Relevant bibliographies by topics / Intelligent fuzzy controller

Contents

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

Academic literature on the topic 'Intelligent fuzzy controller'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Intelligent fuzzy controller"

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Nowaková, Jana, and Miroslav Pokorný. "Intelligent Controller Design by the Artificial Intelligence Methods." Sensors 20, no. 16 (2020): 4454. http://dx.doi.org/10.3390/s20164454.

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With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intell
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He, Fang. "The Study on Field Programmable Gate Array Based on PID Controllers in Real Time." Applied Mechanics and Materials 66-68 (July 2011): 1923–29. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1923.

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In the present work PID control and the fuzzy logic based intelligent control is used to control the rectilinear plant vibrations. PID controller in real time mode is implemented on FPGA. For implementation of fuzzy logic based intelligent controller the FPGA is used as real time data acquisition (DAQ) platform and the fuzzy controller is implemented on the host PC. The sole reason for using FPGA as data acquisition platform was that the LabVIEW FPGA software module is not supporting the complex mathematics required for the fuzzy logic control and also the rectilinear plant interface with the
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S.R. Krishnam Naidu, R., and U. Salma. "Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor." International Journal of Engineering & Technology 7, no. 2.21 (2018): 112. http://dx.doi.org/10.14419/ijet.v7i2.21.11847.

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This paper presents a hybrid intelligent and design in a real time for the rapid prototyping of a robust fuzzy controller along with conventional Proportional– Integral and Derivative (PID) controller that allows quick insight of these integrated designs. The design procedure of the parallel fuzzy PID and its combination with the traditional PID in a universal control scheme are extended. The structural design of the parallel fuzzy PID controller is composed of three fuzzy sub controllers which are connected in parallel. These parallel sub controllers are assembled to get the proposed parallel
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Borunda, Monica, Raul Garduno, Javier de la Cruz Soto, and Rafael Alfonso Figueroa Díaz. "Intelligent Control of an Experimental Small-Scale Wind Turbine." Energies 17, no. 22 (2024): 5656. http://dx.doi.org/10.3390/en17225656.

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Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind
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Zada, Fatma, Shawket K. Guirguis, and Walied M. Sead. "Hybrid Neural-Fuzzy Controller for Motorized Robot Arm." Advanced Materials Research 403-408 (November 2011): 5068–75. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5068.

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In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to n
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Sun, Hong Xing, Chuang Gao, and Na Li. "Research on Dissolved Oxygen Intelligent Control System in Waste Water Treatment." Advanced Materials Research 591-593 (November 2012): 1461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1461.

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By combining with the research hot spot of intelligent control, the oxygen fuzzy-single neuron PID compound controller is designed based on control increment. It contains the advantages of fuzzy controller and the single neuron PID controller. The problem of causing the oscillation in switching two kinds of controllers is solved. The results of the dissolved oxygen fuzzy-single neurons PID controller and fuzzy controller simulation are compared. The results show that the control effect of dissolved oxygen is more ideal. Thus the algorithm is effective and feasible.
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Salma, S. K., and K. Radha Rani. "Transient Stability Enhancement Using Matrix Converter Based UPFC with Intelligent Controlling Techniques." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 8 (2015): 01–12. http://dx.doi.org/10.53555/nneee.v2i8.178.

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This paper presents a matrix converter based UPFC for enhancing the transient stability. Conventional methods use a two step based UPFC (DC-AC-DC). In the proposed model the conventional UPFC is replaced by a Matrix Converter which is controlled with two different controllers which include fuzzy logic controller and the other with an added integrator which is referred to be a HYBRID fuzzy logic controller. In this paper, the working of matrix converter based UPFC with the two controller techniques under certain disturbances is observed using the MATLAB software and the results are verified.
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Ramezanifard, Mehdi. "Design of Intelligent Controllers for Double Inverted Pendulum." Ciência e Natura 37 (December 19, 2015): 406. http://dx.doi.org/10.5902/2179460x20802.

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In this paper, first a fuzzy controller is proposed for systems comprising two inverted pendulums placed in the same plane, where one pendulum moves along the X-axis and the other moves along the Y-axis; rules of a Fuzzy-Mamdani controller is improved by considering the behavior of an operator and a the developed expert system and by trial and error in the observation of the system’s behavior. Simulation results obtained in MATLAB are proposed and then two other intelligent methods are presented. In the second proposed method, a TSK controller is designed with Anfis training approach and its s
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Mekrini, Zineb, and Seddik Bri. "Fuzzy Logic Application for Intelligent Control of An Asynchronous Machine." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (2017): 61. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp61-70.

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<p>The aim of this article is propose a method to improve the direct torque control and design a Fuzzy Logic based Controller which can take necessary control action to provide the desired torque and flux of an asynchronous machine. It’s widely used in the industrial application areas due to several features such as fast torque response and less dependence on the rotor parameters. The major problem that is usually associated with DTC control is the high torque ripple as it is not directly controlled. The high torque ripple causes vibrations to the motor which may lead to component lose,
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Hadi, M. Sukri, Intan Z. Mat Darus, M. Osman Tokhi, and Mohd Fairus Jamid. "Active vibration control of a horizontal flexible plate structure using intelligent proportional–integral–derivative controller tuned by fuzzy logic and artificial bee colony algorithm." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (2019): 1159–71. http://dx.doi.org/10.1177/1461348419852454.

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This paper presents the development of an intelligent controller for vibration suppression of a horizontal flexible plate structure using hybrid Fuzzy–proportional–integral–derivative controller tuned by Ziegler–Nichols tuning rules and intelligent proportional–integral–derivative controller tuned by artificial bee colony algorithm. Active vibration control technique was implemented during the development of the controllers. The vibration data obtained through experimental rig was used to model the system using system identification technique based on auto-regressive with exogenous input model
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Dissertations / Theses on the topic "Intelligent fuzzy controller"

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Saeed, Bahghtar Ibraheem. "Design of a wireless intelligent fuzzy controller network." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24569/.

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Since the first application of fuzzy logic in the field of control engineering, fuzzy logic control has been successfully employed in controlling a wide variety of applications, such as commercial appliances, industrial automation, robots, traffic control, cement kilns and automotive engineering. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic expressions. Despite these achievements, however, there is still a lack of an empirical or analytical design study which adequately addresses a systematic auto-tuning met
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Khor, Jeen Ghee. "An intelligent controller for synchronous generators." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/4125.

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Nainar, Irshad. "An adaptive fuzzy logic controller for intelligent networking and control." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1996. https://ro.ecu.edu.au/theses/1466.

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In this thesis, we present a fuzzy logic control scheme to regulate the flow of traffic approaching a set of intersections. An adaptive Fuzzy Logic Traffic Controller (FLTC) is used to adjust the green phase split of the north-south and east-west approaches of a set of traffic signals based on the actual traffic approaching the intersection. Each intersection is coordinated with its neighbouring intersections by adjusting the offset of the local intersection. The offset is adjusted by a local fuzzy logic controller loacted at each intersection. A new fuzzy control scheme, using a supervisory F
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Chrysanthakopoulos, Georgios. "A fuzzy-logic autonomous agent, applied as a supervisory controller in a simulated environment /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6044.

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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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Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.

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This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discu
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Ari, Evrim Onur. "Fuzzy Actor-critic Learning Based Intelligent Controller For High-level Motion Control Of Serpentine Robots." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606777/index.pdf.

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In this thesis, an intelligent controller architecture for gait selection of a serpentine robot intended to be used in search and rescue tasks is designed, developed and simulated. The architecture is independent of the configuration of the robot and the robot is allowed to make different kind of movements, similar to grasping. Moreover, it is applicable to parallel processing in several aspects and it is an implementation of a controller network on robot segment network. In the architecture several behaviors are defined for each of the segments. Every behavior is realized in the form of Fuzzy
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Song, Yang. "Intelligent PID controller based on fuzzy logic control and neural network technology for indoor environment quality improvement." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14300/.

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The demand for better indoor environment has led to a wide use of heating, ventilating and air conditioning (HVAC) systems. Employing advanced HVAC control strategies is one of the strategies to maintain high quality indoor thermal comfort and indoor air quality (IAQ). This thesis aims to analyse and discuss the potential of using advanced control methods to improve the indoor occupants’ comfort. It focuses on the development of controllers of the major factors of indoor environment quality in buildings including indoor air temperature, indoor humidity and indoor air quality. Studies of the de
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Hitchings, Mark R., and n/a. "Distance and Tracking Control for Autonomous Vehicles." Griffith University. School of Microelectronic Engineering, 1999. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050902.084155.

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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases tra
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Hitchings, Mark. "Distance and Tracking Control for Autonomous Vehicles." Thesis, Griffith University, 1999. http://hdl.handle.net/10072/366396.

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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases tra
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Books on the topic "Intelligent fuzzy controller"

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Faghih, Nezameddin, and Farshad Alamdar Yuli. Fuzzy Controllers in Intelligent Material Control Systems. Farassan, 2007.

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Palm, Rainer. Model Based Fuzzy Control: Fuzzy Gain Schedulers and Sliding Mode Fuzzy Controllers. Springer Berlin Heidelberg, 1997.

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Pratap, Khedkar, and Ames Research Center, eds. Learning and tuning fuzzy logic controllers through reinforcement. Nasa/Ames Research Center, 1992.

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Pratap, Khedkar, and Ames Research Center, eds. Learning and tuning fuzzy logic controllers through reinforcement. Nasa/Ames Research Center, 1992.

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

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Center, Langley Research, ed. Fuzzy logic decoupled lateral control for general aviation airplanes. National Aeronautics and Space Administration, Langley Research Center, 1997.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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1944-, Nguyen Hung T., ed. A first course in fuzzy and neural control. Chapman & Hall/CRC Press, 2003.

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Klawonn, Frank, Kai Michels, Andreas Nürnberger, and Rudolf Kruse. Fuzzy Control: Fundamentals, Stability and Design of Fuzzy Controllers. Springer London, Limited, 2007.

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Castillo, Oscar, Patricia Melin, and Leslie Astudillo. Chemical Optimization Algorithm for Fuzzy Controller Design. Springer, 2014.

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Book chapters on the topic "Intelligent fuzzy controller"

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Kazemian, H. B. "Intelligent Fuzzy PID Controller." In Foundations of Generic Optimization. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6668-9_7.

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Batur, C., C. C. Chan, and A. Srinivasan. "Fuzzy Model Based Predictive Controller." In Methods and Applications of Intelligent Control. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5498-7_6.

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Nguyen, Ngoc-Tung, and Hong-Hee Lee. "Fuzzy PI Controller for Grid-Connected Inverters." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25944-9_39.

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Kumawat, Gaurav, and Jayashri Vajpai. "Fuzzy Sliding Mode Controller for Dynamic Nonlinear Systems." In Intelligent Human Computer Interaction. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52503-7_18.

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Kumar, Mahesh, Krishna Kumar Pandey, Amita Kumari, and Jagdish Kumar. "Fuzzy Logic Based MPPT Controller for PV Panel." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5243-4_46.

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Feizollah, Ali, Shahaboddin Shamshirband, Nor Badrul Anuar, Rosli Salleh, and Miss Laiha Mat Kiah. "Anomaly Detection Using Cooperative Fuzzy Logic Controller." In Intelligent Robotics Systems: Inspiring the NEXT. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40409-2_19.

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Denizci, Aykut, Sinan Karadeniz, and Cenk Ulu. "Fuzzy Cognitive Map Based PI Controller Design." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_146.

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Ponnambalam, P., M. Praveen Kumar, V. Surendar, and G. Gokulakrishnan. "Fuzzy Controller for Flying Capacitor Multicell Inverter." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_43.

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Ponnambalam, P., B. Shyam Sekhar, M. Praveenkumar, V. Surendar, and P. Ravi Teja. "Fuzzy Controller for Reversing Voltage Topology MLI." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_51.

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De Maity, Ritu Rani, and Rajani K. Mudi. "Fuzzy Rule-Based Adaptive Proportional Derivative Controller." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11933-5_22.

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Conference papers on the topic "Intelligent fuzzy controller"

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S, Thasni, and Lal Priya P S. "EEG Controlled Robotic Arm Using Fuzzy Logic Controller." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10624834.

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Qishqish, Asma R., A. M. EIbreki, Tawfiq H. Elmenfy, and Zakariya Rajab. "Prosthetic Hand Controlled Via Intelligent Fuzzy Controller." In ICEMIS'20: The 6th International Conference on Engineering & MIS 2020. ACM, 2020. http://dx.doi.org/10.1145/3410352.3410819.

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Parimi, V. Ram Mohan, and Devendra P. Garg. "Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60502.

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This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is
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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
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Guo Chen, D. P. Kwok, Sun Zengqi, and Yu Jingzhe. "Multimode fuzzy-CMAC intelligent controller." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609548.

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Batayneh, Wafa, and Nash’at Nawafleh. "Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51362.

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This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to t
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Santos, Matilde, Victoria Lopez, and Franciso Morata. "Intelligent fuzzy controller of a quadrotor." In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2010. http://dx.doi.org/10.1109/iske.2010.5680812.

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Erenoglu, I., I. Eksin, E. Yesil, and M. Guzelkaya. "An Intelligent Hybrid Fuzzy Pid Controller." In 20th Conference on Modelling and Simulation. ECMS, 2006. http://dx.doi.org/10.7148/2006-0062.

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Shirzi, Moteaal Asadi, and M. R. Hairi-Yazdi. "Active tracking using Intelligent Fuzzy Controller and kernel-based algorithm." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007585.

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Dominik, Ireneusz. "Interval Type-2 Fuzzy Logic Control of NM70 Shape Memory Actuator." In ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smasis2014-7606.

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The main aim of this article is to present the usage of type-2 fuzzy logic controller to control a shape memory actuator. To enhance real-time performance simplified interval fuzzy sets were used. The algorithm was implemented in the ATmega32 microcontroller. The dedicated PC application was also built. The fuzzy logic controller type-2 was tested experimentally by controlling position of the shape memory alloy actuator NM70 which despite its small size distinguishes itself by its strength. The obtained results confirmed that type-2 fuzzy controller performed efficiently with a difficult to co
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Reports on the topic "Intelligent fuzzy controller"

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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, 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
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