Статті в журналах: "ANFIS CONTROLLER"

1

Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (November 3, 2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

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2

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

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This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

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3

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

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Анотація:

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

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4

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 (December 1, 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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5

Salman, Saddam Subhi, Abdulrahim Thiab Humod, and Fadhil A. Hasan. "Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3217–27. http://dx.doi.org/10.11591/eei.v11i6.4023.

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This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.

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6

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

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Elastic Inverted Pendulum system (EIP) are very popular objects of theoretical investigation and experimentation in field of control engineering. The system becomes highly nonlinear and complex due to transverse displacement of elastic pole or pendulum. This paper presents a comparison study for control of EIP using fuzzy and hybrid adaptive neuro fuzzy inference system (ANFIS) controllers. Initially a fuzzy controller was designed, which was used for training and tuning of ANFIS controller using gbell shape membership functions (MFs). The performance of complete system was evaluated through output responses of settling time, steady state error and maximum overshoot. The study also highlights effect of varying number of MFs on training error of ANFIS. The results showed better performance of ANFIS controller compared to fuzzy controller.

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7

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

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With a long coastal border of about 7500 Kms, India would need an efficient option of hybrid power generation in the coastal region. Abundant availability of wave power and sunlight due to its closeness to equator makes it clear base for power generation from wave generator and the solar power. This paper develops the implementation, which combines both the wave generator and the PV array for a hybrid power delivery controlled using Adaptive Neuro Fuzzy Inference Engine (ANFIS) controller. The super capacitor is used for higher efficiency compared to batteries. It absorbs power and delivers power fast, where it is more important in wave generation as the power and voltage is not stable. The power delivery improvement in this hybrid system while different controllers like the PI and the ANFIS controller is analysed. There is a higher power delivery improvement when ANFIS controller is chosen.

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8

Intidam, Abdessamad, Hassan El Fadil, Halima Housny, Zakariae El Idrissi, Abdellah Lassioui, Soukaina Nady, and Abdeslam Jabal Laafou. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles." Energies 16, no. 11 (May 29, 2023): 4395. http://dx.doi.org/10.3390/en16114395.

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This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.

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9

Shahid, Muhammad Arslan, Ghulam Abbas, Mohammad Rashid Hussain, Muhammad Usman Asad, Umar Farooq, Jason Gu, Valentina E. Balas, Muhammad Uzair, Ahmed Bilal Awan, and Tanveer Yazdan. "Artificial Intelligence-Based Controller for DC-DC Flyback Converter." Applied Sciences 9, no. 23 (November 26, 2019): 5108. http://dx.doi.org/10.3390/app9235108.

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This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

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10

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 (December 18, 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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Bozorgvar, Masoud, and Seyed Mehdi Zahrai. "Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm." Journal of Vibration and Control 25, no. 2 (May 10, 2018): 273–85. http://dx.doi.org/10.1177/1077546318774502.

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This research presents designing a control system to reduce seismic responses of structures. Semi-active control of a magnetorheological (MR) damper is used to improve seismic behavior of a 3-story building implementing neural-fuzzy controller made of adaptive neuro-fuzzy inference system (ANFIS) to determine damper input voltage. Both premise and consequent parameters of fuzzy membership and output functions of ANFIS have the ability for training and improvement but most researchers have focused on just consequent parameters. In order to optimize the controller performance, an approach is proposed in this paper where both premise and consequent parameters of fuzzy functions in an ANFIS network are adjusted simultaneously by genetic algorithm (GA). In order to assess the effectiveness of the designed control system, its function is numerically studied on a benchmark 3-story building and is compared to those of a neural network predictive control (NNPC) algorithm, linear quadratic Gaussian (LQG) and clipped optimal control (COC) systems in terms of seismic performance. The results showed desirable performance of the (ANFIS +GA + membership functions + result function) ANFIS–GA–MFR controller in considerably reducing the structure responses under different earthquakes. The proposed controller showed 30 and 39% reductions in peak story drift (J1) and normed story drift (J4) respectively compared to the NNPC controller, 32 and 44% reductions in J1 and J4 respectively compared to the LQG controller, and 27 and 38% reductions in J1 and J4 respectively compared to the COC controller. The proposed controller effectively reduced acceleration and base shear level compared to the uncontrolled state and had a performance relatively similar to those of three other controllers – for instance, it reduced the maximum level acceleration (J2) 10% higher than COC. Also, the results showed that the ANFIS–GA–MFR controller has more efficiency than the basic ANFIS controller, on average about 20%.

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12

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

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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13

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

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

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14

Kharola, Ashwani, and Pravin P. Patil. "Soft-Computing Control of Ball and Beam System." International Journal of Applied Evolutionary Computation 9, no. 4 (October 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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15

Xavier, S. Arockia Edwin, P. Venkatesh, and M. Saravanan. "A Perfomance study of Ann and Anfis Controller for Statcom in dSpace Environment." Journal of Electrical Engineering 64, no. 3 (May 1, 2013): 159–65. http://dx.doi.org/10.2478/jee-2013-0023.

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Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt-ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with Artificial Neural Network Controller (ANNC) and Adaptive Neuro Fuzzy Inference System(ANFIS) controllers. The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network and ANFIS controllers requires less computation time. They have the ability to generalize and can interpolate in between the training data. The ANNC and ANFIS controllers designed were tested on a 75 V, 100 VA STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that ANFIS controller was producing better results than the ANNC.

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16

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 (December 23, 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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17

Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

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18

Othman, Mohd Hanif, Hazlie Mokhlis, Marizan Mubin, Nur Fadilah Ab Aziz, Hasmaini Mohamad, Shameem Ahmad, and Nurulafiqah Nadzirah Mansor. "Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System." Sustainability 14, no. 17 (August 30, 2022): 10798. http://dx.doi.org/10.3390/su141710798.

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To achieve a more sustainable supply of electricity and reduce dependency on fuels, the application of renewable energy sources-based distribution systems (DS) is stimulating. However, the intermittent nature of renewable sources reduces the overall inertia of the power system, which in turn seriously affects the frequency stability of the power system. A virtual synchronous generator can provide inertial response support to a DS. However, existing active power controllers of VSG are not optimized to react to the variation of frequency changes in the power system. Hence this paper introduces a new controller by incorporating GA-ANFIS in the active power controller to improve the performance of the VSG. The advantage of the proposed ANFIS-based controller is its ability to optimize the membership function in order to provide a better range and accuracy for the VSG responses. Rate of change of frequency (ROCOF) and change in frequency are used as the inputs of the proposed controller to control the values of two swing equation parameters, inertia constant (J) and damping constant (D). Two objective functions are used to optimize the membership function in the ANFIS. Transient simulation is carried out in PSCAD/EMTDC to validate the performance of the controller. For all the scenarios, VSG with GA-ANFIS (VOFIS) managed to maintain the DS frequency within the safe operating limit. A comparison between three other controllers proved that the proposed VSG controller is better than the other controller, with a transient response of 22% faster compared to the other controllers.

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19

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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20

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 (February 20, 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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21

Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (January 1, 2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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

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22

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 (January 12, 2023): 1046. http://dx.doi.org/10.3390/app13021046.

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Taking an intelligent trimming device hydraulic manipulator as the research object, aiming at the uncertainty, nonlinearity and complexity of its system, a trajectory tracking control scheme is studied in this paper. In light of the virtual work principle, a coupling dynamic model of the hydraulic system and manipulator system is established. In order to improve the anti-interference and adaptive abilities of the manipulator system, a compound control strategy combining the adaptive neuro-fuzzy inference system (ANFIS) and proportional integral derivative (PID) controller is proposed. The neural adaptive learning algorithm is utilized to train the given input and output data to adjust the membership functions of the fuzzy inference system, then the PID parameters can be adjusted adaptively to accomplish trajectory tracking. Based on MATLAB/Simulink, the simulation model is established. In addition, to prove the effectiveness of the ANFIS-based PID controller (ANFIS-PID), its performance is compared with PID and fuzzy PID (FPID) controllers. The simulation results indicate that the ANFIS-PID controller is superior to the other controllers in control effect and control precision, and provides a more accurate and effective method for the control of agriculture.

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Selma, Boumediene, Samira Chouraqui, and Hassane Abouaïssa. "Fuzzy swarm trajectory tracking control of unmanned aerial vehicle." Journal of Computational Design and Engineering 7, no. 4 (April 9, 2020): 435–47. http://dx.doi.org/10.1093/jcde/qwaa036.

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Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (June 25, 2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.

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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intelligence Technique based MPPT controller. Simulation results show that for a wide range of input irradiance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller shows improved performance than the Artificial Intelligence Technique based MPPT controller with at various operating conditions.

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Raheema, Mithaq N., Dhirgaam A. Kadhim, and Jabbar S. Hussein. "Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 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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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 (April 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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Ravi, S., M. Sudha, and P. A. Balakrishnan. "Design of Intelligent Self-Tuning GA ANFIS Temperature Controller for Plastic Extrusion System." Modelling and Simulation in Engineering 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/101437.

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This paper develops a GA ANFIS controller design method for temperature control in plastic extrusion system. Temperature control of plastic extrusion system suffers problems related to longer settling time, couple effects, large time constants, and undesirable overshoot. The system is generally nonlinear and the temperature of the plastic extrusion system may vary over a wide range of disturbances. The system is designed with three controllers. The proposed GA ANFIS controller is the most powerful approach to retrieve the adaptiveness in the case of nonlinear system. In this research the control methods are simulated using simulink. Relatively the methodology and efficiency of the proposed method are compared with those of the traditional methods and the results obtained from GA ANFIS controller give improved performance in terms of time domain specification, set point tracking, and disturbance rejection with optimum stability.

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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 (April 5, 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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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 (September 15, 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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Kharola, Ashwani, and Pravin P. Patil. "Position and Tilt Control of Two-Wheeled Robot (TWR)." International Journal of System Dynamics Applications 6, no. 4 (October 2017): 17–33. http://dx.doi.org/10.4018/ijsda.2017100102.

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This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot (TWR) system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference system(ANFIS) controller. A Matlab Simulink model of the system has been built using Newton's second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.

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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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Prajapati, Achyut, and Pratibha Tiwari. "Design and Simulation of ANFIS based Brushless DC Motor Control." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 13, no. 02 (December 25, 2021): 70–76. http://dx.doi.org/10.18090/samriddhi.v13i02.2.

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BLDC motor control with ANFIS control methods was investigated and analysed, in this work. The results show that the ANFIS approach is simple, efficient and achieves the desired speed and torque values. The approach was developed and used in both industrial and literary applications. The ANFIS controller was compared with Fuzzy and PI controllers. ANFIS has proved its reliability and good performance. Besides its swift execution, speed control has demonstrated its capability to maintain a minimum cost and commitment track of the target speed values. The method is a basic method with light steps and no reference processing or synchronisation is required.

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Raheema, Mithaq Nama, and Ahmad Shaker Abdullah. "Design of Prediction System for Aircraft's Position Based on Inverse Control Technique Using Adaptive Neuro-Fuzzy Interference System (ANFIS)." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (April 1, 2019): 238–48. http://dx.doi.org/10.29196/jubpas.v27i1.2117.

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This paper proposes the Adaptive Neuro-Fuzzy Interference System (ANFIS) method to realize the track correlation of Radar. ANFIS is used for the first time in inverse model in addition to model of aircraft position radar from the recorded data. The simulation results show that the proposed ANFIS controller has been successfully implemented. Root mean square error is applied to measure the performance of ANFIS that revealed the optimal setting needed for better estimation of the aircraft position. Results with RMSE less than 10-4 also show that the controller with ANFIS yields good tracking performance, valuable and easy to implement.

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Kharola, Ashwani. "Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle control of Inverted Pendulum (IP) Systems." International Journal of Fuzzy System Applications 5, no. 1 (January 2016): 27–42. http://dx.doi.org/10.4018/ijfsa.2016010102.

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This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

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Mashhadany, Yousif Al. "Hybrid ANFIS Controller for 6-DOF Manipulator with 3D Model." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (November 30, 2005): 631–38. http://dx.doi.org/10.24297/ijct.v4i2c2.4188.

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This paper proposes a hybrid ANFIS (Adaptive Neuro-Fuzzy Inference System) controller with DMRAC (Direct Model Reference Adaptive Control) and mathematical modeling of the kinematic and dynamic solutions. The controller was hybrid with a classical controller, and was designed for a spherical-wristed 6-DOF elbow manipulator. The manipulatorâ€™s trajectory overshoot and settling time affect movement; their minimization was thus aimed for. The whole manipulator-controller system was modeled and simulated on MATLAB Version 2011a and Robotics Toolbox 9. To increase accuracy, the ANFIS controller was trained to use many paths in rules and memberships selection. A 3D display model for the manipulator was built in MATLAB. The simulation of the design had done by using the MATLAB/SIMULINK through connection the design with 3D model. Satisfactory results show the hybrid controllerâ€™s capacity for precision and speed, both of which are higher than a classical controllerâ€™s alone.

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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 (June 1, 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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P. Vijaya and R. Raghunadha Sastry. "Power Quality Improvement in Grid Connected DFIG-Wind System using ANFIS." International Journal for Modern Trends in Science and Technology 06, no. 09 (November 25, 2020): 161–66. http://dx.doi.org/10.46501/ijmtst060925.

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This paper proposes a concept of ANFIS based DFIG controller to variable speed wind turbine system for power smoothening. Power fluctuations due to the unpredictable nature of the wind are eliminated by introducing battery energy storage system in the dc link between two back-to-back connected voltage source converters. The design of BESS is presented for feeding regulated power to the grid irrespective of the wind speeds. The control algorithm of the grid-side converter is implemented with ANFIS for feeding regulated power to the grid. Rotor-side converter is controlled for achieving MPPT and unity power factor operation at the stator terminals. And also to improve the efficiency of WECS an MPPT controller is proposed in this paper. The ANFIS based DFIG system is to be implement in MATLAB.

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Yu, Lie, Lei Ding, Fangli Yu, Jianbin Zheng, and Yukang Tian. "Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system (ANFIS) controller." International Journal of Intelligent Computing and Cybernetics 14, no. 1 (January 12, 2021): 1–16. http://dx.doi.org/10.1108/ijicc-09-2020-0132.

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PurposeThe purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.Design/methodology/approachTwo types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.FindingsThe models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.Originality/valueThis paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

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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 (September 15, 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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Tahour, Ahmed, Hamza Abid, and Ghani Aissaoui. "Adaptive neuro-fuzzy controller of switched reluctance motor." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 23–34. http://dx.doi.org/10.2298/sjee0701023t.

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This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

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Djelamda, Imene, and Ilhem Bochareb. "Field-oriented control based on adaptive neuro-fuzzy inference system for PMSM dedicated to electric vehicle." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 1892–901. http://dx.doi.org/10.11591/eei.v11i4.3818.

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Permanent magnet synchronous motor (PMSM) speed control is generally done using flux-oriented control, which uses conventional proportional-integral (PI) current regulators, but still remain the problem of calculating the coefficients of these regulators, particularly in the case of control hybridization, the development of artificial intelligence has simplified many calculations while giving more accurate, and improved results, this paper presents and compares the performance of the flux oriented control (FOC) of a PMSM powered by pulse width modulation (PWM) using PI regulator, fuzzy logic control (FLC) and adaptive neuro-fuzzy inference system (ANFIS), in this work we present another approach of a neuro ANFIS using the hybrid combination of fuzzy logic and neural networks. This ANFIS is a very powerful tool and can be applied to various engineering problems. To make up for the deficiency of fuzzy logic controller. To understand the performance, characteristics, and influence of each controller on the system response, we use MATLAB/Simulink to model a PMSM (0.5 kW) powered by a three-phase inverter and controlled by the FOC, FOC-FLC, and FOC-ANFIS.

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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 (March 18, 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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Peng, Zhi Jun, Li Cheng, Wei Ji Wang, Bo Gao, and Abdel Aitouche. "An ANFIS Solution for Real-Time Control of the EGR & VGT in a Diesel Engine." Advanced Materials Research 732-733 (August 2013): 1222–25. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1222.

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Анотація:

An ANFIS (Adaptive neuro-fuzzy inference system) controller which bases on Takagi-Sugenos method and combines the advantages of neural controller and fuzzy multi-variable controller has been studied and developed for the real-time control of EGR and VGT in a diesel engine. In the AVL-BOOST and Matlab/Simulink co-simulation environment, the control performance of ANFIS controller has been compared with those optimal control strategies based on a fuzzy logic controller. Results show the new controller can have more active control to EGR position and the optimal emission levels can be maintained.

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Febian, Enggar Bima Ihza, Renny Rakhmawati, and Suhariningsih Suhariningsih. "Comparison of ANFIS and FLC as Charging Battery Based on Zeta Converter." INTEK: Jurnal Penelitian 9, no. 1 (April 1, 2022): 49. http://dx.doi.org/10.31963/intek.v9i1.3410.

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Анотація:

Solar energy is an unlimited source of energyand its availability will never run out. It can be converted into asupply of electrical energy. And saved into battery throughconverter. There are many people who convert solar energy intoelectrical energy. Unfortunately, when stored in the battery, it isstill necessary to adjust the output voltage of the converter. So, itis need a controller to do it. To solve the problem, we proposeFuzzy Logic Controller (FLC) and Adaptive Neuro FuzzyInference System (ANFIS) control to adjust output voltage whilethe input voltage is fluctuative. We will compare which twocontrollers are better. We used the constant voltage method, andafter we ran the simulation, we got ANFIZ better than FLC.Because we get setting point faster when using ANFIZ. Theresult, it need time 0,04s to get setting point on output voltage

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Tapia-Herrera, Ricardo, Jesús Alberto Meda-Campaña, Samuel Alcántara-Montes, Tonatiuh Hernández-Cortés, and Lizbeth Salgado-Conrado. "Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS." Mathematical Problems in Engineering 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/873430.

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The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.

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Vasavi Uma Maheswari, M., Dr P. V. Ramana Rao, and . "Active & reactive powers control of DFIG placed with wind energy system by using hybrid controller." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 662. http://dx.doi.org/10.14419/ijet.v7i4.5.25053.

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Here paper presents around the Active power along with Reactive power clout of a grid allied doubly fed Induction Generator (DFIG) with wind energy system (WES) employing PI & ANFIS controller. DFIG is formed adapting a d-q revolving allusion cage circuit with stator flux oriented, field oriented clout approach. By using a coterminous converter of Variable speed constant Frequency (VSCF) along with active the reactive power and DC tie voltage are controlled at sub and super synchronous speeds. An ANFIS has been coupled by a conventional PI controller in order to enhance the power controlling capability at steady state and voltage dip conditions

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Hoang, Thi-Thuy, Hoang-Phi-Khoi Nguyen, Tuan Nguyen, Van-Dong-Hai Nguyen, Diep-Thuy-Duong Le, Xuan-Chinh Trinh, Ngoc-Phu Nguyen, and Phan-Phuc-Long Nguyen. "A Method of Fuzzy Algorithm in Controlling Ball and Beam through Simulation and Experiment." Robotica & Management 26, no. 2 (2021): 3–8. http://dx.doi.org/10.24193/rm.2021.2.1.

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In this paper, we present a way to create fuzzy controller from LQR method by using ANFIS toolbox of Matlab. First, after proving the ability of stability of this SIMO system under LQR method in Matlab/Simulink, we create a fuzzy controller through ANFIS toolbox of Matlab. The data, which is used to train, is collected from responses of system under LQR controller. Also, we present a hardware platform of ball and beam system. Under this fuzzy controller, control quality of ball and beam is better than under LQR controller in both simulation and experiment.

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Nagaraju, M., Sukumar G. Durga, and M. Ravindrababu. "Performance Comparison of Slim Drive with ANFIS Controller." i-manager’s Journal on Electrical Engineering 16, no. 1 (2022): 15. http://dx.doi.org/10.26634/jee.16.1.19165.

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Normally speed control of a Single-Sided Linear Induction Motor (SLIM) by an indirect vector control scheme is difficult because the motor's parameters are time-dependent and the performance depends on various factors such as end effect, saturation, location of primary losses, and iron losses. Traditional PI current regulators are commonly used in vector regulators, but there is a tuning problem due to the oscillation of an operating point. This problem can be overcome by substituting an adaptive neuro-fuzzy-based current controller, and this controller improves the operation of a SLIM, such as its motor speed and thrust force. In this adaptive neuro-fuzzy controller, the ID and IQ errors and the error delay are inputs, and its outputs are Vds and Vqs, respectively. It is trained based on available values. A SLIM's dynamic modelling is implemented by dividing current (I) and flux-linkages into two terms. In these two terms, one is dependent on the end effect, and the other is independent of the end effect. The function of a Voltage Source Inverter (VSI)-fed indirect vector-controlled SLIM drive is simulated in MATLAB/Simulink, and its operation under various operating conditions is studied using an adaptive neuro-fuzzy current controller. These results are compared to a traditional P-I controller. The Pulse Width Modulation (PWM) technology that is used for controlling the VSI is called Space Vector Modulation (SVM).

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M. Venkatesh and G. Raja Rao. "Speed Control of DC Motor Using Intelligent Controllers." November 2020 6, no. 11 (November 30, 2020): 157–64. http://dx.doi.org/10.46501/ijmtst061130.

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DC Motors are broadly utilized in mechanical applications, home appliances and robot controllers on account of their high unwavering quality, adaptability and low cost, where speed and position control of motor are required. The activity of a DC motor is performed by conventional controllers and intelligent controllers in MATLAB environment. The speed control of a dc motor utilizing conventional controllers (PID, IMC) and intelligent controllers (FLC, ANFIS) in view of MATLAB simulation program. A numerical model of the process has been created utilizing genuine plant information and afterward conventional controllers and intelligent controllers has been planned. The outcome acquired as rise time, settling time. Out of these controllers FUZZY can give a superior outcome. Another intelligent controller like ANFIS Controller was created based on Sugeno type FIS along with PID can give a superior performance like quicker settling time, and its sensitivity to applied load. A relative investigation of execution assessment of all controllers has been finished.

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Rosyada, Dibaj Al, Misbah Misbah, and Eliyani Eliyani. "Anfis Based Material Flow Rate Control System for Weigh Feeder Conveyor." Computer and Information Science 9, no. 2 (May 2, 2016): 112. http://dx.doi.org/10.5539/cis.v9n2p112.

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Weight control system on the feeder conveyor determines the factor of the quality of products within an industry. The dynamics of the flow rate of material through the feeder conveyor weigh requires a good level of performance controllers. The base of current controllers such as FLC (Fuzzy Logic Controller) requires a certain amount of knowledge and expertise in its design that will make it difficult to achieve good system performance. These difficulties can be overcome by using systems based on ANFIS (Adaptive Neuro-Fuzzy Inference System). By doing the learning offline, using ANFIS can be obtained by fuzzy inference systems to create a controller FLC. Microcontroller have FLC controller program, its integrated with notebook can monitor and control the notebook weigh feeder conveyor system. Designing a system that has been created will give good results with an average error value of 3.86% at the set-point of 1000 grams / minute, and the average error of 5.03% on set-point 2000 grams / minute in ten times testing.

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