Journal articles: 'Controller of fuzzy logic'

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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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Abdalla, M. O., and T. A. Al–Jarrah. "Autogeneration of Fuzzy Logic Rule-Base Controllers." Applied Mechanics and Materials 110-116 (October 2011): 5123–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5123.

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A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.

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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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Prabhavathi.O.N.CH, Prabhavathi O. N. CH, and SK Saidulu SK.Saidulu. "Implementation of a Fuzzy Logic Controller on an FPGA." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 170–71. http://dx.doi.org/10.15373/22778179/mar2013/54.

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A., Senthilnathan. "Fuzzy Logic Controller based Zeta Converter for BLDC Motor." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 125–33. http://dx.doi.org/10.5373/jardcs/v12i7/20201992.

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Kim, Min-Soeng, Sun-Gi Hong, and Ju-Jang Lee. "Self-Learning Fuzzy Logic Controller using Q-Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (September 20, 2000): 349–54. http://dx.doi.org/10.20965/jaciii.2000.p0349.

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Fuzzy logic controllers consist of if-then fuzzy rules generally adopted from a priori expert knowledge. However, it is not always easy or cheap to obtain expert knowledge. Q-learning can be used to acquire knowledge from experiences even without the model of the environment. The conventional Q-learning algorithm cannot deal with continuous states and continuous actions. However, the fuzzy logic controller can inherently receive continuous input values and generate continuous output values. Thus, in this paper, the Q-learning algorithm is incorporated into the fuzzy logic controller to compensate for each method’s disadvantages. Modified fuzzy rules are proposed in order to incorporate the Q-learning algorithm into the fuzzy logic controller. This combination results in the fuzzy logic controller that can learn through experience. Since Q-values in Q-learning are functional values of the state and the action, we cannot directly apply the conventional Q-learning algorithm to the proposed fuzzy logic controller. Interpolation is used in each modified fuzzy rule so that the Q-value is updatable.

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Braz-César, Manuel, and Rui Barros. "Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure." International Journal of Structural Stability and Dynamics 17, no. 05 (December 8, 2016): 1740007. http://dx.doi.org/10.1142/s0219455417400077.

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Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.

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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 neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

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Abdalla, Turki, Haroution Hairik, and Adel Dakhil. "Direct Torque Control System for a Three Phase Induction Motor With Fuzzy Logic Based Speed Controller." Iraqi Journal for Electrical and Electronic Engineering 6, no. 2 (December 1, 2010): 131–38. http://dx.doi.org/10.37917/ijeee.6.2.8.

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This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC) drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI) controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI) to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI) controller.

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Paksoy, Mahmut, Rahmi Guclu, and Saban Cetin. "Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper." Advances in Mechanical Engineering 6 (January 1, 2014): 816813. http://dx.doi.org/10.1155/2014/816813.

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Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.

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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 traditional DAQ was not available. The comparative study of performance of intelligent controllers like fuzzy PD and fuzzy PI+ fuzzy PD is done with the conventional controllers on the basis of above mentioned performance indices. The result of the fuzzy PI + fuzzy PD controller is found to be the best among all.

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Can, Erol. "MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE." Acta Polytechnica 59, no. 1 (February 28, 2019): 1–11. http://dx.doi.org/10.14311/ap.2019.59.0001.

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A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

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Puchalski, Bartosz, Kazimierz Duzinkiewicz, and Tomasz Rutkowski. "Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant." Archives of Control Sciences 25, no. 4 (December 1, 2015): 429–44. http://dx.doi.org/10.1515/acsc-2015-0028.

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Abstract In the paper, analysis of multi-region fuzzy logic controller with local PID controllers for steam generator of pressurized water reactor (PWR) working in wide range of thermal power changes is presented. The U-tube steam generator has a nonlinear dynamics depending on thermal power transferred from coolant of the primary loop of the PWR plant. Control of water level in the steam generator conducted by a traditional PID controller which is designed for nominal power level of the nuclear reactor operates insufficiently well in wide range of operational conditions, especially at the low thermal power level. Thus the steam generator is often controlled manually by operators. Incorrect water level in the steam generator may lead to accidental shutdown of the nuclear reactor and consequently financial losses. In the paper a comparison of proposed multi region fuzzy logic controller and traditional PID controllers designed only for nominal condition is presented. The gains of the local PID controllers have been derived by solving appropriate optimization tasks with the cost function in a form of integrated squared error (ISE) criterion. In both cases, a model of steam generator which is readily available in literature was used for control algorithms synthesis purposes. The proposed multi-region fuzzy logic controller and traditional PID controller were subjected to broad-based simulation tests in rapid prototyping software - Matlab/Simulink. These tests proved the advantage of multi-region fuzzy logic controller with local PID controllers over its traditional counterpart.

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Gülbahçe, Erdi, and Mehmet Çelik. "Fuzzy logic aided PPF controller design to active vibration control of a flexible beam." Journal of Structural Engineering & Applied Mechanics 4, no. 3 (September 30, 2021): 184–95. http://dx.doi.org/10.31462/jseam.2021.03184195.

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This paper presents a fuzzy-logic-based observer and a positive position feedback controller to reduce a standard beam's free vibrations using a piezoelectric actuator. It is aimed that fuzzy-logic-based observer is used as feed-through and improves the overall performance of the PPF controller. For this aim, the cantilever beam and a piezoelectric patch are initially numerically modeled using the finite element method considering the close loop control algorithm. The displacement and strain responses results are compared with the experimental model. Then, two controllers are applied to the designed system: positive position feedback (PPF) and fuzzy-logic-based positive position feedback (FLBPPF). The uncontrolled and controlled system responses are investigated and compared in terms of the linear strain and tip displacement results. Using the FLBPPF controller, the settling times of controlled systems are decreased by about 20.7% and 41.6% regarding the linear strain and tip displacement response compared to the PPF controller.

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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 (August 31, 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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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 (July 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, bearing failure or resonance. The fuzzy logic controller is applied to reduce electromagnetic torque ripple. In this proposed technique, the two hysteresis controllers are replaced by fuzzy logic controllers and a methodology for implementation of a rule based fuzzy logic controller are presented. The simulation by Matlab/Simulink was built which includes induction motor d-q model, inverter model, fuzzy logic switching table and the stator flux and torque estimator. The validity of the proposed method is confirmed by the simulative results of the whole drive system and results are compared with conventional DTC method. </p>

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Oudda, Meryem, and Abdeldjebar Hazzab. "Photovoltaic System with SEPIC Converter Controlled by the Fuzzy Logic." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 4 (December 1, 2016): 1283. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1283-1293.

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<span lang="EN-US">In this work, a fuzzy logic controller is used to control the output voltage of a photovoltaic system with a DC-DC converter; type Single Ended Primary Inductor Converter (SEPIC). The system is designed for 210 W solar PV (SCHOTT 210) panel and to feed an average demand of 78 W. This system includes solar panels, SEPIC converter and fuzzy logic controller. The SEPIC converter provides a constant DC bus voltage and its duty cycle controlled by the fuzzy logic controller which is needed to improve PV panel’s utilization efficiency. A fuzzy logic controller (FLC) is also used to generate the PWM signal for the SEPIC converter. </span>

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Sheet, Amer Farhan. "Optimization of DC motor speed control based on fuzzy logic-PID controller." Analysis and data processing systems, no. 3 (September 30, 2021): 143–53. http://dx.doi.org/10.17212/2782-2001-2021-3-143-153.

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In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

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Khin, Aye Mar, and Aye Myint Thwe. "Fuzzy Logic Based Dryer Controller." International Journal of Advances in Scientific Research and Engineering 06, no. 04 (2020): 106–12. http://dx.doi.org/10.31695/ijasre.2020.33795.

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Acosta, Nelson, Jean-Pierre Deschamps, and Gustavo Sutter. "Customized Fuzzy Logic Controller Generator." IFAC Proceedings Volumes 30, no. 1 (February 1997): 87–92. http://dx.doi.org/10.1016/s1474-6670(17)44612-x.

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Aminifar, Sadegh. "Voltage-mode fuzzy logic controller." Indian Journal of Science and Technology 5, no. 11 (November 20, 2012): 1–4. http://dx.doi.org/10.17485/ijst/2012/v5i11.14.

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Tombs, J., A. J. Torralba, and L. G. Franquelo. "A PWM fuzzy logic controller." IEEE Micro 16, no. 5 (1996): 68–71. http://dx.doi.org/10.1109/40.540083.

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Zhang, B. S., and J. M. Edmunds. "Self-organising fuzzy logic controller." IEE Proceedings D Control Theory and Applications 139, no. 5 (1992): 460. http://dx.doi.org/10.1049/ip-d.1992.0057.

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Zong-Mu Yeh. "Adaptive multivariable fuzzy logic controller." Fuzzy Sets and Systems 86, no. 1 (February 1997): 43–60. http://dx.doi.org/10.1016/0165-0114(95)00374-6.

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Mamat, Normaisharah, Fitri Yakub, and Sheikh Ahmad Zaki Sheikh Salim. "Analysis of controllers in suppressing the structural building vibration." Malaysian Journal of Fundamental and Applied Sciences 15, no. 1 (February 4, 2019): 112–16. http://dx.doi.org/10.11113/mjfas.v15n2019.1101.

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Two degree of freedom (2 DOF) mass spring damper system is used in representing as building structure that dealing with the earthquake vibration. The real analytical input is used to the system that taken at El Centro earthquake that occurred in May 1940 with magnitude of 7.1 Mw. Two types of controller are presented in controlling the vibration which are fuzzy logic (FL) and sliding mode controller (SMC). The paper was aimed to improve the performance of building structure towards vibration based on proposed controllers. Fuzzy logic and sliding mode controller are widely known with robustness character. The mathematical model of two degree of freedom mass spring damper wasis derived to obtain the relationship between mass, spring, damper, force and actuator. Fuzzy logic and sliding mode controllers were implemented to 2 DOF system to suppress the earthquake vibration of two storeys building. Matlab/Simulink was used in designing the system and controllers to present the result of two storeys displacement time response and input control voltage for uncontrolled and controlled system. Then the data of earthquake disturbance was taken based on real seismic occurred at El Centro to make it as the force disturbance input to the building structure system. The controllers proposed would minimize the vibration that used in sample earthquake disturbance data. The simulation result was carried out by using Matlab/Simulink. The simulation result showed sliding mode controller was better controller than fuzzy logic. In specific, by using the controller, earthquake vibration can be reduced.

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Firmansyah, Riza Agung, and Dani Junianto. "Rancang Bangun Farming Box Dengan Pengaturan Suhu Menggunakan Fuzzy Logic Controller." ELKHA 12, no. 2 (October 11, 2020): 92. http://dx.doi.org/10.26418/elkha.v12i2.41196.

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Implementation of control systems has been carried out in many fields of science. One of it applications is in the agriculture fields. In this research we implemented a control system on farming in a box. Farming in a box is a system that uses old shipping containers for the purpose of growing plants in any environment. Inside shipping containers is fully assembled hydroponic pipe with air temperature control. In this research was built a little farming box from acryclic to imitate a shipping container. Main focus of this research is design an air temperature control using fuzzy logic controller. Fuzzy logic controller was choosen because many existing farming box use on off controller. In some application, fuzzy logic controller has better performance than on off controller. Farming box temperature is controlled by blowing cool air using an electric fan. In this case, cool air is produced by cold side of peltier. Electric fan speed is controlled by pulse width modulation signal (PWM) that generated from microcontroller. Air temperature data feedback is obtained from DHT 11 sensor that installed in a acrylic box. Sensor is physically connected with microcontroller and Fuzzy logic controller is embedded in microcontroller as an algorithm. Fuzzy logic controller was design with error temperature and error difference as an input, and duty cycle of PWM signal as output. Fuzzy logic controller system performs to reduce the temperature from 31,6 ° C to set poin 28° C in 71 seconds. Steady state error obtained by 1.28% and better than uncontrolled system that obtain steady state error 7,14%.

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Soetedjo, Aryuanto, Yusuf Nakhoda, and Choirul Saleh. "Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems." Electronics 7, no. 9 (September 10, 2018): 189. http://dx.doi.org/10.3390/electronics7090189.

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Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

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Farooq, Umar, K. M. Hasan, Athar Hanif, Muhammad Amar, and Muhammad Usman Asad. "Fuzzy Logic Based Path Tracking Controller for Wheeled Mobile Robots." International Journal of Computer and Electrical Engineering 6, no. 2 (2014): 145–50. http://dx.doi.org/10.7763/ijcee.2014.v6.811.

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NUGA, O. O., K. A. AMUSA, A. J. OLANIPEKUN, and A. ADEWUSI. "DYNAMIC BEHAVIOUR OF A MODELED TRANSPORTATION NETWORKED CONTROL SYSTEM FOR T-JUNCTION." Journal of Natural Sciences Engineering and Technology 16, no. 1 (May 22, 2017): 70–82. http://dx.doi.org/10.51406/jnset.v16i1.1804.

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Traffic congestion has been the major problem on most Nigeria roads. This is particularly due to the rapid increase in urban migration. Majority of the traffic control schemes adopted in the country to alleviate this problem are the fixed time controllers employed at all signalized intersections. This has resulted in increased traffic jam especially during the peak periods at most intersections on our highways. In this study, a fuzzy logic system to control traffic on signalized intersection has been proposed. The Fuzzy Logic Controller regulates the traffic signal timing, the green light extension and phase sequence to ensure smooth flow of traffic, thereby reducing traffic delays and thus increasing the intersection capacity. A fuzzy logic traffic control simulation model was developed and tested using MATLAB/ SIMULINK software. Comparative analysis was carried out between the fuzzy logic controller and a conventional fixed-time controller in order to determine the efficiency of the developed system. Evaluation results of the fuzzy logic traffic controller shows that vehicles spent less time at the intersection compared to the fixed time controller, that is, improved vehicular movement. Moreover, simulation results show that the fuzzy logic controller has better efficiency and that a huge improvement could be realized by adapting it in controlling traffic flow at intersections.

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Arulmozhi, N. "Bioreactor Control Using Fuzzy Logic Controllers." Applied Mechanics and Materials 573 (June 2014): 291–96. http://dx.doi.org/10.4028/www.scientific.net/amm.573.291.

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Bioreactors are characterized by high nonlinearities and are often subjected to parameter uncertainties and disturbances. The control of such processes is often difficult to achieve with traditional linear control techniques. In the present work, a Fuzzy logic controller is designed in two versions to a Bioreactor which exhibits input multiplicities in dilution rate on productivity. Fuzzy controller and Fuzzy tuned PI controller is designed to translate the information obtained from the operator’s experiences for designing an automatic control system The Performance of proposed Fuzzy logic controller versions and conventional PI controller have been analyzed and evaluated. The two Fuzzy controller versions provide stable and faster responses than conventional PI controller. Thus, Fuzzy control is found to overcome the control problems of PI controller due to the input multiplicities near optimal productivity. It is interesting to note that the present fuzzy logic controller is giving superior performance. The process is tested with the MATLAB/SIMULINK and Fuzzy Logic Toolbox. The simulation results were presented which illustrate the validity of the method.

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Hemeyine, Ahmed Vall, Ahmed Abbou, Anass Bakouri, Mohcine Mokhlis, and Sidi Mohamed ould Mohamed El Moustapha. "A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator." Inventions 6, no. 2 (March 24, 2021): 21. http://dx.doi.org/10.3390/inventions6020021.

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This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.

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You, Xiang Yang. "A Novel Sensor Less Control of Induction Motor Based on Fuzzy Sliding-Mode Structure." Advanced Materials Research 588-589 (November 2012): 684–87. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.684.

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A novel fuzzy sliding-mode structure has been proposed for Extend Kalman Filter (EKF) based on sensorless control of an induction motor in this paper. Fuzzy sliding-mode structure includes two nonlinear controllers, one of which is sliding mode type and the other is PI-fuzzy logic based controller. The new structure has two advantages: sliding-mode controller increasing system stability and PI-like fuzzy logic based controller reducing the chattering in permanent state. The scheme has been implemented and experimentally validated.

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Khoi, Phan Bui, and Hong Nguyen Xuan. "Fuzzy Logic-Based Controller for Bipedal Robot." Applied Sciences 11, no. 24 (December 15, 2021): 11945. http://dx.doi.org/10.3390/app112411945.

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In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

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A-Amir, Ahlam Najm, and Hanan A. R. Akkar. "Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA." European Journal of Engineering Research and Science 3, no. 5 (May 11, 2018): 1. http://dx.doi.org/10.24018/ejers.2018.3.5.661.

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In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.

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A-Amir, Ahlam Najm, and Hanan A. R. Akkar. "Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA." European Journal of Engineering and Technology Research 3, no. 5 (May 11, 2018): 1–8. http://dx.doi.org/10.24018/ejeng.2018.3.5.661.

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In this work an efficient Artificial Intelligent Robotic Fuzzy Logic Controller (AIRFC) system have been constructed to control the robot arm. A serial link Robot manipulator with 6 Degree of Freedom (DOF) from DFROBOT of code ROB0036 is used as a case study. A fuzzy logic type1 controller is implemented on LabVIEW to control each joint of the robot arm for nonlinearity measurements and a fuzzy logic type2 controller is applied which is more suitable for uncertainty. The hardware design is implemented and finally downloaded using the Field Programmable Gate Array (FPGA) kit named PCI-7833R from National Instrument. By using the LabVIEW FPGA MODEL the target board can be detected for software implementation of the controllers’ systems. The work shows that in case of type2 fuzzy logic the rise time is less than that of type1 fuzzy logic for the shoulder, wrist roll and the gripper angles and it is higher for base, elbow and wrist pitch angles. The settling time is the same in elbow and wrist pitch angles and for the type2 fuzzy controller it is less for other angles.

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Kalker, T. J. J., C. P. van Goor, P. J. Roeleveld, M. F. Ruland, and R. Babuška. "Fuzzy control of aeration in an activated sludge wastewater treatment plant: design, simulation and evaluation." Water Science and Technology 39, no. 4 (February 1, 1999): 71–78. http://dx.doi.org/10.2166/wst.1999.0191.

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Fuzzy logic can in several ways be applied to improve the control of the activated sludge system. In the present study, two types of fuzzy logic controllers were developed for intermittent aeration control: a low-level fuzzy controller for DO control and a high-level controller for nitrogen removal. A Simulink-SIMBAR model was used to subsequently design and optimise the controller and furthermore to compare various control strategies. The results indicate that the direct fuzzy controller allows for some improvements in comparison with a PI controller. Nevertheless, it is suggested that high-level controllers have more potentials for improving and integrating the control of wastewater treatment. The developed high-level fuzzy controller performs better than two conventional controllers in terms of energy consumption and, at the same time, results in a slightly better effluent quality. Design and tuning were quite straightforward. Since the rule base applied is comprehensive it is expected that in practice the controller will meet with the demands of the operator.

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Sun, Yun Quan, Li Feng Zhao, and Wei Xiang. "A Fuzzy Logic Controller for Vehicle-Active Suspension Systems." Advanced Materials Research 805-806 (September 2013): 1645–49. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1645.

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This paper propose the study of automobile active suspension system for the purpose of improving ride comfort to passengers and simultaneously improving the stability of vehicle by reducing vibration effects on suspension system. A fuzzy-logic-based control for vehicle-active suspension is suggested. The vehicle vibration and disturbance are reduced considerably with a fuzzy logic controller, to enhance comfort in riding faced with uncertain road terrains. A quarter-car active suspension system is controlled to reduce the vertical acceleration, suspension stroke and tire deflection. Simulation studies clearly demonstrate the effectiveness of the fuzzy logic controller for active suspension systems. The performance of the fuzzy logic controller under variations in the suspension component characteristics are also studied and are found to give reasonably good responses.

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R. Nagarajan, M. Gokulkannan, T. Dinesh, S. Murugesan, and M. Naveenprasanth. "PERFORMANCE IMPROVEMENTS OF SEPARATELY EXCITED DC MOTOR USING FUZZY LOGIC CONTROL." International Journal of Engineering Technologies and Management Research 6, no. 2 (March 24, 2020): 47–58. http://dx.doi.org/10.29121/ijetmr.v6.i2.2019.355.

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This paper demonstrates the importance of a fuzzy logic controller over conventional method. The performance of the separately excited DC motor is analyzed by using fuzzy logic controller (FLC) in MATLAB/SIMULINK environment. The FLC speed controller is designed based on the expert knowledge of the fuzzy rules system. The proposed DC motor speed control fuzzy rules are designed for fuzzy logic controller. The output response of the system is obtained by using fuzzy logic controller. The designed fuzzy controller for speed control performance is investigated. Significantly reducing the overshoot and shortening the settling time of the speed response of the motor. They validate different control of approaches, the simulation results show improvement in motor efficiency and speed performance.

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Upendar, Jalla, Sangem Ravi Kumar, Sapavath Sreenu, and Bogimi Sirisha. "Implementation and study of fuzzy based KY boost converter for electric vehicle charging." International Journal of Applied Power Engineering (IJAPE) 11, no. 1 (March 1, 2022): 98. http://dx.doi.org/10.11591/ijape.v11.i1.pp98-108.

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Elecetric vehicle batteries require direct current (DC) current for charging; hence the circuit alternating current (AC) is converted to DC by a battery charger. Battery charger mostly consists of a rectifier and DC-DC converter with a controller built in to serve as a protective circuit. A harmonic source load is a type of electric car charger. During the AC-DC change over method, harmonic current is introduced into the power system, affecting power quality. In this study, a charging station consisting of buck boost and a charging station consisting a KY Boost converter were simulated. To maintain output voltage of DC-DC converters constant controller is used, the controller is either PI or fuzzy logic controller. So, four models are developed and simulated which are buck-boost converter controlled by proportional-integral (PI)-controller, KY-boost converter controlled by proportional integral-controller, buck boost converter controller fuzzy logic controller and KY boost-converter controlled by fuzzy logic controller. The total harmonic distortion (THD) of the four models is compared.

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Bouserhane, Ismail K., Abdeldjebar Hazzab, Abdelkrim Boucheta, Benyounes Mazari, and Rahli Mostefa. "Optimal Fuzzy Self-Tuning of PI Controller Using Genetic Algorithm for Induction Motor Speed Control." International Journal of Automation Technology 2, no. 2 (March 5, 2008): 85–95. http://dx.doi.org/10.20965/ijat.2008.p0085.

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We present induction motor speed control using optimal PI controller fuzzy gain scheduling. To improve PI controller performance, we designed fuzzy PI controller gain tuning for indirect-field oriented IMspeed control using fuzzy rules on-line to adapt PI controller parameters based on error and its first time derivative. To overcome the major disadvantage of fuzzy logic control, i.e., the lack of design technique, we propose optimization of fuzzy logic tuning parameters using a genetic algorithm. Optimally designed fuzzy logic provides suitable PI controller gain to achieve the desired speed while varying load torque and parameters. Simulation demonstrated the performance of the proposed optimal fuzzy-logic tuning PI controller, and numerical validation results of our proposal showed performance comparable to a fuzzy controller having parameters chosen by a human operator.

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Journal articles on the topic 'Controller of fuzzy logic'

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