.org/10.1016/0026-2692(95)98950-v.

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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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Aung, Thae Thae Ei, and Zar Chi Soe. "Liquid Flow Control by Using Fuzzy Logic Controller." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2190–93. http://dx.doi.org/10.31142/ijtsrd18263.

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Madhava, Meghna, N. Meghana, Mulpuru Supriya, Div ya, and Siddalingesh S. Navalgund. "Automatic Train Control System Using Fuzzy Logic Controller." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 56–61. http://dx.doi.org/10.9756/bijrce.8201.

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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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Gerla, Giangiacomo. "Fuzzy Logic Programming and Fuzzy Control." Studia Logica 79, no. 2 (March 2005): 231–54. http://dx.doi.org/10.1007/s11225-005-2977-0.

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Africa, Aaron Don M. "Fuzzy Logic Temperature Control: A feedback control system implemented by fuzzy logic." International Journal of Emerging Trends in Engineering Research 8, no. 5 (May 25, 2020): 1879–85. http://dx.doi.org/10.30534/ijeter/2020/66852020.

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Arifin, Bustanul, Bhakti Yudho Suprapto, Sri Arttini Dwi Prasetyowati, and Zainuddin Nawawi. "Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control." World Electric Vehicle Journal 13, no. 3 (March 17, 2022): 53. http://dx.doi.org/10.3390/wevj13030053.

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The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.
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KATO, Akio, and Daisuke INUKAI. "Control Augmentation Using Fuzzy Logic Control." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 49, no. 570 (2001): 222–30. http://dx.doi.org/10.2322/jjsass.49.222.

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Dissertations / Theses on the topic "Fuzzy-logic control"

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

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Wang, Jian Zhou. "Robust control with fuzzy logic algorithms." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13195.

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This thesis presents the results of an investigation of the robustness of the widely used Mandani-type fuzzy logic control systems under a wide variation of parameters of the controlled process. The measurements of the dynamic performance and system robustness of a control system were firstly defined from the engineering point of view, and the concepts of the robust space and the robustness index were introduced. The robustness of the FLC systems was investigated by analyzing the structure of the fuzzy rule base and membership functions of the input-output variables. Based on the close relation of the fuzzy rule base and the system dynamic trajectory on the phase plane, a switching line method is introduced to qualitatively analyze the dynamic performance of the SISO FLC systems. This switching line method enables the qualitative prediction of the shape and position of the robust space of the FLC controlled first order processes and second order processes. The effects of FLC parameters on system robustness were also investigated. The movements of the position and the shape of the switching line with the variation of the controller parameters were analyzed, and its relation with the system performance was reported. Three methods were proposed to improve the robustness of the FLC system. The first design method proposed was based on the switching line characteristics of the FLC system. The second method, called phase advanced FLC, was introduced to handle the control of high order processes with fuzzy algorithms. The third method was an evolutionary method based on the genetic algorithm which was used to automatically design a robust fuzzy control system, assuming the availability of the controlled process model.
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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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Farah, Hassan (Hassan Kahiye) Carleton University Dissertation Engineering Mechanical and Aerospace. "The Fuzzy logic control of aircraft." Ottawa, 1999.

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García, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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García, Z. Yohn E. "Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.

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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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Books on the topic "Fuzzy-logic control"

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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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United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. [Washington, DC: National Aeronautics and Space Administration, 1994.

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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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Espinosa, Jairo, Joos Vandewalle, and Vincent Wertz. Fuzzy Logic, Identification and Predictive Control. London: Springer London, 2005. http://dx.doi.org/10.1007/b138626.

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Book chapters on the topic "Fuzzy-logic control"

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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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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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Protzel, P., R. Holve, J. Bemasch, and K. Naab. "Abstandsregelung von Fahrzeugen mit Fuzzy Control." In Fuzzy Logic, 212–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_22.

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Lloyd, Nathan, and Arjab Singh Khuman. "Adaptive Cruise Control Using Fuzzy Logic." In Fuzzy Logic, 191–219. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_12.

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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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Conference papers on the topic "Fuzzy-logic control"

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Bonivento, Claudio, Cesare Fantuzzi, and Riccardo Rovatti. "Fuzzy Logic Control." In Proceedings of the International Summer School. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814528450.

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Kim, Sunghwan, and William W. Clark. "Fuzzy Logic Semi-Active Vibration Control." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0564.

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Abstract Fuzzy logic control is so-called artificial intelligence because control laws are designed by human decision, not by deterministic numerical calculations. This paper investigates the effectiveness of a fuzzy logic control method in vibration control with a semi-active MR fluid actuator. The system to be controlled is a simply supported beam to which is applied a persistent excitation. Numerical simulation and experimental tests are performed. Three semi-active control laws, one deterministic and two fuzzy-logic, were applied and compared on the basis of vibration control performance and energy consumption. These controllers are compared to the baseline fully-on control where the MR fluids are energized so that the actuator performs as a tuned passive damper. Simulation and experimental data were examined.
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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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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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Zhao, Zhen-Yu, Masayoshi Tomizuka, and Setsuo Sagara. "A Fuzzy Tuner for Fuzzy Logic Controllers." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792541.

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Simon, Andra´s, and George T. Flowers. "Magnetic Bearing Control Using Interval Type-2 Fuzzy Logic." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82507.

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Magnetic bearings are an exciting and innovative technology that has seen considerable advances in recent years. Being unstable by nature, these systems require active control. Most often linear techniques are used very successfully. On the other hand, there are applications where linear methods have limited effectiveness. Fuzzy logic control performs very well in nonlinear control situations where the plant parameters are either partially or mostly unidentified. Its effectiveness for nonlinear systems also offers advantages to magnetic bearing systems. Type-2 fuzzy logic systems represent significant advances over traditional fuzzy logic systems in general. These fuzzy logic systems are capable to deal with uncertainties which can be found in almost every practical system. Uncertainties stem from several sources; noise present in the position input signals, the location and shape of fuzzy sets and the fuzzy rule-base describing the operation of the fuzzy controller, among others. Since a mathe-matical model of the controlled plant is often only a conveniently close approximation of the real process at hand, a major challenge lies in the application of the control methods to real plants. Type-2 fuzzy logic and fuzzy logic systems in general tackle the control problem at hand using human reasoning based on rules and expert knowledge of the plant described by human expressions. The current work consist of model development, controller design, simulation and experimental validation. The basic simulation model consist of a horizontal shaft supported by a radial magnetic bearing. The magnetic bearing is modeled as a nonlinear element. The controller designs are implemented and tested using a bench-top rotor rig equipped with a radial magnetic bearing. Some representative results are presented and discussed.
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Edwards, Dean B., and John R. Canning. "An Algorithm for Designing Conventional and Fuzzy Logic Control Systems." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/cie-4465.

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Abstract This paper presents an algorithm that can be used to design either conventional or fuzzy logic control systems. In order to use the algorithm, the engineer must first choose a performance index for the system which he or she wants to optimize relative to some specified design parameters. For conventional state space controllers, the design parameters are the feedback constants associated with the state variables of the system. For fuzzy logic controllers, the design parameters are the parameters used to define the fuzzy sets for the input state and control variables. We use the algorithm to design proportional plus derivative (PD) and proportional, integral, and derivative (PID) control systems and their equivalent fuzzy logic control systems. The algorithm therefore provides a unifying approach for designing either conventional or fuzzy logic control systems.
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Coban, Melih, and Murat Fidan. "Fuzzy Logic Based Temperature Control." In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2019. http://dx.doi.org/10.1109/ismsit.2019.8932906.

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Lieh, Junghsen, and Wei Jie Li. "Fuzzy Logic Control of Material Forming Process." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0409.

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Abstract Adaptive controllers with self-adjustment capabilities possess many advantages over conventional methods. An adaptive fuzzy controller may be implemented either in a direct form or in an indirect form, and it is generally referred to as a self-organizing controller. One advantage of fuzzy controllers is their simple computation requirements in comparison with more algorithmic-based controllers. A metal forming process is a good candidate for the implementation of fuzzy logic rules because of its nonlinear and stochastic properties. In this paper, a prototype electromechanical forming machine was developed and tested. The system includes an optical sensor, an AC induction motor, a servo controller, a forming mechanism, and a microprocessor. The measured state variable is processed by the CPU with a fuzzy logic algorithm. The controller utilizes primary and secondary errors between the actual response and desired output to conduct rule reasoning. Results from testing are presented.
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Tarannum, Shahla, and Suraiya Jabin. "A comparative study on Fuzzy Logic and Intuitionistic Fuzzy Logic." In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2018. http://dx.doi.org/10.1109/icacccn.2018.8748844.

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Reports on the topic "Fuzzy-logic control"

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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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Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other tasks, however, computers perform very poorly at tasks that humans find to be trivial. A discussion of the differences in the way humans and computer process information is crucial to an understanding of the field of artificial intelligence. In this project, several artificial intelligence/ intelligent control systems were examined: heuristic search techniques, adaptive control, expert systems, fuzzy logic, neural networks, and genetic algorithms. Of these, neural networks showed the most potential for use on large bore engines because of their ability to recognize patterns in incomplete, noisy data. Two sets of experimental tests were conducted to test the predictive capabilities of neural networks. The first involved predicting the ignition timing from combustion pressure histories; the best networks responded within a specified tolerance level 90% to 98.8% of the time. In the second experiment, neural networks were used to predict NOx, A/F ratio, and fuel consumption. NOx prediction accuracy was 91.4%, A/F ratio accuracy was 82.9%, and fuel consumption accuracy was 52.9%. This report documents the assessment of the state of the art of artificial intelligence for application to the monitoring and control of large-bore natural gas engines.
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