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Artykuły w czasopismach na temat „ANFIS CONTROLLER”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 31 lipca 2025

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1

Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated control

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Baz, Rachida, Khalid El Majdoub, Fouad Giri, and Ossama Ammari. "Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 745. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp745-755.

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Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was base

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Baz, Rachida, Khalid El Majdoub, Fouad Giri, and Ossama Ammari. "Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 745–55. https://doi.org/10.11591/ijeecs.v34.i2.pp745-755.

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Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was base

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P, Sobha Rani, Padma R, Sarveswara Prasad R, and Rathnakar Kumar P. "Enhancement of power quality in grid connected PV system." Indian Journal of Science and Technology 13, no. 35 (2020): 3630–41. https://doi.org/10.17485/IJST/v13i35.1266.

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Abstract <strong>Background/Objectives:</strong> In grid connected photo voltaic systems inverter is the key element. The inverter is required to shape dc current into sinusoidal current and provide fast response under various disturbances. The quality of power injected into the grid depends on proper inverter control. The objective of this paper is reducing harmonics and to improve power factor in grid connected system with balanced and unbalanced loads.<strong>Methods/Statistical analysis:</strong> In this study, three control mechanisms, adaptive neuro fuzzy inference system (ANFI

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Kheioon, Imad A., Raheem Al-Sabur, and Abdel-Nasser Sharkawy. "Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique." Automation 6, no. 1 (2025): 4. https://doi.org/10.3390/automation6010004.

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Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled usin

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6

Rosyid, Abdur, Mohanad Alata, and Mohamed El Madany. "Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System." International Letters of Chemistry, Physics and Astronomy 55 (July 2015): 1–11. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.55.1.

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This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. Aft

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7

Rosyid, Abdur, Mohanad Alata, and Mohamed El Madany. "Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System." International Letters of Chemistry, Physics and Astronomy 55 (July 3, 2015): 1–11. http://dx.doi.org/10.56431/p-q1glae.

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This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. Aft

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8

Manku Priya. "Adaptive Control of Three-Level UPQC via ANFIS for Enhanced Power Quality: A MATLAB/Simulink-Based Evaluation." Journal of Electrical Systems 20, no. 11s (2024): 1150–65. https://doi.org/10.52783/jes.7393.

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This paper presents a comprehensive study of a three-level Unified Power Quality Conditioner (UPQC) controlled by Adaptive Neuro-Fuzzy Inference System (ANFIS) and its simulation in MATLAB/Simulink. The UPQC is designed to enhance power quality by simultaneously mitigating voltage sags, swells, and harmonic distortions while providing voltage regulation. The proposed ANFIS controller leverages the advantages of both fuzzy logic and neural networks, allowing for adaptive learning and improved performance in dynamic environments. Simulation results demonstrate the effectiveness of the ANFIS cont

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9

Rakhi, S. Ambhore, B. Mandake Yogesh, and S. Bankar Deepak. "Simulation and Analysis of Performance of SRM by Using Different Controller." Indian Journal of Science and Technology 16, no. 25 (2023): 1910–17. https://doi.org/10.17485/IJST/v16i25.1166.

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Abstract <strong>Objectives:</strong> With concerns about energy efficiency, Switched Reluctance Motors (SRM) have piqued the interest of researchers in the fields of Electric Vehicle (EV) due to their robust construction, fault-tolerant operation, high starting torque without the problem of excessive inrush current, and highspeed operation. The goal of this research is Simulation and Analysis of Performance of SRM by Using Different Controller that is fuzzy logic controllers. <strong>Methods:</strong> This study represents a new modified fuzzy-pi controller (MFPI) and Adaptive

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10

Ghimire, Sajesan, Bhriguraj Bhattrai, Sulav Shrestha, and Sagar Poudel. "Comparative Assessment of PID and ANFIS Controllers in an Automatic Voltage Regulator." OODBODHAN 7 (December 31, 2024): 50–57. https://doi.org/10.3126/oodbodhan.v7i1.75766.

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This research paper provides an in-depth analysis of the performance characteristics of PID (Proportional-Integral-Derivative) and ANFIS (Adaptive Neuro-Fuzzy Inference System) controllers within Automatic Voltage Regulator (AVR) systems. The primary objective is to evaluate these controllers' behavior and efficacy, potentially extending their application to other control systems in the power sector. Utilizing the robust capabilities of MATLAB-SIMULINK, the PID controller was finely tuned, while the ANFIS controller was trained using carefully selected data. The findings highlight the ANFIS co

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11

Nguyen, V. H., H. Nguyen, M. T. Cao, and K. H. Le. "Performance Comparison between PSO and GA in Improving Dynamic Voltage Stability in ANFIS Controllers for STATCOM." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 4863–69. http://dx.doi.org/10.48084/etasr.3032.

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One of STATCOM’s advantages is its quick response to disturbances in the power systems. The controller of STATCOM is commonly a PID controller. However, the PID controller is usually only highly effective at one or some operation points. In order to improve operational efficiency of the controller of STATCOM, the proposed ANFIS-PSO and ANFIS-GA controllers have been studied and applied to the studied power system. To demonstrate the performance of the proposed controllers, simulations of the voltage response in time-domain were performed in MATLAB to evaluate the effectiveness of the designed

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12

Nguyen, H. V., H. Nguyen, M. T. Cao, and K. H. Le. "Performance Comparison between PSO and GA in Improving Dynamic Voltage Stability in ANFIS Controllers for STATCOM." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 4863–69. https://doi.org/10.5281/zenodo.3566112.

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One of STATCOM’s advantages is its quick response to disturbances in the power systems. The controller of STATCOM is commonly a PID controller. However, the PID controller is usually only highly effective at one or some operation points. In order to improve operational efficiency of the controller of STATCOM, the proposed ANFIS-PSO and ANFIS-GA controllers have been studied and applied to the studied power system. To demonstrate the performance of the proposed controllers, simulations of the voltage response in time-domain were performed in MATLAB to evaluate the effectiveness of the des

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13

Salman, Saddam Subhi, Abdulrahim Thiab Humod, and Fadhil A. Hasan. "Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3217–27. http://dx.doi.org/10.11591/eei.v11i6.4023.

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This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (

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14

Saddam, Subhi Salman, Thiab Humod Abdulrahim, and A. Hasan Fadhil. "Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3217~3227. https://doi.org/10.11591/eei.v11i6.4023.

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This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimizati

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15

Kharola, Ashwani, and Pravin P. Patil. "Stabilization and Control of Elastic Inverted Pendulum System (EIPS) Using Adaptive Fuzzy Inference Controllers." International Journal of Fuzzy System Applications 6, no. 4 (2017): 21–32. http://dx.doi.org/10.4018/ijfsa.2017100102.

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Elastic Inverted Pendulum system (EIP) are very popular objects of theoretical investigation and experimentation in field of control engineering. The system becomes highly nonlinear and complex due to transverse displacement of elastic pole or pendulum. This paper presents a comparison study for control of EIP using fuzzy and hybrid adaptive neuro fuzzy inference system (ANFIS) controllers. Initially a fuzzy controller was designed, which was used for training and tuning of ANFIS controller using gbell shape membership functions (MFs). The performance of complete system was evaluated through o

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16

Saleh, B. Al-Tuhaifi, and Mousa Al-Aubidy Kasim. "Neuro-fuzzy-based anti-swing control of automatic tower crane." TELKOMNIKA 21, no. 04 (2023): 891–900. https://doi.org/10.12928/telkomnika.v21i4.24044.

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Controlling the position of the final load and the anti-swing control of the loads during the operation of the tower crane are challenging tasks. These are the most important control issues for safe operation, which are difficult to achieve easily with conventional control systems. Hence, the need to integrate the concepts of soft-computing into the tower crane control system. The aim of this research work is to design an adaptive-network-based fuzzy inference system (ANFIS) controller to move the payload to the final position with the lowest possible swing angle. To evaluate the ability of th

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B S, Manohar, and Banakara Basavaraja. "ANFIS based hybrid solar and wave generator for distribution generation to grid connection." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 1 (2019): 479. http://dx.doi.org/10.11591/ijpeds.v10.i1.pp479-485.

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With a long coastal border of about 7500 Kms, India would need an efficient option of hybrid power generation in the coastal region. Abundant availability of wave power and sunlight due to its closeness to equator makes it clear base for power generation from wave generator and the solar power. This paper develops the implementation, which combines both the wave generator and the PV array for a hybrid power delivery controlled using Adaptive Neuro Fuzzy Inference Engine (ANFIS) controller. The super capacitor is used for higher efficiency compared to batteries. It absorbs power and delivers po

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Manohar, B. S., and Basavaraja Banakara. "ANFIS based hybrid solar and wave generator for distribution generation to grid connection." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 1 (2019): 479–85. https://doi.org/10.11591/ijpeds.v10.i1.pp479-485.

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With a long coastal border of about 7500 Kms, India would need an efficient option of hybrid power generation in the coastal region. Abundant availability of wave power and sunlight due to its closeness to equator makes it clear base for power generation from wave generator and the solar power. This paper develops the implementation, which combines both the wave generator and the PV array for a hybrid power delivery controlled using Adaptive Neuro Fuzzy Inference Engine (ANFIS) controller. The super capacitor is used for higher efficiency compared to batteries. It absorbs power and delivers po

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TIDKE, MONIKA S., and S. SANKESWARI SUBHASH. "IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER." JournalNX - a Multidisciplinary Peer Reviewed Journal 3, no. 8 (2017): 20–26. https://doi.org/10.5281/zenodo.1420773.

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This article presents the design and simulation of the ANFIS controller for better performance of the servomotor of a brushless DC motor (BLDC). Productivity BLDC servomotors based on ANFIS, fuzzy and PID controller are tested under different operating conditions, for example, changes in speed setting, parameter variations, load disturbance, etc. BLDC servo motors are used in the aerospace, control and measurement systems, electric vehicles, robotics and industrial control applications. In such cases, they are realized, as conventional P, PI and PID controllers of the control systems BLDC driv

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Intidam, Abdessamad, Hassan El Fadil, Halima Housny, et al. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles." Energies 16, no. 11 (2023): 4395. http://dx.doi.org/10.3390/en16114395.

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This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter

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Shahid, Muhammad Arslan, Ghulam Abbas, Mohammad Rashid Hussain, et al. "Artificial Intelligence-Based Controller for DC-DC Flyback Converter." Applied Sciences 9, no. 23 (2019): 5108. http://dx.doi.org/10.3390/app9235108.

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This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response chara

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Lutfy, O. F., Mohd S. B. Noor, M. H. Marhaban, and K. A. Abbas. "A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223, no. 3 (2008): 309–21. http://dx.doi.org/10.1243/09596518jsce683.

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This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, wi

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Ramakrishna, Kothuri, Karre Mahesh, B. V. Deepthi Yadav, and V. Joshi Manohar. "Solar Fed 15 Level Cascaded Multilevel Inverter with ANFIS Control Strategy for Enhancement of Power Quality." E3S Web of Conferences 472 (2024): 01012. http://dx.doi.org/10.1051/e3sconf/202447201012.

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Solar PV energy conversion systems suffer from degraded power quality issues because of harmonics in the system. In order to eliminate harmonics, this paper focuse on a solar-powered 15-level cascaded inverter. A number of controllers such as PI, Fuzzy Logic Neural Network and ANFIS are used in this study in order to manage the PWM frequency (Proportional Integral, Fuzzy Logic, NN and Adaptive Neuro Fuzzy Inference System, respectively). A PV-fed 15-level cascaded multilevel inverter for power quality improvement will be developed by using an ANFIS controller. The suggested ANFIS controller ha

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Bozorgvar, Masoud, and Seyed Mehdi Zahrai. "Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm." Journal of Vibration and Control 25, no. 2 (2018): 273–85. http://dx.doi.org/10.1177/1077546318774502.

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This research presents designing a control system to reduce seismic responses of structures. Semi-active control of a magnetorheological (MR) damper is used to improve seismic behavior of a 3-story building implementing neural-fuzzy controller made of adaptive neuro-fuzzy inference system (ANFIS) to determine damper input voltage. Both premise and consequent parameters of fuzzy membership and output functions of ANFIS have the ability for training and improvement but most researchers have focused on just consequent parameters. In order to optimize the controller performance, an approach is pro

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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptatio

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Gaya, Muhammad Sani, Norhaliza Abdul Wahab, Yahya M. Sam, and Sharatul Izah Samsuddin. "Comparison of ANFIS and Neural Network Direct Inverse Control Applied to Wastewater Treatment System." Advanced Materials Research 845 (December 2013): 543–48. http://dx.doi.org/10.4028/www.scientific.net/amr.845.543.

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Large disturbances and highly nonlinear nature of the wastewater treatment system makes its control very difficult and challenging. The control of the system using conventional techniques becomes hard and often impossible. This paper presents a comparison of an adaptive neuro-fuzzy inference system (ANFIS) and neural network (NN) inverse control applied to the system. The performances of the controllers were evaluated based on the rise time; percent overshot and the mean error. Simulation results revealed that the ANFIS controller performance was slightly better compared to the neural network

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Kharola, Ashwani, and Pravin P. Patil. "Soft-Computing Control of Ball and Beam System." International Journal of Applied Evolutionary Computation 9, no. 4 (2018): 1–21. http://dx.doi.org/10.4018/ijaec.2018100101.

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This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were coll

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Xavier, S. Arockia Edwin, P. Venkatesh, and M. Saravanan. "A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment." Journal of Electrical Engineering 64, no. 3 (2013): 159–65. http://dx.doi.org/10.2478/jee-2013-0023.

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Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCO

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Areola, R. I., O. A. Aluko, and O. I. Dare-Adeniran. "Modelling of Adaptive Neuro-fuzzy Inference System (ANFIS) - Based Maximum Power Point Tracking (MPPT) Controller for a Solar Photovoltaic System." Journal of Engineering Research and Reports 25, no. 9 (2023): 57–69. http://dx.doi.org/10.9734/jerr/2023/v25i9981.

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Aim: The aim of this research is to model and simulate Adaptive Neuro-Fuzzy Inference System (ANFIS) - based MPPT Controller and also compare its performance with the Perturb and Observe MPPT controller for Photovoltaic systems. Study of the Design: The PV system consists of a PV module, a PWM inverter, an MPPT controller and a DC-DC converter, all of which are connected using Matlab-Simulink environment. Methodology: The ANFIS reference model is constructed based on two input parameters: solar irradiance and temperature. Its output parameter is the reference maximum power output.

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Machrus Ali, Hidayatul Nurohmah, Rukslin, Dwi Ajiatmo, and M Agil Haikal. "Hybrid Design Optimization of Heating Furnace Temperature using ANFIS-PSO." Journal FORTEI-JEERI 1, no. 2 (2020): 35–42. http://dx.doi.org/10.46962/forteijeeri.v1i2.21.

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-- Intelligent control design for industrial heating furnace temperature control is indispensable. PID, Fuzzy, and ANFIS controllers have been proven reliable and have been widely used. However, it is constrained in choosing a better gain controller. Then an approach method is given to determine the most appropriate controller gain value using the artificial intelligence tuning method. The artificial intelligence method used is a combination of the Adaptive Neuro Fuzzy Inference System and Particle Swarm Optimization (ANFIS-PSO) methods. As a comparison, several methods were used, namely; Conv

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Basha, Mr D. Mahaboob. "Optimizing Power Control for Dual Excited Synchronous Generators in Wind Turbine Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29992.

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This study presents a MATLAB Simulink-based approach for optimizing power control in wind turbine systems with dual excited synchronous generators (DESGs), implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. DESGs offer advantages in efficiency and reliability, necessitating effective power control strategies. The proposed methodology integrates Simulink models of wind turbine dynamics, DESG behavior, and an ANFIS controller to optimize power output while ensuring stability and minimizing losses. Various factors such as wind speed variations and grid conditions are incorp

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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 (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

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Girisha, Joshi, and Pius A. J. Pinto. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1177–85. https://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

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Ammar, A. Aldair1 and Weiji Wang2. "FPGA BASED ADAPTIVE NEURO FUZZY INFERENCE CONTROLLER FOR FULL VEHICLE NONLINEAR ACTIVE SUSPENSION SYSTEMS." International Journal of Artificial Intelligence & Applications (IJAIA) 1, no. 4 (2019): 1–15. https://doi.org/10.5281/zenodo.3405792.

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A Field Programmable Gate Array (FPGA) is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS) for controlling a full vehicle nonlinear active suspension system. A Very High speed integrated circuit Hardware Description Language (VHDL) has been used to implement the proposed controller. An optimal Fraction Order PIλ D µ (FOPID) controller is designed for a full vehicle nonlinear active suspension system. Evolutionary Algorithm (EA) has been applied to modify the five parameters of the FOPID controller (i.e. proportional constant Kp, integral constant Ki , derivative

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Ammar, A. Aldair1 and Weiji Wang2. "FPGA BASED ADAPTIVE NEURO FUZZY INFERENCE CONTROLLER FOR FULL VEHICLE NONLINEAR ACTIVE SUSPENSION SYSTEMS." International Journal of Artificial Intelligence & Applications (IJAIA) 1, October (2020): 1–15. https://doi.org/10.5281/zenodo.3799414.

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A Field Programmable Gate Array (FPGA) is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS) for controlling a full vehicle nonlinear active suspension system. A Very High speed integrated circuit Hardware Description Language (VHDL) has been used to implement the proposed controller. An optimal Fraction Order PIλ D µ (FOPID) controller is designed for a full vehicle nonlinear active suspension system. Evolutionary Algorithm (EA) has been applied to modify the five parameters of the FOPID controller (i.e. proportional constant Kp, integral constant Ki , derivative

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TIDKE, MONIKA S. SUBHASH S. SANKESWARI. "IMPLEMENTATION AND PERFORMANCE ANALYSIS OF BLDC MOTOR DRIVE BY PID, FUZZY AND ANFIS CONTROLLER." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 8 (2018): 20–26. https://doi.org/10.5281/zenodo.1158338.

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This article presents the design and simulation of the ANFIS controller for better performance of the servomotor of a brushless DC motor (BLDC). Productivity BLDC servomotors based on ANFIS, fuzzy and PID controller are tested under different operating conditions, for example, changes in speed setting, parameter variations, load disturbance, etc. BLDC servo motors are used in the aerospace, control and measurement systems, electric vehicles, robotics and industrial control applications.

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Othman, Mohd Hanif, Hazlie Mokhlis, Marizan Mubin, et al. "Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System." Sustainability 14, no. 17 (2022): 10798. http://dx.doi.org/10.3390/su141710798.

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To achieve a more sustainable supply of electricity and reduce dependency on fuels, the application of renewable energy sources-based distribution systems (DS) is stimulating. However, the intermittent nature of renewable sources reduces the overall inertia of the power system, which in turn seriously affects the frequency stability of the power system. A virtual synchronous generator can provide inertial response support to a DS. However, existing active power controllers of VSG are not optimized to react to the variation of frequency changes in the power system. Hence this paper introduces a

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Abdulla, Shwan. "Comparative Assessment of PID, Fuzzy Logic and ANFIS Controllers in an Automatic Voltage Regulator of A Power System." Jordan Journal of Electrical Engineering 8, no. 4 (2022): 379. http://dx.doi.org/10.5455/jjee.204-1664025424.

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A comparative study and performance analysis of three different controllers - namely proportional-integral-derivative (PID), PD-like fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) - utilized to control the output voltage of an automatic voltage regulator (AVR) of a power system are carried out. The obtained results show that the PID controller is capable of rejecting simultaneous disturbance signals effectively with zero steady-state error (SSE). However, it is not robust to unexpected parameter changes of the system. On the other hand, the fuzzy logic controller shows the abili

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Nasim, Farhat, Shahida Khatoon, Ibraheem, et al. "Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine." Sustainability 17, no. 6 (2025): 2454. https://doi.org/10.3390/su17062454.

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Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability and consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid wind speed variations, grid disturbances, and parameter uncertainties. These fluctuations result in power instability, increased overshoot, and prolonged settling times, negatively impacting grid compliance and system efficiency. Conventional proportional-integral (PI) controllers are simple and effective in steady-state conditions, but they lack adaptability in dynamic situations.

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Diab, Ahmed A. Zaki, Saleh Al Dawsari, Ibram Y. Fawzy, Ahmed M. Elsawy, and Ayat G. Abo El-Magd. "Adaptive Neuro-Fuzzy Inference System-Based Static Synchronous Compensator for Managing Abnormal Conditions in Real-Transmission Network in Middle Egypt." Processes 13, no. 3 (2025): 745. https://doi.org/10.3390/pr13030745.

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This paper examines the deployment of a 25 MVA Static Synchronous Compensator (STATCOM) to improve voltage stability in a real 66 kV 525 MVA transmission network in the Middle Egypt Electricity Zone. A MATLAB/Simulink model is developed to assess the performance of the STATCOM in both normal and fault conditions, including single-phase and three-phase faults. The STATCOM regulates the voltage by adjusting it within ±10% of the nominal value and is connected to a shunt with the bus B11. Four control strategies are implemented: a proportional–integral (PI) controller, an adaptive neuro-fuzzy inf

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Parmjit Singh, Prince Jindal and Simerpreet Singh. "An Improved Hybrid Fuzzy-PID Tunning With Particle Sawrm Optimization For Enhancing Induction Motor Performance." International Journal for Modern Trends in Science and Technology 7, no. 07 (2022): 66–71. http://dx.doi.org/10.46501/ijmtst051234.

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The fuzzy logic controllers are estimated as an appropriate controller because it is minimally complex method and did not involve any of the mathematical models. The major concern of this study is to control the fluctuations in speed of the induction motor through improving the conventional mechanism by utilizing the ANFIS paradigm as controller. Therefore a new mechanism is to be projected that will execute ANFIS. Because of the merits like Adaptive learning, Self-Organization, Real Time Operation, Fault Tolerance through Redundant Information Coding etc. The ANFIS algorithm is utilized as a

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Chaitanya Kumar Reddy, Kamatam Muni Naga, and Nallathambi Kanagasabai. "Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 729. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp729-742.

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This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tun

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Kamatam, Muni Naga Chaitanya Kumar Reddy Nallathambi Kanagasabai. "Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 729–42. https://doi.org/10.11591/ijeecs.v35.i2.pp729-742.

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This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tun

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Guo, Yu, Rui Yang, Zhiheng Zhang, and Bing Han. "ANFIS-Based Course Controller Using MMG Maneuvering Model." Journal of Marine Science and Engineering 13, no. 3 (2025): 490. https://doi.org/10.3390/jmse13030490.

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In the domain of course control, traditional methods such as proportional–integral–derivative (PID) control often exhibit limitations when addressing complex nonlinear systems and uncertain disturbances. To mitigate these challenges, the adaptive neuro-fuzzy inference system (ANFIS) has been integrated into course control strategies. The primary objective of this study is to investigate the course control characteristics of vessels governed by the ANFIS controller under both normal and severe sea conditions. A three-degree-of-freedom (3-DOF) maneuvering model set (MMG) was employed and validat

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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and labora

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Han, Jiangyi, Fan Wang, and Chenxi Sun. "Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System." Applied Sciences 13, no. 2 (2023): 1046. http://dx.doi.org/10.3390/app13021046.

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Taking an intelligent trimming device hydraulic manipulator as the research object, aiming at the uncertainty, nonlinearity and complexity of its system, a trajectory tracking control scheme is studied in this paper. In light of the virtual work principle, a coupling dynamic model of the hydraulic system and manipulator system is established. In order to improve the anti-interference and adaptive abilities of the manipulator system, a compound control strategy combining the adaptive neuro-fuzzy inference system (ANFIS) and proportional integral derivative (PID) controller is proposed. The neur

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Selma, Boumediene, Samira Chouraqui, and Hassane Abouaïssa. "Fuzzy swarm trajectory tracking control of unmanned aerial vehicle." Journal of Computational Design and Engineering 7, no. 4 (2020): 435–47. http://dx.doi.org/10.1093/jcde/qwaa036.

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Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment o

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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.

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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intellige

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Raheema, Mithaq N., Dhirgaam A. Kadhim, and Jabbar S. Hussein. "Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 675. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp675-685.

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<div>This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy

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Raheema, Mithaq N., Dhirgaam A. Kadhim, and Jabbar S. Hussein. "Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 675–85. https://doi.org/10.11591/ijeecs.v23.i2.pp675-685.

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This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the interfacing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device. The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference

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