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1
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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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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Vinh, Nguyen, Huu, Hung Nguyen, and Kim Hung Le. "Application of Anfis-Pid Controller for Statcom to Enhance Power Quality in Power System Connected Wind Energy System." International Journal of Engineering & Technology 7, no. 4.4 (2018): 35. http://dx.doi.org/10.14419/ijet.v7i4.4.19604.
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In this paper, a proposed ANFIS-PID controller for the STATCOM to improve transient stability of the power system including DFIG based wind farm based on their nonlinear modeling is presented. The comparative simulation results in two cases of no controller and the ANFIS-PID controller for the STATCOM when occurs a three-phase short-circuit fault in the studied multi-machine power system are shown. It is shown the effectiveness of the proposed ANFIS-PID controller and applicability to a practical power system for enhancing power quality in transient time under large disturbance.
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Zhang, Zhishuai, and Qun Nan. "Adaptive Network-Based Fuzzy Inference System–Proportional–Integral–Derivative Controller Based on FPGA and Its Application in Radiofrequency Ablation Temperature Control." Applied Sciences 14, no. 11 (2024): 4510. http://dx.doi.org/10.3390/app14114510.
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The radiofrequency ablation temperature system is characterised by its time-varying, non-linear, and hysteretic nature. The application of PID controllers to the control of radiofrequency ablation temperature systems has a number of challenges, including overshoot, dependence on high-precision mathematical models, and difficulty in parameter tuning. Therefore, in order to improve the effectiveness of radiofrequency ablation temperature control, an adaptive network-based fuzzy inference system combined with an incremental PID controller was used to optimise the shortcomings of the PID controller in radiofrequency ablation temperature control. At the same time, the learning rate at the time of updating the consequence parameters was set by segmentation to solve the problem of poor control accuracy when the ANFIS-PID controller is implemented based on FPGA fixed-point decimals. Based on FPGA-in-the-loop simulation experiments and ex vivo experiments, the effectiveness of the ANFIS-PID controller in the temperature control of radiofrequency ablation was verified and compared with the PID controller under the same conditions. The experimental results show that the ANFIS-PID controller has a superior performance in terms of tracking capability and stability compared with the PID controller.
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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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Sharma, Deepesh. "Automatic generation control of multi source interconnected power system using adaptive neuro-fuzzy inference system." International Journal of Engineering, Science and Technology 12, no. 3 (2020): 66–80. http://dx.doi.org/10.4314/ijest.v12i3.7.
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LFC (Load Frequency Control) difficulty is created by load of power system variations. Extreme acceptable frequency distinction is ±0.5 Hz which is extremely intolerable. Here, LFC is observed by PID controller (PID-C), Fuzzy and ANFIS controller (ANFIS-C). To control different errors like frequency and area control error (ACE) in spite of occurrences of load disturbance and uncertainties of system is checked by MATLAB/SIMULINK software. Proposed Controller offers less, and small peak undershoot, speedy response to make final steady state. LFC is mandatory for reliability of large interconnected power system. LFC is used to regulate power output of generator within specified area to maintain system frequency and power interchange. Here, two area multi source LFC system is analyzed. ANFIS is utilized for tie-line power deviation and controlling frequency. Proposed controller is compared with other controller and it is found that proposed controller is better than other controller. Proposed controller is better in terms of Robustness. The output responses of interconnected areas have been compared on basis of peak-undershoot, peak-overshoot and settling time (Ts). Result of FLC is compared to that of with classical controller such as proportional derivative plus integral (PID) controller which suggests that conventional controller is slow.
 Keywords: LFC, Fuzzy, PID, ANFIS, LFC; FLC; ACE; PID-C, AGC.
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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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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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I. Berbek, Mohammed, and Ahmed A. Oglah. "Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 229. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp229-242.
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Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.</span>
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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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Reddy, K. M. N. Chaitanya kumar, and Dr N. Kanagasabai. "Performance Analysis of ANFIS-PID Controller based Speed Regulation and Harmonic Reduction in BLDC Motor Application." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 187–94. http://dx.doi.org/10.37391/ijeer.120127.
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This study focuses on assessing the performance of a Proportional-Integral-Derivative (PID) controller integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the context of speed regulation and harmonic reduction in Brushless DC (BLDC) motor applications. Rising BLDC motor speed elevates Total harmonic distortion (THD) due to non-linearity. THD reduction is vital for efficiency, reliability, and compliance in applications like electric vehicles, HVAC, and industrial automation, ensuring optimal performance and longevity. Through simulation-based design and implementation, the effectiveness of the ANFIS-PID controller is evaluated for achieving precise speed control and reducing harmonic distortions in a virtual environment. Various conventional control topologies are considered, with the ANFIS-PID controller demonstrating superior performance. The synergy of adaptive fuzzy logic and classic control components allows the ANFIS-PID controller to outperform others, particularly in dynamic conditions and varying motor characteristics, offering enhanced speed regulation and harmonic reduction in BLDC motor applications. Detailed simulations in MATLAB/Simulink software thoroughly assess the controller's dynamic response and its ability to accurately regulate BLDC motor speed while concurrently reducing harmonic distortions.
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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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Oudah, Manal Kadhim, Salam Waley Shneen, and Suad Ali Aessa. "Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System." International Journal of Robotics and Control Systems 5, no. 2 (2025): 678–97. https://doi.org/10.31763/ijrcs.v5i2.1684.
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Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.
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Berbek, Mohammed I., and Ahmed A. Oglah. "Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 229–42. https://doi.org/10.11591/ijeecs.v26.i1.pp229-242.
Full textAbstract:
Routers are vital during network congestion. All routers have input and output packet buffers. Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PIDcontroller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.
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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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Aloo, Linus Alwal, Peter Kamita Kihato, Stanley Irungu Kamau, and Roy Sam Orenge. "Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2005–16. http://dx.doi.org/10.11591/beei.v12i4.4906.
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The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.
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Aloo, Linus Alwal, Peter Kamita Kihato, Stanley Irungu Kamau, and Roy Sam Orenge. "Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 2005–16. http://dx.doi.org/10.11591/eei.v12i4.4906.
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The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.
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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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Ali, Machrus, and Miftachul Ulum. "Kontrol Frekuensi Wind-Diesel Menggunakan Hibrid Kontroller PID-BA-ANFIS." JE-Unisla 5, no. 1 (2020): 332. http://dx.doi.org/10.30736/je.v5i1.422.
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The wind diesel system is greatly influenced by the wind speed and which is then combined with the diesel engine. Optimization of wind-diesel systems is needed to get good frequency quality and optimal power. The optimal setting of the gain and time constant on the Load Frequency Control (LFC) causes the frequency stability to be weak. In practice, the wind-diesel system is controlled by a PID controller and Fuzzy Logic Controller. At present the gain value setting of the PID is still in the conventional method, so it is difficult to get the optimal value. In this study the control design is applied by using the Smart Method in finding the optimum Proportional Integral Derivative (PID) based on Bat Algorithm (BA). For comparison, the method is used without a control method, conventional PID method, PID auto tune matlab method, PID-BA method, and PID-BA-ANFIS. Wind-diesel modeling uses the transfer function of wind turbine and diesel diagrams. From the results of research that has been done shows that the smallest undershoot on PID-BA-ANFIS, the smallest overshot on PID-BA-ANFIS, and the fastest settling time is equal to PID-BA-ANFIS. This research can later be continued using other artificial intelligence methods.
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Wang, Xin, Seyed Mehdi Abtahi, Mahmood Chahari, and Tianyu Zhao. "An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System." Mathematics 10, no. 6 (2022): 976. http://dx.doi.org/10.3390/math10060976.
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In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.
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Ali, Machrus, Asnun Parwanti, Iswinarti Iswinarti, and Muhammad Agil Haikal. "Pengendalian Ketinggian Air Menggunakan Metode Kecerdasan Buatan berbasis ANFIS." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 11, no. 1 (2020): 10–15. http://dx.doi.org/10.32492/jintake.v11i1.153.
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Abstrak -Pengukuran aliran fluida sangat dibutuhkan oleh pengontrol dalam proses industri. Kuantitas air harus ditentukan untuk mengontrol volume air yang digunakan di tangki penyimpanan. Model kontrol kinerja aliran air berdasarkan tangki diperlukan dengan menggunakan sistem kontrol Proportional-Integral-Derivative (PID). Sistem ini menggunakan sensor aliran untuk mendeteksi kecepatan suatu aktuator. Aktuator menstabilkan kecepatan air keluaran per menit pada titik tertentu. Menentukan nilai konstanta PID secara manual akan sangat sulit dan tidak optimal. Maka diperlukan suatu metode pengendalian yang otomatis dan akurat. Penelitian ini berfokus pada empat perbandingan metode yang dirancang terkait ketinggian air tanpa kontrol, metode PID konvensional, metode Fuzzy Logic Controller (FLC), metode Fuzzy-PID, dan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Hasil simulasi menemukan bahwa keempat model kontrol memiliki performansi yang berbeda. Model PID-ANFIS memperoleh nilai overshot terkecil pada model PID-ANFIS sebesar 0,5135 pu, undershot terkecil pada PID-ANFIS 0,5291 pu. Output Output Saat Ini diperoleh nilai overshot terkecil pada model PID-ANFIS sebesar 0,0023 pu, undershot terkecil pada model PID-ANFIS sebesar 0,0014 pu. Hasil penelitian ini akan dibandingkan dengan metode kecerdasan buatan lainnya.
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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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Kim, Dong Hwa, and Chang Kee Jung. "New 2-DOF PID Controller Tuning by Adaptive Neural Fuzzy Inference System for Gas Turbine Control System." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (2000): 341–48. http://dx.doi.org/10.20965/jaciii.2000.p0341.
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The purpose of introducing a combined cycle with gas turbines in power plants is to reduce loss of energy. Their main role lies in the utilization of waste heat that may be found in exhaust gases from the gas turbine or at some other points of the process to produce additional electricity. The efficiency of the plant exceeds 50%, while the traditional steam turbine plants is approximately 35% ∼ 40% or so. To date, the PID controller has been used to operate under such systems, but since PID controller gain manually has to be tuned by trial and error procedures, getting optimal PID gains is very difficult manually without control design experience. We studied acquiring transfer function from operating data on the Gun-san gas turbine in Korea and a new 2-DOF PID controller tuning by ANFIS is designed for the optimum control of the Guns-san gas turbine’s variables. Since the shape of a membership function in the ANFIS vary on the characteristics of plant, ANFIS-based control is effective for plants whose variables vary. Its results are compared to the conventional 2-DOF PID controller and represents satisfactory response. We expect this method will be used for another process because it is studied using actual operating data.
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Ali, Mahrus, and Muhlasin Muhlasin. "Kontrol Kecepatan Putaran Permanent Magnet Synchronous Machine (PMSM) Menggunakan PID, FLC Dan ANFIS." Jurnal Elektro 4, no. 1 (2019): 253. http://dx.doi.org/10.30736/je.v4i1.302.
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Permanent Magnet Syschronous Machine (PMSM) has low torque in a number of specific applications, so a good control model is needed. PMSM uses the principle of faraday experiments by turning a magnet in a coil by utilizing another energy source ... When a magnet moves in a coil or vice versa. Turning the engine will change the magnetic force flux in the coil and penetrate perpendicular to the coil so that there is a potential difference between the ends of the coil. That is due to changes in magnetic flux. To get the best control method, a comparison of several speed control models is needed. In this study comparing PMSM speed control without controller, using PID controller, using FLC controller, and using ANFIS controller. From the simulation results show that the best model on ANFIS controller, which is closest to the Speed reff (300 rpm) is ANFIS obtained the round profile with the smallest undershot of 300,015 rpm at t = 0.0055 seconds and steady state at 300.02 rpm at 0.004 seconds, obtained output current profile best on FLC = 3.39 A, while at ANFIS = 3.38 A, the best torque profile (the smallest overshot) is obtained on the ANFIS controller of 0.28 pu, the best voltage profile (most continuous) on the ANFIS controller is 300.03. The results of this study will be continued with the use of other artificial intelligence.
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M., John Prabu, Poongodi P., and Premkumar K. "Rotor Position Control of Brushless DC Motor using Adaptive Neuro Fuzzy Inference System." Middle-East Journal of Scientific Research 24, no. 7 (2016): 2395–403. https://doi.org/10.5281/zenodo.3544413.
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In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) based rotor position controller is developed for Brushless DC (BLDC) motor. The rotor position control of BLDC motor is simulated using MATLAB. The rotor position response of the BLDC motor with proposed ANFIS controller is considered for step and ramp reference input. The effectiveness of the proposed controller performance is compared with Proportional Integral Derivative (PID) controller and Fuzzy PID controller. The proposed controller is able to solve the problem of nonlinearities and uncertainty due to reference input changes of BLDC motor and confirm the fast and accurate rotor position response with a extraordinary stable state performance. Also, experimental hardware results are developed to reveal effectiveness of the proposed control scheme.
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Murali, Dasari, Reddy Srinivasula, and Vijaya Kumar M. "GA-ANFIS PID compensated model reference adaptive control for BLDC motor." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 1 (2019): 265–76. https://doi.org/10.11591/ijpeds.v10.i1.pp265-276.
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Adaptive control is one of the widely used control strategies to design advanced control systems for better performance and accuracy. Model reference adaptive control (MRAC) is a direct adaptive strategy with some adjustable controller parameters and an adjusting mechanism to adjust them. In this work Model Reference Adaptive Control for BLDC motors has been designed with a PID controller tuned by GA-ANFIS. GA-Trained ANFIS framework for tuning the PID controller has been proposed. This is used along with the MRAC to deliver enhanced performance in the control of BLDC motor. The performance of the proposed approach is validated for motor control under conditions of change in speed, change in load, change in inertia and change in phase resistance. The performance is validated against convention PID and self tuning PID controllers. The result demonstrates a superior performance of the proposed approach.
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.., Nirmal Kumar, Manish Prateek, Neeta Singh, and Abhinav Saxena. "An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System." Fusion: Practice and Applications 12, no. 2 (2023): 193–205. http://dx.doi.org/10.54216/fpa.120216.
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The article presents the design and control of the adaptive neuro fuzzy Inference system (ANFIS) for the wind-driven permanent magnet synchronous generator (PMSG) in the grid connected system. The rectifier and inverter are connected with the PMSG output and the grid for maintaining the voltage at the grid under variable wind operations. Such interconnections have many challenges, like high harmonics at the output and an improper voltage profile. The harmonics are measured in terms of total harmonic distortion (THD). Performance parameters like peak overshoot and settling time of DC link voltage and rotor speed have been measured. The control of the rectifier and inverter has been assessed with the ANFIS and PID controllers. A closed strategic mechanism has been developed for the ANFIS and PID controllers for improving the performance parameters and harmonics.. Finally, it is observed that the peak overshoot (%) and settling time (sec) of the DC link voltage with ANFIS are 5.2% and 2.9 sec, which are found to be less in comparison to the PID controller with the values of 6.1% and 3.8 sec and other existing methods. Similarly, the settling time (sec) of rotor speed with ANFIS is 1.1 sec, which is less than the settling time (2.6 sec) of the PID controller. Another advantage of ANFIS is the reduction of THD (%) of 5.1% with respect to THD (%) of PID controllers of 6.2% and other existing methods. The reduced THD shows the improved version of the voltage profile.
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Mohammed, Abdullah Fadhil, Hamzah M. Marhoon, Noorulden Basil, and Alfian Ma'arif. "A New Hybrid Intelligent Fractional Order Proportional Double Derivative + Integral (FOPDD+I) Controller with ANFIS Simulated on Automatic Voltage Regulator System." International Journal of Robotics and Control Systems 4, no. 2 (2024): 463–79. http://dx.doi.org/10.31763/ijrcs.v4i2.1336.
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In the dynamic realm of Automatic Voltage Regulation (AVR), the pursuit of robust transient response, adaptability, and stability drives researchers to explore novel avenues. This study introduces a groundbreaking approach—the Hybrid Intelligent Fractional Order Proportional Derivative2+Integral (FOPDD+I) controller—leveraging the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). The novelty lies in the comparative analysis of three scenarios: the AVR system without a controller, with a traditional PID controller, and with the proposed FOPDD+I-based ANFIS. By fusing ANFIS with a hybrid controller, we forge a unique path toward optimized AVR performance. The hybrid controller, based on FOPID (Fractional Order Proportional Integral Derivative) principles, synergizes individual integral factors with ANFIS, augmenting them with a doubled derivative factor. The ANFIS design employs a hybrid optimization learning scheme to fine-tune the Fuzzy Inference System (FIS) parameters governing the AVR system. To train the fuzzy inference system, we utilize a Proportional-Integral-Derivative (PID) simulation of the entire AVR system, capturing essential data over approximately seven seconds. Our simulations, conducted in MATLAB/Simulink, reveal impressive performance metrics for the FOPDD+I-ANFIS approach: Rise time: 1.1162 seconds, settling time: 0.5531 seconds, Overshoot: 0%, Steady-state error: 0.00272, These results position our novel approach favorably against existing works, underscoring the transformative potential of intelligent creation in AVR control.
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FAKHRUDDIN, HANIF HASYIER, HANDRI TOAR, ERA PURWANTO, et al. "Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 9, no. 4 (2021): 786. http://dx.doi.org/10.26760/elkomika.v9i4.786.
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ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView
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Muthukumari, T., T. A. Raghavendiran, R. Kalaivani, and P. Selvaraj. "Intelligent tuned PID controller for wind energy conversion system with permanent magnet synchronous generator and AC-DC-AC converters." IAES International Journal of Robotics and Automation (IJRA) 8, no. 2 (2019): 133. http://dx.doi.org/10.11591/ijra.v8i2.pp133-145.
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This paper presents the intelligent tuned PID controller-based Single Ended Primary Inductor Converter (SEPIC) for Maximum Power Point Tracking (MPPT) operation of Wind Energy Conversion System (WECS). As the voltage and frequency of the Permanent Magnet Synchronous Generator (PMSG) varies with the wind speed changes, Intelligent controlled SEPIC is utilized to maintain the constant DC link voltage. The intelligent tuned PID controller combines the advantages of both conventional and soft controllers. The 1.5MW variable speed WECS (VSWECS) with AC-DC-AC converter is developed using MATLAB/Simulink software. PMSG delivers a load/utility grid through an uncontrolled diode rectifier, intelligent controlled SEPIC and three phase inverter. The real time implementation of the proposed system is done by the DSP processor MSP430F5529. The performance of the SEPIC is tested in both simulation and experiment at different wind speed conditions. The performance of the proposed Intelligent MPPT control of SEPIC are compared with the conventional PID controller. Intelligent tuning of PID controller such as Fuzzy-PID, and ANFIS-PID is implemented in the proposed system and results are compared. The simulation and experimental results reveals that the proposed ANFIS method provide improved performance than the conventional PID method in terms of power quality.
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Muthukumari, T., T. A. Raghavendiran, R. Kalaivani, and P. Selvaraj. "Intelligent tuned PID controller for wind energy conversion system with permanent magnet synchronous generator and AC-DC-AC converters." IAES International Journal of Robotics and Automation (IJRA) 8, no. 2 (2019): 133–45. https://doi.org/10.11591/ijra.v8i2.pp133-145.
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This paper presents the intelligent tuned PID controller-based Single Ended Primary Inductor Converter (SEPIC) for Maximum Power Point Tracking (MPPT) operation of Wind Energy Conversion System (WECS). As the voltage and frequency of the Permanent Magnet Synchronous Generator (PMSG) varies with the wind speed changes, Intelligent controlled SEPIC is utilized to maintain the constant DC link voltage. The intelligent tuned PID controller combines the advantages of both conventional and soft controllers. The 1.5MW variable speed WECS (VSWECS) with AC-DC-AC converter is developed using MATLAB/Simulink software. PMSG delivers a load/utility grid through an uncontrolled diode rectifier, intelligent controlled SEPIC and three phase inverter. The real time implementation of the proposed system is done by the DSP processor MSP430F5529. The performance of the SEPIC is tested in both simulation and experiment at different wind speed conditions. The performance of the proposed Intelligent MPPT control of SEPIC are compared with the conventional PID controller. Intelligent tuning of PID controller such as Fuzzy-PID, and ANFIS-PID is implemented in the proposed system and results are compared. The simulation and experimental results reveals that the proposed ANFIS method provide improved performance than the conventional PID method in terms of power quality.
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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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Nireekshana, Namburi, R. Ramachandran, and G. V. Narayana. "Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation." E3S Web of Conferences 472 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202447202005.
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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.
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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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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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Haikal, Muhammad Agil, Machrus Ali, Irrine Budi S, and Faridah Hanim Binti Mohd Noh. "Hybrid Method for Optimization of Permanent Magnet Synchronous Machine (PMSM) Rotation using FA-ANFIS." Frontier Energy System and Power Engineering 4, no. 2 (2022): 09. http://dx.doi.org/10.17977/um049v4i2p09-19.
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PMSM uses the principle of Faraday's experiment by rotating a magnet in a coil by utilizing another energy source. When the magnet moves in the coil or vice versa. The rotation of the machine will change the flux of magnetic force on the coil and penetrate perpendicularly to the coil so that a potential difference arises between the ends of the coil. It is caused by a change in magnetic flux. The Firefly Algorithm (FA) method has been proven successful in overcoming system optimization problems. Modifying the FA is expected to improve its performance of the FA. To get the best control method, it is necessary to vary the speed control model. This study compares the PMSM speed control without a controller, PID Control, PID-FA, and PID-FA-ANFIS. The simulation results show that the best model on the PID-FA-ANFIS controller which is closest to the Speed Reff (2980 rpm) is that PID-FA-ANFIS obtains a rotation profile with the smallest undershot, the fastest steady state, the best output current profile, the best torque profile, and the best stress profile. The results of this study will be followed by other uses of artificial intelligence.
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Maghfiroh, Hari, Chico Hermanu, and Vernanda Sitorini Zul Hizmi. "POSITION CONTROL OF VTOL SYSTEM USING ANFIS VIA HARDWARE IN THE LOOP." SINERGI 25, no. 3 (2021): 309. http://dx.doi.org/10.22441/sinergi.2021.3.008.
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Electric motors have been widely applied in various equipment. One application is found in Unmanned Aerial Vehicles (UAVs). An electric motor speed control system that can balance the aircraft's position is one of the mandatory features that must be owned by the aircraft. The position balancer control also supports the Vertical Take-Off Landing (VTOL) system. This study's VTOL position control system uses Hardware-in-the-loop (HIL) method with MATLAB Simulink and Arduino. ANFIS (Adaptive Neuro-Fuzzy Inferences System) is used as a position control algorithm. The controller performance is compared with conventional PID and FLC (Fuzzy Logic Controller). The system is tested as an initial position variation and loading test. The experiment shows that HIL can help fast prototyping by faster changes in the controller algorithms and is easy to program. The result is varied in each experiment. In the ISE (Integral Square of Error) point of view, ANFIS is better than PID by 100 % and has a very small difference from FLC in the initial position test. ANFIS is better by 95.44% and 4.56% compared with PID and FLC in the loading test, respectively.
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Kumar, Varun, Ajay Shekhar Pandey, and Sunil Kumar Sinha. "Stability Improvement of DFIG-Based Wind Farm Integrated Power System Using ANFIS Controlled STATCOM." Energies 13, no. 18 (2020): 4707. http://dx.doi.org/10.3390/en13184707.
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The stability of the control grid is a critical prerequisite for a safe and efficient power system service. A thorough knowledge of the effects of the power system volatility is essential for the effective study and control of power systems. This paper presents the simulation outcome of a multimachine power network implemented by a wind farm (WF) utilizing a static synchronous compensator (STATCOM) for better stability control objectives. A similarly aggregated double-fed induction generator (DFIG) powered by a gearbox analogy with an equally aggregated wind turbine (WT) determines the operating output of the wind farm. A proportional–integral–derivative controller (PID)-based damping controller, PID including Fuzzy Logic Controller (FLC), and an adaptive network-based fuzzy inference system (ANFIS) controller of the proposed SATCOM are intended to add sufficient damping properties to the dominating modes of the examined system during diverse working circumstances. To assess the feasibility of the suggested control schemes, a frequency-domain method concentrated on a linearized mathematical structure layout utilizing a time-domain strategy centered on a nonlinear configuration of the device that is subjected to severe fault on the attached bus was carried out consistently. A STATCOM damping controller is configured using the ANFIS method to apply appropriate damping properties to the device’s decisive modes being evaluated under various test conditions. From the findings of the comparative simulation, it can be inferred that the suggested STATCOM along with the planned ANFIS is seen as comparable to STATCOM with PID and STATCOM with PID plus FLC to increase the stability of the studied device.
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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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Hidayatul Nurohmah, Machrus Ali, Rukslin, Dwi Ajiatmo, and Muhammad Agil Haikal. "Komparasi PID, FLC, dan ANFIS sebagai Kontroller Dual Axis Tracking Photovoltaic berbasis Bat Algorithm." Jurnal JEETech 3, no. 2 (2022): 71–77. http://dx.doi.org/10.48056/jeetech.v3i2.197.
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Photovoltaic merupakan pembangkit energy listrik terbarukan yang sangat cocok untuk negara trapis yang banyak mendapat sinar matahari. Akan tetapi pembangkit ini mempunyai efisiensi yang masih kecil. Untuk mengatasi kekurangan ini, beberapa peneliti telah megoptimasi dengan metode dual axis tracking solar secara konvensional. Diperlukan penelitian dengan mengoptimasi menggunakan kecerdasan buatan, dalam hal ini Adaptive Neuro-Fuzzy Inference System (ANFIS) dan Bat Algorithm (BA). Dengan membandingkan performasi model tanpa kontrol, model PID konvensional, PID Auto tuning matlab, metode Fuzzy Logic Controller (FLC), metode ANFIS, dan metode ANFIS-BA. Hasil simulasi menunjukkan bahwa desain model terbaik pada harisontal axis dan vertical axis dual tracking photovoltaic adalah ANFIS-BA dengan overshot terkecil, undershot terkecil, dan settling time tercepat dari semua desain model.
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Balamurugan, K., and R. Mahalakshmi. "ANFIS — Fractional order PID with inspired oppositional optimization based speed controller for brushless DC motor." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (2019): 1941004. http://dx.doi.org/10.1142/s0219691319410042.
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Due to the expanded industrialization, the necessity of variable speed machines/drives keeps on expanding. The vast majority of computerized Brushless Direct Current (BLDC) motor frame-works are utilized because of their speedier reaction and high stablity. In this paper, an innovative technique, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fractional-Order PID (FOPID) controllers for controlling a portion of the parameters, for example, speed, and torque of the BLDC motor are exhibited. With a specific end goal being the performance of the proposed controller under outrageous working conditions, for example, varying load and set speed conditions, simulation results are taken for deliberation. An Opposition-based Elephant Herding Optimization (OEHO) optimization algorithm is utilized to improve the tuning parameters of FOPID controller. At that point, the ANFIS is gladly proposed to adequately control the speed and torque of the motor. The simulation result exhibited that the composed FOPID controller understands a decent dynamic behavior of the BLDC, an immaculate speed tracking with less ascent and gives better execution. The performance investigation of the proposed strategy lessened the error signal contrasted with the existing strategies, for example, FOPID-based Elephant Herding Optimization (EHO), Proportional–Integral–Derivative BAT (PID-BAT), and PID-ANFIS.
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Febryanto Simorangkir, Walter Willy Metyu, I. Made Mataram, and I. Ketut Wijaya. "KONTROL FREKUENSI BEBAN MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) PADA SISTEM HYBRID WIND MIKROHIDRO DAN DIESEL." Jurnal SPEKTRUM 10, no. 4 (2023): 329. http://dx.doi.org/10.24843/spektrum.2023.v10.i04.p38.
Full textAbstract:
This study aims to analyze and compare the performance of three frequency load controller methods in a hybrid generator consisting of three types of energy generation, namely microhydro, diesel and wind. The controller methods being compared are the PID (Proportional Integral Derivative) method, ANFIS (Adaptive Neuro-Fuzzy Inference System), and Fuzzy. This analysis was carried out to improve the efficiency and stability of the hybrid generating system by optimizing the frequency load controller. Frequency instability can occur due to load fluctuations, changes in environmental conditions, or system disturbances. Therefore, the use of an effective and adaptive controller method is very important in maintaining the stability and reliability of the hybrid generator system. The analytical method used in this study involved data collection and computer modeling. The required data includes information on load variations, energy production, and environmental conditions. Furthermore, the PID, ANFIS, and Fuzzy methods are applied to the data to build a frequency load controller model. The results of the analysis show that the three controller methods have different performance in maintaining frequency stability in the hybrid generator system. The PID method provides a fast and accurate response to load fluctuations, but is less adaptive to environmental changes. While the ANFIS method is able to adapt well to changes in system conditions, it requires time for model training. Fuzzy methods can provide control that is more adaptive and tolerant to disturbances, but may require more complex tuning. However, in general, the use of intelligent controller methods such as ANFIS and Fuzzy can improve the performance of hybrid generators by producing more adaptive and stable controls. This research has important implications for the development of more efficient and reliable hybrid power systems. By choosing the right controller method, hybrid power plants can provide energy in a more stable and environmentally friendly manner, and reduce dependence on limited fossil resources. Therefore, this research can make a significant contribution to the development of renewable energy and sustainable development in the future.
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Nurohmah, Hidayatul, Machrus Ali, and Dwi Ajiatmo. "Komparasi PID, FLC, dan ANFIS sebagai Kontroller Dual Axis Tracking Photovoltaic berbasis Bat Algorithm." Jurnal JEETech 3, no. 2 (2022): 71–77. http://dx.doi.org/10.32492/jeetech.v3i2.197.
Full textAbstract:
Photovoltaic is a renewable electrical energy generator that is very suitable for tropical countries that get a lot of sunlight. However, this generator has low efficiency. To overcome this deficiency, several researchers have optimized the conventional dual-axis tracking solar method. Research is needed to optimize using artificial intelligence, in this case, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Bat Algorithm (BA). By comparing the performance of the model without control, conventional PID model, PID Auto tuning MatLab, Fuzzy Logic Controller (FLC) method, ANFIS method, and ANFIS-BA method. The simulation results show that the best model design on the horizontal axis and vertical axis dual tracking photovoltaic is ANFIS-BA with the smallest overshot, smallest undershot, and the fastest settling time of all model designs.
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Yousif, I. Al Mashhadany. "Virtual reality trajectory of modified PUMA 560 by hybrid intelligent controller." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2261–69. https://doi.org/10.11591/eei.v9i6.2579.
Full textAbstract:
The high performance is a goal for all designers to get better, faster, or more efficient than others. This paper proposes a design for virtual reality (VR) of modified PUMA 560 by hybrid controller between adaptive neuro fuzzy inference system (ANFIS) controller and fractional order proportional, integral, derivative (FOPID) controller. The main purpose is to obtain the optimal trajectory by get the best value of controller’s parameters that regulate the manipulator movements smoothly to the desired target. The procedure of design start by obtains the optimal values of the traditional PID controller parameters normally. The next step is applied the FOPID controller with high accuracy. It is high performance to control the perplexing physics system than, the classical integer order of PID controller. The final step to get high performance of the control system under considers is achieved by hybrid between FOPID with ANFIS controller which used the pervious output as predictive point. The whole proposed hybrid controller model was simulated and reproduction by MATLAB Version 2019b and Robotic system Toolbox 9. The optimal design of this controller is applied with 3D model of modified PUMA 560 which design by using VR technique under MATLAB/Simulink.
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Al Mashhadany, Yousif I. "Virtual reality trajectory of modified PUMA 560 by hybrid intelligent controller." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2261–69. http://dx.doi.org/10.11591/eei.v9i6.2579.
Full textAbstract:
The high performance is a goal for all designers to get better, faster, or more efficient than others. This paper proposes a design for virtual reality (VR) of modified PUMA 560 by hybrid controller between adaptive neuro fuzzy inference system (ANFIS) controller and fractional order proportional, integral, derivative (FOPID) controller. The main purpose is to obtain the optimal trajectory by get the best value of controller’s parameters that regulate the manipulator movements smoothly to the desired target. The procedure of design start by obtains the optimal values of the traditional PID controller parameters normally. The next step is applied the FOPID controller with high accuracy. It is high performance to control the perplexing physics system than, the classical integer order of PID controller. The final step to get high performance of the control system under considers is achieved by hybrid between FOPID with ANFIS controller which used the pervious output as predictive point. The whole proposed hybrid controller model was simulated and reproduction by MATLAB Version 2019b and Robotic system Toolbox 9. The optimal design of this controller is applied with 3D model of modified PUMA 560 which design by using VR technique under MATLAB/Simulink.
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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.
Full textAbstract:
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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Prof., Prakash M. Pithadiya1 Dr. Vipul A. Shah 2. "PERFORMANCE INDEX BASED COMPARATIVE STUDY AND ANALYSIS OF HIGHLY COMPLEX NONLINEAR DYNAMIC SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 9 (2017): 140–47. https://doi.org/10.5281/zenodo.886752.
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This paper presents the modification of adaptive neuro fuzzy interference system for interaction problem solving of highly complex nonlinear multiple input multiple output quadruple tank system. one of the problem of the mimo system is interaction which create major issues in industrial process. Major problem of industrial nonlinear process to control and stabilizes the dynamic response of the system. in this research paper gives Conventional PID controller and ANFIS based controller and also a modification of the ANFIS controller (CANFIS) for improving the dynamic response and change the coupling effect. The result of all controller for QTS system, CANFIS controller gives the better control and improve the dymamic response of the highly nonlinear control system. Based on performance criteria give better output than other controller and control and stabilizes the highly complex nonlinear system. ANFIS and CANFIS controller, which is used for nonlinear applications. Finally a comparative study of performance index and analysis between different control strategies is carried out.