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Relevant bibliographies by topics / Controller of fuzzy logic

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Academic literature on the topic 'Controller of fuzzy logic'

Author: Grafiati

Published: 28 May 2022

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Journal articles on the topic "Controller of fuzzy logic"

1

Awad, Fathy H., Ahmed A. Mansour, and Essam E. Abou Elzahab. "Thyristor Controlled Reactor with Different Topologies Based on Fuzzy Logic Controller." International Journal of Engineering Research 4, no. 9 (September 1, 2015): 498–505. http://dx.doi.org/10.17950/ijer/v4s9/906.

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Mahdi, Mohammed Chessab, Abdal-Razak Shehab, and Mohammed J. F. Al Bermani. "Direct Fuzzy Logic Controller for Nano-Satellite." Journal of Control Engineering and Technology 4, no. 3 (July 30, 2014): 210–19. http://dx.doi.org/10.14511/jcet.2014.040307.

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K.M.MAKWANA, K. M. MAKWANA, Dr B. R. PAREKH Dr.B.R.PAREKH, and SHEETAL SHINKHEDE. "Fuzzy Logic Controller Vs Pi Controller for Induction Motor Drive." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 315–18. http://dx.doi.org/10.15373/2249555x/july2013/97.

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Muhammad Saqlain, Kashaf Naz, Kashf Gaffar, and Muhammad Naveed Jafar. "Fuzzy Logic Controller." Scientific Inquiry and Review 3, no. 3 (September 20, 2019): 16–29. http://dx.doi.org/10.32350/sir.33.02.

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In this research paper, the impact of water pH on detergent was measured by constructing a Fuzzy Logic Controller (FLC) based on Intuitionistic Fuzzy Numbers (IFNs) by incorporating three linguistic inputs and one output as taken by Saeed. M. et al. [1]. The inference process was carried out using MATLAB fuzzy logic toolbox and the results were compared with FLC based on fuzzy numbers. The objective of the study was the comparison of FLC based on intuitionistic and fuzzy numbers. The results showed that FLC based on IFNs is approximately the same but has more precise values. So, IFNs based FLC can be used in the Instinctive Laundry System.

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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

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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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Akbatı, Onur, Hatice Didem Üzgün, and Sirin Akkaya. "Hardware-in-the-loop simulation and implementation of a fuzzy logic controller with FPGA: case study of a magnetic levitation system." Transactions of the Institute of Measurement and Control 41, no. 8 (December 13, 2018): 2150–59. http://dx.doi.org/10.1177/0142331218813425.

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This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.

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Raja, S., and N. P. Ananthamoorthy. "Evaluation of Newly Developed Liquid Level Process with PD and PID Controller without Altering Material Characteristics." Journal of New Materials for Electrochemical Systems 24, no. 3 (September 30, 2021): 218–23. http://dx.doi.org/10.14447/jnmes.v24i3.a10.

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This article explains the design of fuzzy logic controllers (FLCs) for level processes which is generally used in numerous control operations. The main purpose of the proposed design is to maintain the liquid level in the tank at the desired level. In this paper, the fuzzy logic controller is chosen as the controller for the level process because of its fault tolerance, knowledge representation, expertise, non-linearity, uncertainty, and real-time manipulation. Fuzzy logic controllers have been developed and compared in the Mamdani version. Performance on proportional derivatives (PD) and proportional-integral-derivatives (PID) controllers. Whereas traditional PD and PID controllers are simple, dependable and eliminate steady-state errors, fuzzy logic controllers are rule-based systems that are a logical model of human behavior in processes of the proposed design. The response is provided as follows: The LabVIEW software has been validated. It is used to simulate the proposed system. Comparing error indicators such as PD controller, PID controller, fuzzy logic controller integral absolute error, integral quadratic error, time and absolute error integral, time and quadratic error integral, fuzzy logic controller is observed from the simulation results. increase. It offers better performance than other controllers.

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Yamakawa, Takeshi. "A fuzzy logic controller." Journal of Biotechnology 24, no. 1 (June 1992): 1–32. http://dx.doi.org/10.1016/0168-1656(92)90059-i.

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Dissertations / Theses on the topic "Controller of fuzzy logic"

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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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GarcÃa, 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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Menon, Vinay. "Fuzzy logic controller for an artificial heart." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq32405.pdf.

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Vijeh, Nader. "Microprocessor engineering aspects of a self-organizing fuzzy-logic controller." Thesis, University of Exeter, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328485.

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Mohan, Ashwin. "A fuzzy controller developed in RSLogix 5000 using ladder logic and function blocks implemented on a Control Logix PLC /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1420941.

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Jiang, Xiaowen. "A fuzzy logic controller for intestinal levodopa infusion in Parkinson’s disease." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4727.

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The aim of this work is to evaluate the fuzzy system for different types of patients for levodopa infusion in Parkinson Disease based on simulation experiments using the pharmacokinetic-pharmacodynamic model. Fuzzy system is to control patient’s condition by adjusting the value of flow rate, and it must be effective on three types of patients, there are three different types of patients, including sensitive, typical and tolerant patient; the sensitive patients are very sensitive to drug dosage, but the tolerant patients are resistant to drug dose, so it is important for controller to deal with dose increment and decrement to adapt different types of patients, such as sensitive and tolerant patients. Using the fuzzy system, three different types of patients can get useful control for simulating medication treatment, and controller will get good effect for patients, when the initial flow rate of infusion is in the small range of the approximate optimal value for the current patient’ type.

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Sheng, Lan. "Fuzzy logic controller synthesis for electro-mechanical systems with nonlinear friction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/MQ35526.pdf.

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Hu, Yanting. "Advanced control system for stand-alone diesel engine driven-permanent magnet generator sets." Thesis, De Montfort University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366632.

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Tam, Kin Seng. "Intelligent power factor controller with new measuring method and fuzzy logic control." Thesis, University of Macau, 1998. http://umaclib3.umac.mo/record=b1447758.

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Almardy, Mohamed. "Design of fuzzy logic controller for the cathodic protection of underground pipelines." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0007/MQ43133.pdf.

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Books on the topic "Controller of fuzzy logic"

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Choy, Vanessa W. S. Real-time online fuzzy logic controller for laser interstitial thermal therapy. Ottawa: National Library of Canada, 2003.

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Suryana, Yaya. Genetic design of fuzzy logic controllers. Salford: University of Salford, 1995.

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Lakhanpal, Sanjiv K. Designing and optimizing fuzzy-logic controllers. Ottawa: National Library of Canada, 1993.

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Bennett, W. E. Construction equipment emerging technologies: Fuzzy logic controllers. Springfield, Va: Available from National Technical Information Service, 1995.

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McClintock, Shaunna. Soft computing: A fuzzy logic controlled genetic algorithm environment. [S.l: The Author], 1999.

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Dualibe, Carlos, Michel Verleysen, and Paul G. A. Jespers. Design of Analog Fuzzy Logic Controllers in CMOS Technologies. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/b101857.

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Dualibe, Carlos. Design of analog fuzzy logic controllers in CMOS technologies: Implementation, test, and application. Boston: Kluwer Academic Publishers, 2003.

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Michel, Verleysen, and Jespers Paul G, eds. Design of analog fuzzy logic controllers in CMOS technologies: Implementation, test, and application. Boston: Kluwer Academic Publishers, 2003.

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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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Bothe, Hans-Heinrich. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21929-4.

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Book chapters on the topic "Controller of fuzzy logic"

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Kelber, J., S. Triebel, and G. Scarbata. "Modulgeneratoren für Fuzzy-Controller." In Fuzzy Logic, 32–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_4.

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Grantner, J. L. "Parallel Algorithm for Fuzzy Logic Controller." In Fuzzy Logic, 177–95. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_6.

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Bien, Zeungnam, and Jongcheol Park. "Hybrid Fuzzy Self-Organizing Controller for Visual Tracking." In Fuzzy Logic, 569–78. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_52.

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Saffiotti, Alessandro, Enrique H. Ruspini, and Kurt Konolige. "A Fuzzy Controller for Flakey, An Autonomous Mobile Robot." In Fuzzy Logic, 3–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_1.

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Abe, Jair Minoro. "Para-Fuzzy Logic Controller." In Lecture Notes in Computer Science, 935–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30133-2_123.

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Qiao, Wu Zhi, and Masaharu Mizumoto. "On the Crisp-type Fuzzy Controller: Behaviour Analysis and Improvement." In Fuzzy Logic, 117–39. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_4.

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Graham, Bruce, and Robert Newell. "An adaptive fuzzy model-based controller." In Fuzzy Logic and Fuzzy Control, 56–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7_19.

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Yen, John, and Nathan Pfluger. "Using fuzzy logic in a mobile robot path controller." In Fuzzy Logic and Fuzzy Control, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7_25.

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Niittymäki, Jarkko, Riku Nevala, and Marko Mäenpää. "Fuzzy Logic-Based Traffic Controller." In Soft Computing in Industrial Electronics, 249–78. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1783-6_7.

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Hajiyev, Chingiz, Halil Ersin Soken, and Sıtkı Yenal Vural. "Fuzzy Logic-Based Controller Design." In State Estimation and Control for Low-cost Unmanned Aerial Vehicles, 201–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16417-5_11.

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Conference papers on the topic "Controller of fuzzy logic"

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

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Yager, Ronald R. "Fuzzy logic controller structures." In Boston - DL tentative, edited by David P. Casasent. SPIE, 1991. http://dx.doi.org/10.1117/12.25168.

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Luo, Jia, and Craig A. Kluever. "A Fuzzy Logic Controller for Orbital Rendezvous." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0407.

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Abstract A new application of fuzzy logic is investigated. A control scheme based on fuzzy logic techniques is developed for the orbital rendezvous phase of a low-thrust spacecraft. The minimum-fuel trajectory between specified energy levels is utilized as a reference trajectory. The fuzzy logic controller responds effectively to a range of initial state perturbations and results in very precise terminal orbit conditions with good tracking performance. The fuzzy logic controller exhibits better tracking performance and in some cases substantial fuel savings compared to a Linear perturbation control method. Numerical results are presented for a range of rendezvous maneuvers.

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Shi, Linda Z., and Mohamed B. Trabia. "Comparison of Two Distributed Fuzzy Logic Controllers for Flexible-Link Manipulators." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2334.

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Abstract Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers. An expert in a particular system can usually design a fuzzy logic controller for it easily as can be seen in many applications where fuzzy logic has been already successfully implemented. On the other hand, fuzzy logic controllers are not readily available for flexible-link manipulators. This paper presents two different approaches to design distributed controllers for flexible-link manipulators. The first approach, which is based on observing the performance of flexible manipulators, uses a distributed controller composed of two PD-like fuzzy logic controllers; one controller controls the joint angle while the other controls the tip vibration. The second distributed controller is based on evaluating the importance of the parameters of the system. The most two important parameters, joint and tip point velocities, are grouped together in the same fuzzy logic controller. The other parameters, joint angle and tip point displacement, are used in the second fuzzy logic controller. Both approaches are tuned using nonlinear programming. The paper compares these two approaches with tracking using a linear Quadratic Regulator (LQR).

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Ozatay, Evren, Samim Y. Unlusoy, and Murat A. Yildirim. "Design of Fuzzy Logic Controller for Four Wheel Steering System." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84114.

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Integration of the driver’s steering input together with the four-wheel steering system (4WS) in order to improve the vehicle’s dynamic behavior with respect to yaw rate and body sideslip angle is possible with intelligent vehicle dynamics control systems. The goal of this study is to develop a fuzzy logic controller for this purpose. In the first stage of the study, a three-degree of freedom nonlinear vehicle model including roll dynamics is developed. The Magic Formula is applied in order to formulate the nonlinear characteristics of the tires. In the design of the fuzzy logic controller, a two-dimensional rule table is created based on the error and on the change in the error of sideslip angle, which is to be minimized. Fuzzy logic controlled model is then compared with front wheel steering vehicle and the vehicles having different control strategies that have previously been studied in literature. Simulations indicate that fuzzy logic controlled vehicle can provide zero body sideslip angle in transient motion and quick response in terms of yaw rate during steady state cornering and lane change maneuvers.

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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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Trabia, Mohamed B., Jamil M. Renno, and Kamal A. F. Moustafa. "A Single Phase Anti-Swing Fuzzy Logic Controller for an Overhead Crane With Hoisting." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13837.

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This paper presents a novel approach for automatically creating anti-swing fuzzy logic controllers for overhead cranes with hoisting. This approach uses the inverse dynamics of the overhead crane to determine the ranges of the variables of the controllers. The control action is distributed among three fuzzy logic controllers (FLCs): travel controller, hoist controller, and anti-swing controller. Simulation examples show that the proposed controller can successfully drive overhead cranes under various operating conditions.

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Dewantoro, Gunawan, and Yong-Lin Kuo. "Robust speed-controlled permanent magnet synchronous motor drive using fuzzy logic controller." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007419.

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Kaur, G., A. Chauhan, and P. V. Subramanyam. "Fuzzy logic based temperature controller." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547340.

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Attia, F., and M. Upadhyaya. "Fuzzy logic based robotic controller." In Conference on Intelligent Robots in Factory, Field, Space, and Service. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1202.

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Reports on the topic "Controller of fuzzy logic"

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Rubaai, Ahmed. Real-Time Implementation of a Fuzzy Logic Controller for DC-DC Switching Converters. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430407.

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.

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Tailor, Sanjay. Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada310470.

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Meitzler, Thomas J., David Bednarz, E. J. Sohn, Kimberly Lane, and Darryl Bryk. Fuzzy Logic Based Image Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada405123.

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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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Bharadwaj, Arjun, and Jerry M. Mendel. Fuzzy Logic for Unattended Ground Sensor Fusion. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444339.

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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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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada419061.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada434605.

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Wojcik, Frank A. Human Factors Reach Comfort Determination Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada517381.

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