Relevant bibliographies by topics / Summing ANFIS PID controller

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Summing ANFIS PID controller'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Summing ANFIS PID controller"

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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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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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Dissertations / Theses on the topic "Summing ANFIS PID controller"

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Liu, Shouheng, and 劉守恆. "AGV Control Using PSO for ANFIS-PID Controller Parameters Tuning." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11692809627531703945.

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碩士<br>明志科技大學<br>電機工程研究所<br>100<br>This paper aims to study the intelligent control approach on the speed control of an automatic guided vehicle (AGV). An adaptive neural fuzzy Inference System (ANFIS) with PID controller is adopted according to the features of motor and vehicle. Through training ANFIS controller, the rising time and settling time can be decreased to minimum values. Further, particle swarm optimization (PSO) is used to optimize the parameters of fuzzy rules, membership function and normalization gaining of ANFIS controller. It can be shown that the better performance such as the shortest rising time and settling time can be achieved by the proposed PSO-ANFIS with PID control scheme.
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Book chapters on the topic "Summing ANFIS PID controller"

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Chawla, Ishan, Vikram Chopra, and Ashish Singla. "Performance Comparison of PID and ANFIS Controller for Stabilization of x and x-y Inverted Pendulums." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16657-1_17.

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Nguyen, Huu Vinh, Hung Nguyen, and Kim Hung Le. "ANFIS and Fuzzy Tuning of PID Controller for STATCOM to Enhance Power Quality in Multi-machine System Under Large Disturbance." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14907-9_4.

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Singh, Balvender, and Shree Krishan Bishnoi. "An Application of ANFIS-PID Controller for Multi Area Hybrid Power System." In Artificial Intelligence and Communication Technologies, 2022nd ed. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-59.

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In this work adaptive fuzzy- PID (ANFIS-PID) controller is presented for frequency and tie-line regulation of multi-area interconnected power systems (IPS). The proposed controller has the properties of both neural network and fuzzy logic. A novel four area power system comprises two reheat turbines (area1 and area2), one hydropower plant (area 3) with the hydraulic governor and one wind plant (area 4) with a pitch actuator. This hybrid interconnected power system model has been built in MATLAB/ Simulink version 2020(a) and the proposed controller is employed with the same design of ANFIS model in all areas. Further, the performance of power system model is presented with the suggested controller. The step load change is considered in each area. An HVDC lines are considered in each area to improve the performance of the proposed model. Further, a fair comparison is made for the conventional PID and Fuzzy-PID controllers. The designed controller performs best compared to the other compared controller for tie-line power and frequency regulation in all four areas of the IPS. The dynamic response of the proposed model improves with AC/DC line as compared to AC line. The sensitivity analysis with change in plant parameters, step load perturbation (SLP) and random load change is also being applied to check the robustness of the controller.
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Timene, Aristide, Ndjiya Ngasop, and Haman Djalo. "Design of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for tractor-implement tillage depth control." In Adaptive Neuro-Fuzzy Inference System as a Universal Estimator [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004096.

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During ploughing operations, variations in soil conditions cause ploughing depth errors. This chapter presents the designed of a neuro-fuzzy controller to decrease tractors ploughing depth errors. The tractor’s electrohydraulic lifting system consisting of pump, valves and cylinders, position and force sensors, and the neuro-fuzzy controller, is modeled using MATLAB software. The aim of this study is to control the draft force and the position of the lifting mechanism using a controller based on the Adaptive Neuro-fuzzy Inference System (ANFIS). After several simulations, the performance of the proposed controller is analysed and compared with that of a Proportional Integral Derivative (PID) controller and a fuzzy logic controller. The performance index based on the Integral Time Absolute value Error (ITAE) criterion indicates a value of 0.32 in the case of the neuro-fuzzy controller; this is almost half the value of the PID controller, which is 0.76. In addition, the values of the standard deviations on the desired depth for the proposed controller are lower than those obtained by the PID controller and those of the fuzzy controller. The results obtained show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.
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Jeyashanthi, J., and J. Barsana Banu. "Performance Analysis of DTC-IM Drive Using Various Control Algorithms." In Futuristic Projects in Energy and Automation Sectors: A Brief Review of New Technologies Driving Sustainable Development. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815080537123010014.

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Direct Torque Control (DTC) is the dominant strategy used in three-phase induction motor control, thanks to its excellent and vibrant characteristics, consistent operation, fewer mathematical calculations, and rigidity in adjustable velocity drives. However, torque ripple is the main drawback of DTC, and it is challenging to reduce it. While DTC based conventional PID controller is utilized, it gets pretentious by lengthy settling time, maximum peak overshoot, and torque and speed curve oscillations. The current research aims to diminish the torque ripple and augment the DTC-enabled induction motor drive performance. Various control methods, such as Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were used in the chapter to enhance the DTC-enabled induction motor drive performance. These control methods were carefully verified and simulated under MATLAB/Simulink 2017. The effectiveness of the projected work was confirmed through simulation, which achieved promising results, thus establishing the supremacy of the proposed model.
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Swapna, S., and N. Shanmugasundaram. "Characteristics of Bridgeless Boost Converter Fed Bldcm and Improvement of Pf Under Different Loads." In Intelligent Technologies for Scientific Research and Engineering. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815079395123010013.

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This chapter describes the Power Factor Correction (PFC) Bridgeless (BL) Boost converter fed BLDC motor drive as a cost-effective solution for low-power applications. A BL configuration of the boost converter is proposed, which offers the elimination of the diode bridge rectifier, thus reducing the conduction losses associated with it. A PFC BL boost converter is designed to operate in Discontinuous Conduction Mode (DICM) to provide an inherent PFC at mains by using the ANFIS-PID controller. The performance of the proposed drive is evaluated over a wide range of speed control and varying input supply voltages (universal mains at 90–230 V) with improved power quality at AC mains. The obtained power quality indices are within the acceptable limits of international power quality standards such as the IEC 61000-- -2. The performance of the proposed bridgeless boost converter fed BLDC motor drive is simulated in MATLAB/Simulink environment. The measured simulation results of THD are reduced to an optimum value of 2.51 for the 400V DC link. Also, the observed results are compared and used to improve the power factor unity according to the variation of the DC link voltage.
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Conference papers on the topic "Summing ANFIS PID controller"

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Azis, Satria Muhammad, Oyas Wahyunggoro, and Adha Imam Cahyadi. "Speed Control of Coconut Grater Machine using PID based ANFIS Controller." In 2024 16th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2024. https://doi.org/10.1109/icitee62483.2024.10808334.

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Saware, Abhishek, Navdeep Gavkar, Shubham Shinde, and Priyanka Kulkarni. "Performance Improvement of PID Controller for PMSM Using ANFIS Controller." In 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). IEEE, 2022. http://dx.doi.org/10.1109/iciccsp53532.2022.9862372.

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Alasvandi, M., S. Moussavi, E. Morad, and E. Rasouli. "ANFIS based IMC PID Controller for Permanent Magnet DC Motor." In 16th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007840402350242.

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Joelianto, Endra, and Deddy Candra Anura. "Transient response improvement of PID controller using ANFIS-hybrid reference control." In 2011 2nd International Conference on Instrumentation Control and Automation (ICA). IEEE, 2011. http://dx.doi.org/10.1109/ica.2011.6130127.

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Maulana, Yulian Zetta, Sutanto Hadisupadmo, and Edi Leksono. "Performance analysis of PID controller, fuzzy and ANFIS in pasteurization process." In 2016 International Conference on Instrumentation, Control and Automation (ICA). IEEE, 2016. http://dx.doi.org/10.1109/ica.2016.7811496.

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Yadav, Deepti, and Arunima Verma. "Behaviour Analysis of PMSM Drive using ANFIS Based PID Speed Controller." In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2018. http://dx.doi.org/10.1109/upcon.2018.8596845.

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Singh, Hitwik, Patil Rahul D, and Saravana Prakash P. "Performance Analysis of PID, Fuzzy and ANFIS Based Speed Controller for PMSM Drive." In 2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation (SeFeT). IEEE, 2022. http://dx.doi.org/10.1109/sefet55524.2022.9908935.

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omer, Zeinab mahmoud, Osman Ibrahim Al-Agha, Aatif osman altahir bakr, and khalid hamid Bilal. "Performance Analysis of OWMR Directional Wheels Robot Arm based on (ANFIS+PID) controller." In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. http://dx.doi.org/10.1109/iccceee.2018.8515874.

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Dasari, Murali, A. Srinivasula Reddy, and M. Vijaya Kumar. "Modeling of a commercial BLDC motor and control using GA-ANFIS tuned PID controller." In 2017 International Conference on Innovative Research In Electrical Sciences (IICIRES). IEEE, 2017. http://dx.doi.org/10.1109/iicires.2017.8078305.

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Ali, Machrus, Hidayatul Nurohmah, Budiman, Judi Suharsono, Hadi Suyono, and Muhammad Aziz Muslim. "Optimization on PID and ANFIS Controller on Dual Axis Tracking for Photovoltaic Based on Firefly Algorithm." In 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE). IEEE, 2019. http://dx.doi.org/10.1109/iceeie47180.2019.8981428.

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