Artykuły w czasopismach: „ANFIS CONTROLLER”

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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 controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

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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 based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

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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 based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

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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 Background/Objectives: 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.Methods/Statistical analysis: In this study, three control mechanisms, adaptive neuro fuzzy inference system (ANFIS), ANFIS with static synchronous compensator (STATCOM), ANFIS with dynamic voltage restorer (DVR) are employed to improve power quality. The performances of fuzzy and ANFIS controllers are compared in terms of total harmonic distortion and power factor. MATLAB/ Simulink is used to perform the simulation. Findings: ANFIS controller is more effective compared to fuzzy controller. ANFIS controller with DVR gives less THD and improved power factor as compared to fuzzy controller.Novelty/Applications: Fuzzy and neural network controllers do not need any mathematical modelling and give accurate control as compared to classical PI controllers. In this work, ANFIS controller alone and ANFIS controller along with custom power devices STATCOM and DVR are designed for reduction in total harmonic distortion and improved power factor. The results are tabulated, analyzed and compared with fuzzy controller. It has been shown that ANFIS controller gives better performance compared to fuzzy controller. Keywords: Artificial neural networks (ANN); Adaptive neuro fuzzy inference system (ANFIS); fuzzy controller; power factor; power quality; Total Harmonic Distortion (THD)

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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 using the developed ANFIS-PID. Three methods are examined in the developed ANFIS-PID controller: grid partitioning (genfis1), subtractive clustering (genfis2), and fuzzy C-means clustering (genfis3). The results show that the added cam can improve the gripping strength and that the ANFIS-PID model effectively handles the rise time and supported settling time. The developed ANFIS-PID controller demonstrates more efficient performance than Fuzzy-PID and traditional tuned-PID controllers. This proposed controller does not achieve any overshoot, and the rise time is improved by approximately 50–51%, and the steady-state error is improved by 75–95%, compared with Fuzzy-PID and tuned PID controllers. Moreover, the developed ANFIS-PID controller provides more stability for a wide range of set point displacements—0.05 cm, 0.5 cm, and 1.5 cm—during the testing period. The developed ANFIS-PID controller is not affected by disturbance, making it well suited for robotic gripper designs. Grip force control is also investigated using the proposed ANFIS-PID controller and compared with the Fuzzy-PID in three scenarios. The result from this force control proves objects’ higher actual gripping performance by using the proposed ANFIS-PID.

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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. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

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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. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

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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 controller in comparison to traditional Proportional-Integral (PI) and fuzzy controllers. Key performance metrics, including response time, settling time, and steady-state error, are evaluated across various operating conditions. The findings indicate that the ANFIS-controlled UPQC outperforms both the PI and fuzzy controllers in terms of rapid response and robustness, making it a promising solution for advanced power quality management in modern electrical systems. This research contributes to the growing body of knowledge on intelligent control strategies for power quality enhancement, emphasizing the potential of ANFIS in improving the performance of UPQC systems.

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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 Objectives: 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. Methods: This study represents a new modified fuzzy-pi controller (MFPI) and Adaptive Neural Fuzzy Interference System (ANFIS) modelled on a high power Switched Reluctances Motor for applications. The simulation was carried out using MATLAB, the different parameters included speed control of SRM by ANFIS and FUZZY LOGIC and FUZZY PI Controller. The comparisons are carried out in terms of the Variation Without Controller, Variation With Fuzzy Logic Controller, Variation With Fuzzy Pi Controller and Variation With Anfis Controller. Findings: The motor speed was regulated by the standard fuzzy- PI (FPI) and ANFIS controller after designing a non-linear model of SRM. The fuzzy logic controller gives a perfect speed tracking without overshoot and enhances the speed regulation. Also, ANFIS is used in this model to control the speed regulation. From the result it can be concluded that the speed can regulate fast and accuracy by ANFIS. It was observed that the maximum torque obtained at 5s is 49N-m for Variation Without Controller. Current (For Three Phases) was found ith maximum variation at 8s is 17A in case of Variation With Anfis Controller. Initially speed with maximum value is 9100rpm and at 8s 6200rpm with oscillation only at starting in case of Variation With Anfis Controller. Novelty: This study presents a new approach that combines fuzzy logic, fuzzy PI, and ANFIS controllers for achieving optimum reference tracking for SRM drives. The use of these controllers improves speed regulation and offers accurate speed tracking without any overshoot. This approach is not commonly reported in the literature and represents a unique contribution to the field of SRM control. Additionally, the simulation results demonstratethe effectiveness of the proposed approach in achieving better speed control compared to state-of-the-art techniques. Keywords: Switched Reluctance Motor; Hysteresis Current Controller; Fuzzy Logic Controller; ANFIS Controller & Torque Control

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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 controller's exceptional performance, characterized by a notably fast settling time of 1.7277 seconds and 1.8716% overshoot. In comparison, the PID controller exhibited greater overshoot and a longer settling time, demonstrating less efficiency. These results were compared with other published research papers, further validating the superior performance of the ANFIS controller. This detailed evaluation confirms the ANFIS controller's superiority, offering significant guidance for researchers and industry professionals in making informed decisions regarding the optimal choice of controllers for various control systems.

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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 controllers for STATCOM. The simulation results showed that the proposed controllers can be used to improve the system stability as well as the voltage quality more effectively than the conventional PID controller. The ANFIS PSO controller carried out the best response after the occurrence of a three-phase short circuit fault.

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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 designed controllers for STATCOM. The simulation results showed that the proposed controllers can be used to improve the system stability as well as the voltage quality more effectively than the conventional PID controller. The ANFIS PSO controller carried out the best response after the occurrence of a three-phase short circuit fault.

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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 (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.

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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 optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.

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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 output responses of settling time, steady state error and maximum overshoot. The study also highlights effect of varying number of MFs on training error of ANFIS. The results showed better performance of ANFIS controller compared to fuzzy controller.

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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 the proposed controller to meet the control requirements, its performance was compared to three other controllers: a conventional proportional derivative (PD) controller, a fuzzy-tuned PD controller and a fuzzy controller. MATLAB-based computer simulations of the crane and controllers were carried out to verify and compare the performance of the proposed controllers. The obtained results show the effectiveness of the ANFIS-based controller in adjusting the load position while keeping the load fluctuations small at the final position. The load oscillation angle is about ±2.28° with the ANFIS controller while it is about ±10° when using the PD controller. In addition, only one ANFIS controller is used for both load position and swing angle control.

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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 power fast, where it is more important in wave generation as the power and voltage is not stable. The power delivery improvement in this hybrid system while different controllers like the PI and the ANFIS controller is analysed. There is a higher power delivery improvement when ANFIS controller is chosen.

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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 power fast, where it is more important in wave generation as the power and voltage is not stable. The power delivery improvement in this hybrid system while different controllers like the PI and the ANFIS controller is analysed. There is a higher power delivery improvement when ANFIS controller is chosen.

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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 drive servo motors satisfactory transient and steady state responses. However, the main problem that arises with a conventional PID controller is that the parameters adjusted gain obtained from the drive control systems of the BLDC servo motor cannot produce a more transient response and a stable state under various operating conditions such as parameter variations, load disturbance, etc. In this Paper, design and implementation of the ANFIS controller and its performance compared to the PID controller and fuzzy controller to show its ability to monitor the errors and utility of ANFIS controller management applications. https://journalnx.com/journal-article/20150416

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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 variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.

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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 characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

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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, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller.

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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 has a lower harmonic distortion than conventional controllers; hence the quality of the power improves. The PI, FL, ANN, and ANFIS controllers simulated results are analysed in MATLAB.

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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 proposed in this paper where both premise and consequent parameters of fuzzy functions in an ANFIS network are adjusted simultaneously by genetic algorithm (GA). In order to assess the effectiveness of the designed control system, its function is numerically studied on a benchmark 3-story building and is compared to those of a neural network predictive control (NNPC) algorithm, linear quadratic Gaussian (LQG) and clipped optimal control (COC) systems in terms of seismic performance. The results showed desirable performance of the (ANFIS +GA + membership functions + result function) ANFIS–GA–MFR controller in considerably reducing the structure responses under different earthquakes. The proposed controller showed 30 and 39% reductions in peak story drift (J1) and normed story drift (J4) respectively compared to the NNPC controller, 32 and 44% reductions in J1 and J4 respectively compared to the LQG controller, and 27 and 38% reductions in J1 and J4 respectively compared to the COC controller. The proposed controller effectively reduced acceleration and base shear level compared to the uncontrolled state and had a performance relatively similar to those of three other controllers – for instance, it reduced the maximum level acceleration (J2) 10% higher than COC. Also, the results showed that the ANFIS–GA–MFR controller has more efficiency than the basic ANFIS controller, on average about 20%.

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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 adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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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 controller. The proposed ANFIS controller is effective and useful to the process.

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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 collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

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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 STATCOM is developed with Artificial Neural Network Controller (ANNC) and Adaptive Neuro Fuzzy Inference System(ANFIS) controllers. The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network and ANFIS controllers requires less computation time. They have the ability to generalize and can interpolate in between the training data. The ANNC and ANFIS controllers designed were tested on a 75 V, 100 VA STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that ANFIS controller was producing better results than the ANNC.

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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. The temperature and irradiance data employed for the particular site examined in this study have been acquired from an online global database. The P&O MPPT method was used as a benchmark against the proposed ANFIS-based MPPT technique using Matlab-Simuink Enviroment. Results: In the absence of the controller, the PV voltage registered at 50V. However, through the ANFIS-based MPPT, the incoming voltage from the PV was able to roughly double. Conversely, in the absence of the controller, the PV current stabilized at 4.5A, but the ANFIS-based MPPT managed to reduce this incoming current from the PV by approximately half. The generated PV power follows a similar trajectory to the theoretical PV power, reaching a peak of around 420W, which closely aligns with the theoretical peak power of 440W. The power spans the range from nearly 0 to 420W. As a result, the overall efficiency of the ANFIS-based MPPT charge controller is estimated to be around 60%. Conclusion: The evaluation of the ANFIS-based MPPT and Perturb and Observe-based MPPT Controllers reveals that the Perturb and Observe-based controller demonstrated superior efficiency. Based on this investigation, it can be inferred that both MPPT controllers effectively address uncertain weather scenarios and can readily accommodate challenges such as partial shading and other irregularities commonly associated with varying weather conditions.

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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; Conventional PID (PID-Konv), Matlab Auto tuning PID (PID-Auto), PSO tuned PID (PID-PSO), and Hybrid ANFIS-PSO. The ANFIS-PSO controller is the best choice compared to conventional single loop control systems, conventional PID, and matlab 2013a auto tuning methods to control this nonlinear process. The simulation results show that the ANFIS-PSO design is the best method with overshot = 0.0722, undershot 0.0085, and settling time at 18.8789 seconds which can produce a fast response with strong dynamic performance.

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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 incorporated into the simulation environment. The ANFIS controller dynamically adjusts DESG control parameters based on real-time inputs, enhancing system adaptability and performance. Simulation graphs validate the efficiency of proposed approach in improving DESG performance and overall wind turbine system efficiency. This research contributes to advancing renewable energy technologies by leveraging MATLAB Simulink and ANFIS controllers for optimized power control in DESG-based wind turbine systems. Key Words: Wind power generation, Grid, DESG, MSC, GSC, and ANFIS.

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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 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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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 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 MATLABSIMULINK 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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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 constant Kd, integral order λ and derivative order µ). The data obtained from the FOPID controller are used as a reference to design the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to train the ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIS control parameters obtained from the Matlab program are used to write the VHDL codes. Hardware implementation of the FPGA is dependent on the configuration file obtained from the VHDL program. The experimental results have proved the efficiency and robustness of the hardware implementation for the proposed controller. It provides a novel technique to be used to design NF controller for full vehicle nonlinear active suspension systems with hydraulic actuators.

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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 constant Kd, integral order λ and derivative order µ). The data obtained from the FOPID controller are used as a reference to design the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to train the ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIS control parameters obtained from the Matlab program are used to write the VHDL codes. Hardware implementation of the FPGA is dependent on the configuration file obtained from the VHDL program. The experimental results have proved the efficiency and robustness of the hardware implementation for the proposed controller. It provides a novel technique to be used to design NF controller for full vehicle nonlinear active suspension systems with hydraulic actuators.

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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 new controller by incorporating GA-ANFIS in the active power controller to improve the performance of the VSG. The advantage of the proposed ANFIS-based controller is its ability to optimize the membership function in order to provide a better range and accuracy for the VSG responses. Rate of change of frequency (ROCOF) and change in frequency are used as the inputs of the proposed controller to control the values of two swing equation parameters, inertia constant (J) and damping constant (D). Two objective functions are used to optimize the membership function in the ANFIS. Transient simulation is carried out in PSCAD/EMTDC to validate the performance of the controller. For all the scenarios, VSG with GA-ANFIS (VOFIS) managed to maintain the DS frequency within the safe operating limit. A comparison between three other controllers proved that the proposed VSG controller is better than the other controller, with a transient response of 22% faster compared to the other controllers.

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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 ability to resist changes in the system parameters. Nonetheless, it exhibits both an increase of 12.5% in the SSE and fluctuations in disturbance rejection test. On the contrary, the ANFIS controller shows: i) superior performance and ii) robustness to disturbance signals and changes in the system parameters compared to the other two controllers. For these reasons, we believe that utilization of an ANFIS controller will not only promote safety, but also reliability of the AVR in power systems.

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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. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic controllers (FLCs) and artificial neural networks (ANNs), improve adaptability but suffer from high computational demands and training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference system (ANFIS)-PI controller for DFIG-based WTS. The proposed controller integrates fuzzy logic adaptability with neural network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) and grid-side converter (GSC), ANFIS enhances reactive power management, grid compliance, and overall system stability. The system was tested under a step wind speed signal varying from 10 m/s to 12 m/s to evaluate its robustness. The simulation results confirmed that the ANFIS-PI controller significantly improved performance compared with the conventional PI controller. Specifically, it reduced rotor speed overshoot by 3%, torque overshoot by 12.5%, active power overshoot by 2%, and DC link voltage overshoot by 20%. Additionally, the ANFIS-PI controller shortened settling time by 50% for rotor speed, by 25% for torque, by 33% for active power, and by 16.7% for DC link voltage, ensuring faster stabilization, enhanced dynamic response, and greater efficiency. These improvements establish the ANFIS-PI controller as an advanced, computationally efficient, and scalable solution for enhancing the reliability of DFIG-based WTS, facilitating seamless integration of wind energy into modern power grids.

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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 inference system (ANFIS), a fuzzy logic controller (FLC), and an FLC combined with a supercapacitor. FLCs outperform PI controllers in maintaining voltage stability; however, they exhibit limitations regarding their responsiveness to dynamic changes within the network. The findings demonstrate that the STATCOM enhances the voltage and current stability compared to the system without this component. The ANFIS controller demonstrates optimal performance characterized by minimal waveform fluctuations. Under standard conditions, a single STATCOM integrated with an ANFIS elevates the bus voltages to 100.382% (B10) and 101.953% (B11), surpassing the performance of the FLC (100.314% and 101.246%) and the FLC–supercapacitor combination (100.326% and 101.392%). The deployment of two STATCOM units alongside an ANFIS improves the voltage levels to 102.122% (B10) and 102.200% (B11). The findings demonstrate that the AN-FIS-controlled STATCOM enhances system performance under normal operating conditions, voltage source fluctuations, and fault scenarios. The deployment of two STATCOM units, each rated at 25 MVA and controlled by an ANFIS, significantly enhances voltage stability compared to a single unit.

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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 speed control in the proposed work. It is expected that the hybridization of ANFIS and PID controller can be useful in order to achieve the stability. An optimization technique is also utilized in this study. The values of PID controller can be adjusted by an optimization technique that can be a PSO (Partial Swarm optimization) technique which is required to optimize the values of PID controller in order to choose the best values of P, I and D. In this way, the best output results of the proposed work can be attained.

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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-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

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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-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

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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 validated through sea turning tests. The design of the ANFIS controller involved a combination of the backpropagation algorithm with the least square method. Training data for the ANFIS control system were derived from a linear control framework, followed by simulation tests conducted under normal and severe sea conditions to assess control performance. The simulation results indicate that in normal sea conditions, ANFIS has more stable heading control (smaller Aψ), but at the cost of more energy consumption (larger Iδ). Notably, response time is reduced by approximately 36.7% compared to that of the linear controller. Conversely, during severe sea conditions, ANFIS exhibits an increase in response time by about 33.3% relative to the linear controller while maintaining a smaller Iδ. In the whole course control stage, the stability is better than the linear controller, and it has better energy-saving characteristics. Under scenarios involving small and large course alterations, Aψ values for ANFIS are approximately 11.28% and 13.97% higher than those observed with the best-performing linear controller (λψ = 60), respectively. As the propeller speed increases, the Aψ value of the ANFIS controller decreases significantly, to about 62.71%, indicating that the energy efficiency is improved and the course stability is also enhanced. In conclusion, it can be asserted that the implementation of an ANFIS controller yields commendable performance in terms of controlling vessel courses effectively.

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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 laboratory mixing process.

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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 neural adaptive learning algorithm is utilized to train the given input and output data to adjust the membership functions of the fuzzy inference system, then the PID parameters can be adjusted adaptively to accomplish trajectory tracking. Based on MATLAB/Simulink, the simulation model is established. In addition, to prove the effectiveness of the ANFIS-based PID controller (ANFIS-PID), its performance is compared with PID and fuzzy PID (FPID) controllers. The simulation results indicate that the ANFIS-PID controller is superior to the other controllers in control effect and control precision, and provides a more accurate and effective method for the control of agriculture.

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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 of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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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 Intelligence Technique based MPPT controller. Simulation results show that for a wide range of input irradiance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller shows improved performance than the Artificial Intelligence Technique based MPPT controller with at various operating conditions.

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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 inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.</div>

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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 system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show an excellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposed position/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.

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