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Journal articles on the topic 'Fuzzy speed controller'
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Ab Rahman, Nur Naajihah, Nafrizuan Mat Yahya, and Nurul Umiza Mohd Sabari. "Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control." IAES International Journal of Robotics and Automation (IJRA) 12, no. 1 (2023): 98. http://dx.doi.org/10.11591/ijra.v12i1.pp98-107.
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Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor’s speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit’s electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller’s gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.
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Nur, Naajihah Ab Rahman, Mat Yahya Nafrizuan, and Umiza Mohd Sabari Nurul. "Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control." IAES International Journal of Robotics and Automation (IJRA) 12, no. 1 (2023): 98–107. https://doi.org/10.11591/ijra.v12i1.pp98-107.
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Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor’s speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit’s electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller’s gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.
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Muhammad, Khan Kaloi, Ali Ehsan, Mustafa Ghulam, et al. "Fuzzy-PID based control scheme for PMDC series motor speed control." Indian Journal of Science and Technology 13, no. 28 (2020): 2911–23. https://doi.org/10.17485/IJST/v13i28.653.
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Abstract <strong>Abstract Background/Objectives:</strong> PID controllers are widely used in different industrial appliations especially in achieving stable speed control of DC motors. Hower, due to their limitations, most often other methods like Sliding Mode controller, or fuzzy based controller are used. Since the Fuzzy controlers are more adaptive to non-linearity. This study is to analyse a fuzzy-based speed control scheme for tuning/adusting the performance of PID controller for the purpose of speed control of DC motor under the load condition. <strong>Methods/Statistical Analysis:</strong> Because of low precision and slow response in different control schemes, a performance comparison is shown between three different control approaches for the DC motor speed control. MATLAB’s Simulink platform is used to realize DC Motor and to implement the PID, Fuzzy and Fuzzy-based PID controllers to run the DC motor on the desired speed. Fuzzy controller is based on very few rule & performance is analyzed considering the load.<strong> Findings:</strong> The performance of three controllers is evaluated in terms of transient domain characteristics like Percentage overshoot, settling and rise times and percentage error under load (2000 rpm). The PID controller has the highest overshoot and hence a faster rise time while FLC has significantly reduced the overshoot, therefore causing rise time to increase. For the Fuzzy-PID controller the percentage overshoot has almost vanished. The rise time also decreased as compared to the FLC. The simulated controller’s responses confirm that Fuzzy-based PID controller has better performance comparing to independent PID and FLC controllers. <strong>Novel/Applications:</strong> In this work, the design of an intelligent Fuzzy-PID controller for the speed control of the DC motor with reduced complexity and a faster response using with minimum number of Fuzzy rules producing more optimized performance is presented. The design of a control strategy that has capability to control nonlinear behaviors and to stabilize the performance of linear systems specially to provide optimized performance for speed tracking system which is an important aspect in real time system design. <strong>Keywords:</strong> PMDC motor; PID Controller; fuzzy controller; fuzzy-based PID controller; transient response
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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.
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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.
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Girisha, Joshi, and Pius A. J. Pinto. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1177–85. https://doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.
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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLABSIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.
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Sheet, Amer Farhan. "Optimization of DC motor speed control based on fuzzy logic-PID controller." Analysis and data processing systems, no. 3 (September 30, 2021): 143–53. http://dx.doi.org/10.17212/2782-2001-2021-3-143-153.
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In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.
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Mohammed, Reham H., Ahmed M. Ismaiel, Basem E. Elnaghi, and Mohamed E. Dessouki. "African vulture optimizer algorithm based vector control induction motor drive system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2396. http://dx.doi.org/10.11591/ijece.v13i3.pp2396-2408.
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<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>
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Reham, H. Mohammed, M. Ismaiel Ahmed, E. Elnaghi Basem, and E. Dessouki Mohamed. "African vulture optimizer algorithm based vector control induction motor drive system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2396–408. https://doi.org/10.11591/ijece.v13i3.pp2396-2408.
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This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.
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Raza, Wasim, Dieky Adzikya, Saba Mehmood, et al. "Fuzzy Logic Speed Regulator for D.C. Motor Tuning." JTAM (Jurnal Teori dan Aplikasi Matematika) 8, no. 1 (2024): 36. http://dx.doi.org/10.31764/jtam.v8i1.16919.
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A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model. This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.
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Rakhi, S. Ambhore, B. Mandake Yogesh, and S. Bankar Deepak. "Simulation and Analysis of Performance of SRM by Using Different Controller." Indian Journal of Science and Technology 16, no. 25 (2023): 1910–17. https://doi.org/10.17485/IJST/v16i25.1166.
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Abstract <strong>Objectives:</strong> With concerns about energy efficiency, Switched Reluctance Motors (SRM) have piqued the interest of researchers in the fields of Electric Vehicle (EV) due to their robust construction, fault-tolerant operation, high starting torque without the problem of excessive inrush current, and highspeed operation. The goal of this research is Simulation and Analysis of Performance of SRM by Using Different Controller that is fuzzy logic controllers. <strong>Methods:</strong> This study represents a new modified fuzzy-pi controller (MFPI) and Adaptive Neural Fuzzy Interference System (ANFIS) modelled on a high power Switched Reluctances Motor for applications. The simulation was carried out using MATLAB, the different parameters included speed control of SRM by ANFIS and FUZZY LOGIC and FUZZY PI Controller. The comparisons are carried out in terms of the Variation Without Controller, Variation With Fuzzy Logic Controller, Variation With Fuzzy Pi Controller and Variation With Anfis Controller.<strong> Findings:</strong> The motor speed was regulated by the standard fuzzy- PI (FPI) and ANFIS controller after designing a non-linear model of SRM. The fuzzy logic controller gives a perfect speed tracking without overshoot and enhances the speed regulation. Also, ANFIS is used in this model to control the speed regulation. From the result it can be concluded that the speed can regulate fast and accuracy by ANFIS. It was observed that the maximum torque obtained at 5s is 49N-m for Variation Without Controller. Current (For Three Phases) was found ith maximum variation at 8s is 17A in case of Variation With Anfis Controller. Initially speed with maximum value is 9100rpm and at 8s 6200rpm with oscillation only at starting in case of Variation With Anfis Controller. <strong>Novelty:</strong> This study presents a new approach that combines fuzzy logic, fuzzy PI, and ANFIS controllers for achieving optimum reference tracking for SRM drives. The use of these controllers improves speed regulation and offers accurate speed tracking without any overshoot. This approach is not commonly reported in the literature and represents a unique contribution to the field of SRM control. Additionally, the simulation results demonstratethe effectiveness of the proposed approach in achieving better speed control compared to state-of-the-art techniques. <strong>Keywords:</strong> Switched Reluctance Motor; Hysteresis Current Controller; Fuzzy Logic Controller; ANFIS Controller & Torque Control
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R., Nagarajan, Gokulkannan M., Dinesh T., Murugesan S., and Naveenprasanth M. "PERFORMANCE IMPROVEMENTS OF SEPARATELY EXCITED DC MOTOR USING FUZZY LOGIC CONTROL." International Journal of Engineering Technologies and Management Research 6, no. 2 (2019): 47–58. https://doi.org/10.5281/zenodo.2585498.
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This paper demonstrates the importance of a fuzzy logic controller over conventional method. The performance of the separately excited DC motor is analyzed by using fuzzy logic controller (FLC) in MATLAB/SIMULINK environment. The FLC speed controller is designed based on the expert knowledge of the fuzzy rules system. The proposed DC motor speed control fuzzy rules are designed for fuzzy logic controller. The output response of the system is obtained by using fuzzy logic controller. The designed fuzzy controller for speed control performance is investigated. Significantly reducing the overshoot and shortening the settling time of the speed response of the motor. They validate different control of approaches, the simulation results show improvement in motor efficiency and speed performance.
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Zhang, Cui Ping, Li Ping Sun, and Zhi Ying Yue. "Gasoline Engine Idle Speed Control Based on PID Fuzzy Algorithm." Advanced Materials Research 338 (September 2011): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amr.338.65.
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According to the characteristics of operating procedures of gasoline engine idle speed; a fuzzy control method is developed to control idle speed of gasoline engine. A novel controller is designed. The controller, which combines fuzzy logic algorithm with traditional PID algorithm, improves steady and dynamic performances of idle speed control. The method has the advantage of not requiring a precise mathematical model of the controlled object. By using SIMULINK simulation software of MATLAB, the simulation results obtained with the PID fuzzy controller show that the PID fuzzy controller has better controlled performances and robustness. It provides some reference values for further practical application.
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Chen, Liping, Haoyu Liu, Ze Cao, et al. "Speed Control of Permanent Magnet Synchronous Motor Based on Variable Fractional-Order Fuzzy Sliding Mode Controller." Actuators 14, no. 1 (2025): 38. https://doi.org/10.3390/act14010038.
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A variable fractional-order (VFO) fuzzy sliding mode controller is designed to control the speed of a permanent magnet synchronous motor (PMSM). First, a VFO sliding mode surface is established. Then, a VFO fuzzy sliding mode controller is designed, capable of suppressing the effects of parameter uncertainties and disturbances to achieve precise PMSM speed control. The global stability and finite time convergence of the controlled system state are demonstrated using Lyapunov stability theory. The numerical and experimental results validate the effectiveness of the controller, showing better immunity to disturbances and a smaller overshoot compared to PID and fixed-order fuzzy sliding mode controllers.
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Abdalla, Turki, Haroution Hairik, and Adel Dakhil. "Direct Torque Control System for a Three Phase Induction Motor With Fuzzy Logic Based Speed Controller." Iraqi Journal for Electrical and Electronic Engineering 6, no. 2 (2010): 131–38. http://dx.doi.org/10.37917/ijeee.6.2.8.
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This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC) drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI) controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI) to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI) controller.
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Tanawat, Chalardsakul, Piliyasilpa Chotnarin, and Sukontanakarn Viroch. "TMS320F28379D microcontroller for speed control of permanent magnet direct current motor." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2816–28. https://doi.org/10.11591/ijai.v13.i3.pp2816-2828.
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This paper aims to study the behavior of the proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.
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Borunda, Monica, Raul Garduno, Javier de la Cruz Soto, and Rafael Alfonso Figueroa Díaz. "Intelligent Control of an Experimental Small-Scale Wind Turbine." Energies 17, no. 22 (2024): 5656. http://dx.doi.org/10.3390/en17225656.
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Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.
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Hore, Debirupa. "Fuzzy Logic Based Advance Speed Control of Induction Motor." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 124–28. http://dx.doi.org/10.24297/ijct.v4i1c.3113.
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The speed control of Induction motor is done using the advance AI Technique methods. In this System the vector control scheme in the stator flux oriented reference frame is used for controlling the variable speed Induction motor. For this the conventional Speed PI controller and Current PI Controllers are tuned and the responses are observed. The Conventional Speed PI Controller is then replaced by the Fuzzy Logic Speed Controller to observe the various responses of the system. The fuzzy Logic Speed Controller is designed and tuned in such a way to obtain better and fast sped responses of the system. Simulation results reveal that the fuzzy-controller improves the performance of variable speed Induction Motor in terms of speed and Power factor
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Bagavathy, S., P. Maruthupandi, S. Sheebarani, Radhika ., and Ramya Rani N. "Fuzzy with PID based Sensorless Brushless DC Motor Control." International Journal of Engineering & Technology 7, no. 4.19 (2018): 481–84. http://dx.doi.org/10.14419/ijet.v7i4.19.23205.
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In this Work, the controller with fuzzy and PID used to execute the speed calculation of a brushless dc motor. The proposed strategy is basic and solid. Amid regenerative braking mode, it saves energy in a rechargeable battery. The proposed controller is coordinated utilizing the fuzzy logic toolbox in MATLAB. The work proposes the idea of fuzzy with PID controller for controlling the speed of BLDC motor. The goals of the undertaking are to analyze the execution of PI and the proposed fuzzy with PID controllers utilizing MATLAB software for the controlling the speed of BLDC motor. The executions of the two controllers are compared based on different control framework time domain parameters. Simulation consequences of the two controllers have been exhibited and it is discovered that the control idea with fuzzy with  PID controller outflanks customary controller in a large portion of the angles. Â
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Brahim, Dahhou, Bendjebbar Mokhtar, and Lachtar Salah. "Improvement of adaptive fuzzy control to adjust speed for a doubly fed induction motor drive (DFIM)." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 1 (2020): 496. http://dx.doi.org/10.11591/ijpeds.v11.i1.pp496-504.
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This paper presents the doubly fed induction motor (DFIM) speed control using adaptive fuzzy logic PI (AFLPI) controller to give better dynamic performances. Before the advent of modern technology, integral proportional based current controller is usually used due to its simplicity. But, the performance of closed-loop control is largely influenced by this type of speed and torque controllers used, as long as the PI controllers suffer from tuning problem. To overcome the problem, a new technique AFLPI based speed controller for direct field oriented control fed DFIM to get fast speed response and to minimize the torque ripple. The application of this type of control is very satisfactory to replace the conventional PI controller and, even the fuzzy logic PI (FLPI) controller. The performance of the field oriented controlled DFIM drive has simulated at different operating conditions using the AFLPI controller and the obtained results are compared with FLPI controller and conventional PI controller. Accordingly, an improvement in dynamic and robustness is clearly appears in AFLPI controller simulation results compared to the others aforementioned controllers. Simulation Results are presented for the three techniques using Matlab/Simulink to prove the dynamic performances and robustness.
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Chalardsakul, Tanawat, Chotnarin Piliyasilpa, and Viroch Sukontanakarn. "TMS320F28379D microcontroller for speed control of permanent magnet direct current motor." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2816. http://dx.doi.org/10.11591/ijai.v13.i3.pp2816-2828.
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<p><span>This paper aims to study the behavior of the <a name="_Hlk169245133"></a>proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.</span></p>
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Suman Kar. "Simulation of self-tuned fuzzy PID controlled DC motor in simulink and performance analysis from speed time characteristics." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 026–33. https://doi.org/10.30574/wjarr.2025.27.1.1951.
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This paper presents the simulation and performance analysis of a self-tuned fuzzy PID-controlled DC motor using MATLAB/Simulink. The proposed controller integrates fuzzy logic with a conventional PID controller to dynamically adjust its parameters, improving the system’s adaptability to varying operating conditions. The simulation model is developed in Simulink, and the speed-time characteristics of the DC motor are analyzed to evaluate the controller’s performance. Key performance metrics such as rise time, settling time, overshoot, and steady-state error are compared with conventional PID and fuzzy logic controllers. The results demonstrate that the self-tuned fuzzy PID controller enhances system stability, reduces overshoot, and improves response time, making it an effective control strategy for DC motor speed regulation.
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Salahuddin, Humayun, Kashif Imdad, Muhammad Umar Chaudhry, Dmitry Nazarenko, Vadim Bolshev, and Muhammad Yasir. "Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller." Applied Sciences 12, no. 9 (2022): 4647. http://dx.doi.org/10.3390/app12094647.
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The substantial rise in the demand for electric vehicles (EVs) has emphasized an environment-friendly and intelligent design for speed control strategies. In this paper, a Mamdani fuzzy logic controller (MFLC) was proposed to vigorously control the speed of EVs at discrete levels. MFLC member functions (MFs) are tuned for EVs operating at three different speed modes (40, 60, and 80 km/h). The proposed speed controller operation for the speed tracking of EVs was designed and tested in MATLAB (Simulink) environment. The proposed speed controller validated a remarkable improvement in dynamic speed control compared with existing P-I, FLC, Fuzzy FOPID (ACO), Fuzzy FOPID (GA), and Fuzzy FOPID (PSO) controllers. Its stability under a user-defined drive pattern is also observed. In this proposed work, the speed controller highlights the better tracking of user-defined speed response compared to the conventional aforementioned controllers. Moreover, the speed tracking of the designed model was tested for robustness against speed transients at predefined time instants, respectively. The comparison suggests that the MFLC model removes overshoot and significantly reduces the steady-state time.
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G.R.P.Lakshmi, G. R. P. Lakshmi, G. R. Puttalakshmi G.R.Puttalakshmi, and S. Paramasivam S.Paramasivam. "Speed Control of Brushless Dc Motor Using Fuzzy Controller." Indian Journal of Applied Research 3, no. 11 (2011): 215–19. http://dx.doi.org/10.15373/2249555x/nov2013/69.
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Pandian, A., and R. Dhanasekaran. "Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor." Applied Mechanics and Materials 573 (June 2014): 155–60. http://dx.doi.org/10.4028/www.scientific.net/amm.573.155.
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This paper presents improved Fuzzy Logic Controller (FLC) of the Direct Torque Control (DTC) of Three-Phase Induction Motor (IM) for high performance and torque control industrial drive applications. The performance of the IM using PI Controllers and general fuzzy controllers are meager level under load disturbances and transient conditions. The FLC is extended to have a less computational burden which makes it suitable for real time implementation particularly at constant speed and torque disturbance operating conditions. Hybrid control has advantage of integrating a superiority of two or more control techniques for better control performances. A fuzzy controller offers better speed responses for startup and large speed errors. If the nature of the load torque is varied, the steady state speed error of DTC based IM drive with fuzzy logic controller becomes significant. To improve the performance of the system, a new control method, Hybrid fuzzy PI control is proposed. The effectiveness of proposed method is verified by simulation based on MATLAB. The proposed Hybrid fuzzy controller has adaptive control over load toque variation and can maintain constant speed.
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Hayder, Yousif Abed, Thiab Humod Abdulrahim, and J. Humaidi Amjad. "Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 265–74. https://doi.org/10.11591/ijece.v10i1.pp265-274.
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This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
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Orlowska-Kowalska, T., and M. Dybkowski. "Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction motor drive with MRAS-type speed estimator." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 1 (2012): 61–70. http://dx.doi.org/10.2478/v10175-012-0010-0.
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Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction motor drive with MRAS-type speed estimator This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive in a wide speed range. The adaptive speed controller uses on-line trained fuzzy neural network, which enables very fast tracking of the changing speed reference signal. This adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller in the direct rotor-field oriented control (DRFOC) of the induction motor (IM) drive structure. Connective weights of the controller are trained on-line according to the error between the actual speed of the drive and the reference model output signal. The rotor flux and speed of the vector controlled induction motor are estimated using the model reference adaptive system (MRAS) - type estimator. Presented simulation results are verified by experimental tests performed on the laboratory-rig with DSP controller.
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M. Dawood, Suroor, Samar H. Majeed, and Habeeb J. Nekad. "COMPARATIVE ANALYSIS OF SPEED CONTROL OF MULTIPLE )MASTER/SLAVES) PMSMS USING PI CONTROLLER AND FUZZY LOGIC CONTROLLER." Kufa Journal of Engineering 8, no. 1 (2017): 119–37. http://dx.doi.org/10.30572/2018/kje/811184.
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This paper presents the use of two different controllers in speed control of multi permanent magnet synchronous motors (PMSMs) master/slaves system. These controllers used to improve the performance of multi PMSMs drive system are conventional PI and PD-like fuzzy + I controllers. For PD-like fuzzy + I speed controller, simulation results clearly show that the response of the system is superior as compared to PI speed controller in terms of rise time, settling time, accuracy, and steady state error. The design and analysis of the system with both controllers have been simulated and studied using MATLAB/Simulink environment. Speed responses obtained under PI and PD-like fuzzy + I controllers are compared for a variety of load conditions.
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Suwarno, Iswanto, Yaya Finayani, Robbi Rahim, Jassim Alhamid, and Ahmed Ramadhan Al-Obaidi. "Controllability and Observability Analysis of DC Motor System and a Design of FLC-Based Speed Control Algorithm." Journal of Robotics and Control (JRC) 3, no. 2 (2022): 227–35. http://dx.doi.org/10.18196/jrc.v3i2.10741.
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DC motor is an electrical motor widely used for industrial applications, mostly to support production processes. It is known for its flexibility and operational-friendly characteristics. However, the speed of the DC motor needs to be controlled to have desired speed performance or transient response, especially when it is loaded. This paper aims to design a DC motor model and its speed controller. First, the state space representation of a DC motor was modeled. Then, the controllability and observability were analyzed. The transfer function was made based on the model after the model was ensured to be fully controllable and observable. Therefore, a fuzzy logic controller is employed as its speed controller. Fuzzy logic controller provides the best system performance among other algorithms; the overshoot was successfully eliminated, rise time was improved, and the steady-state error was minimized. The proposed control algorithm showed that the speed controller of the DC motor, which was designed based on the fuzzy logic controller, could quickly control the speed of the DC motor. The detail of resulted system performance was 2.427 seconds of rising time, 11 seconds of settling time, and only required 12 seconds to reach the steady state. These results were proved faster and better than the system performance of PI and PID controllers.
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Sathishkumar, H. "Performance Analysis of Speed Controller for 3hp and 150hp Three Phase Induction Motors Being Used in Cable Industry Applications." Asian Journal of Electrical Sciences 8, no. 1 (2019): 7–14. http://dx.doi.org/10.51983/ajes-2019.8.1.2339.
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In this paper, Performance analysis of speed controller for 3hp and 150hp three phase induction motors being used in cable industry applications is carried out. For this purpose, cable manufacturing industry (Ravicab Cables Private Limited) at Bidadi, Ramanagara district is taken into study. A 3hp 3Φ induction motor is used to pull the single core cable which comes out of diameter controller. Similarly, a 150hp 3Φ induction motor is used to pull the four single core cable to form a four core cable. Therefore, as far as this cable industry is concerned, these motors speed control is essential. If the speed of these motors is not controlled precisely, then these motors will run at a speed which is deviated from reference speed. Hence, single and four core cable manufacturing is discussed here. Moreover, performance of speed controller (PID) which is currently existing in this industry for 3hp and 150 hp 3Φ induction motor is analysed and various controllers are proposed (Fuzzy, Neural network, Neuro-fuzzy). Eventually, robust controller for 3hp and 150hp motors is identified using comparative performance analysis between various controllers.
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Elhawat, Masoud, and Hüseyin Altınkaya. "Frequency Regulation of Stand-Alone Synchronous Generator via Induction Motor Speed Control Using a PSO-Fuzzy PID Controller." Applied Sciences 15, no. 7 (2025): 3634. https://doi.org/10.3390/app15073634.
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This paper introduces a novel approach to frequency regulation in stand-alone synchronous generators by combining particle swarm optimization (PSO) with a Fuzzy PID controller. This study compares three control methods: a programmable logic controller (PLC)-based PID, a Fuzzy PID, and a PSO-Fuzzy PID controller. An experimental setup is implemented using real physical equipment, including an asynchronous motor, a synchronous generator, and various power and control components. The system is monitored and controlled in real-time via an S7-1215 PLC with the TIA Portal V17 interface, and the controllers are designed using MATLAB/Simulink. PLC-MATLAB communication is implemented using the KEPServerEX interface and the OPC UA protocol. The PSO-Fuzzy PID controller demonstrates superior performance, reducing overshoot, undershoot, and settling time compared to the other methods. These results highlight the effectiveness and real-time applicability of the PSO-Fuzzy PID controller for industrial frequency control, especially under varying load conditions and the nonlinear characteristics of the synchronous generator.
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Narmadha, T. Velayudham, Chackaravarthy Baskaran, and K. Sivakumar. "Comparison of Performance Measures of Speed Control for a DC Motor Using Hybrid Intelligent Controller and Optimal LQR." Applied Mechanics and Materials 622 (August 2014): 23–31. http://dx.doi.org/10.4028/www.scientific.net/amm.622.23.
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-In this paper , performance of fuzzy PD , fuzzy PI , fuzzy PD+I , fuzzy PID controllers are evaluated and compared. This paper also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DC motor, which merely focuses on performance index of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is modelled using MATLAB software, the simulation results shows that the fuzzy PID controllers are the best performing candidates in all aspects but it as higher overshoot and IAE in comparison with optimal LQR. The Fuzzy PI controller exhibited null offset but suffers from poor stability and peak overshoot, whereas the fuzzy PD controller has fast rise time, with no overshoots but the IAE is much greater. Thus, the comparative analysis recommends fuzzy PID controller but it is usually associated with complicated rule base and tedious tuning. To circumvent these problems, the proposed LQR controller gives better performance than the other controllers.
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Abed, Hayder Yousif, Abdulrahim Thiab Humod, and Amjad J. Humaidi. "Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 265. http://dx.doi.org/10.11591/ijece.v10i1.pp265-274.
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<span lang="EN-US">This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.</span>
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Othmani, Hichem, D. Mezghani, and A. Mami. "Fuzzy Gain-Scheduling Proportional–Integral control for Improving the speed behavior of a three-phases induction motor." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 4 (2016): 1161. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1161-1171.
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In this article, we have set up a vector control law of induction machine where we tried different type of speed controllers. Our control strategy is of type Field Orientated Control (FOC). In this structure we designed a Fuzzy Gain-Scheduling Proportional–Integral (Pi) controller to obtain best result regarding the speed of induction machine. At the beginning we designed a Pi controller with fixed parameters. We came up to these parameters by identifying the transfer function of this controller to that of Broïda (second order transfer function). Then we designed a fuzzy logic (FL) controller. Based on simulation results, we highlight the performances of each controller. To improve the speed behaviour of the induction machine, we have designend a controller called “Fuzzy Gain-Scheduling Proportional–Integral controller” (FGS-PI controller) which inherited the pros of the aforementioned controllers. The simulation result of this controller will strengthen its performances.
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Fan, Li Ping, and Yi Liu. "Fuzzy Tuning PID Control of the Rolling Mill Main Drive System." Applied Mechanics and Materials 713-715 (January 2015): 739–42. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.739.
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The main drive system using for four-high mill is usually a double closed loop DC motor speed regulating system. Generally, the speed controller and current controller are PID controllers, and the parameters of controllers are determined by the engineering design method. Once disturbance occur, the control effects are often just passable. In this paper, fuzzy logic is used to set the parameters of the speed PID controller, so as to improve the disturbance rejection ability. Simulation results show that the fuzzy tuning PID controller can give better control effect than the regular control.
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Jaydeepsinh, C. Baria, Patel Kuldip, C. Dalwadi Darshankumar, and Dalwadi C. "High-Performance and Robust Field-Oriented Control of IPMSM Drive Over Wide Speed Operation by PI and Fuzzy Intelligent Controllers." Indian Journal of Science and Technology 16, no. 26 (2023): 1947–57. https://doi.org/10.17485/IJST/v16i26.776.
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Abstract <strong>Objectives:</strong> This paper simulates a high-performance PI and FLC-based controller for an IPMSM drive in which the speed should closely follow the reference speed and voltage-current trajectory with different conditions like load disturbance, variation in circuit parameters, and control strategy. In FOC, the PI and fuzzy controller are used for sliding mode control. This controller generates the d-axis current (Id) and q-axis current (Iq), which control the torque and magnetic field vector for each stator winding, so the speed of the motor can be effectively controlled. <strong>Methods:</strong> In this paper, we used two loops: one is the outer speed loop, and another is the inner loop called the current loop, which generates the pulses for the inverter. The optimal behavior of the controllers is designed based on the Maximum Torque per Ampere (MTPA) method for stand-still to base speed operation and the Field Weakening (FW) method for the above base speed operation. The mathematical model of the IPMSM motor can be derived from its dynamic d-q model. This proposed drive system is simulated in MATLAB-Simulink software with different conditions like speeds and loads. Moreover, the fixed-gain PI controller is mostly affected by the step change in reference speed, circuit parameters, and load condition. The accurate speed control of the drive becomes a complex issue due to the non-linear coupling between the stator current and rotor and the nonlinearity in the torque. Therefore, the fuzzy controller is more popular in highperformance IPMSM drives. <strong>Novelty:</strong> In this paper, the FLC is used as a speed controller, and the proposed control scheme reduces the computation for real-time implementation. The hysteresis current controllers are used, and outputs are given to logic, which is used to switch ON and OFF the inverter. The robustness of the FLC-based drive is verified by theoretical and simulation results. In this paper, the three inputs to the FLC are: is the change in rotor speed, wr is rotor speed, and Δe is the change in speed error. Fuzzy rules are developed by different membership functions used to obtain the optimized value (Id * and Iq*), which gives precise speed control under different operatingconditions over a wide speed range. <strong>Keywords:</strong> Interior Permanent Magnet Synchronous Motor (IPMSM); Fuzzy Logic Controller (FLC); proportionalintegral (PI) Maximum Torque per Ampere (MTPA); Field Weakening (FW); Vector Control; fieldoriented control (FOC); Hysteresis Current Controller
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Shneen, Salam Waley, Chengxiong Mao, and Dan Wang. "Advanced Optimal PSO, Fuzzy and PI Controller with PMSM and WTGS at 5Hz Side of Generation and 50Hz Side of Grid." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 1 (2016): 173. http://dx.doi.org/10.11591/ijpeds.v7.i1.pp173-192.
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To use different control systems, Like Classical PI Controller, Expert System, Fuzzy Logic Controller and Optimization PSO Controller. It used to control for PMSM which worked in integration system to Wind Energy. Wind Energy content of wind turbine, PMSM, Rectifier, DC bus, Inverter, Filter, Load and Grid. In the first step, to run the PMSM with different speeds to get different frequency to selected the frequency in side of generation with the rated speed. Second step, solve the mathematical equation to use different values of wind speed with selected (15,20 m/s and less than with more than 15&amp;20m/s). Third step, Calculation the power generation with wind speed (15 m/5 &amp; 20 m/s). Fourth step, using these component system Rectifier, DC bus, Inverter, Filter, Load &amp; Grid with WTGS &amp; PMSM. Final step, use different control systems, Like Classical PI Controller, Expert System Fuzzy Logic Controller and Optimization PSO Controller With PMSM to analysis all results after using the simulation model of proposed variable speed based WECS. The wind turbine coupled with PMSM.A closed loop control system with a PI control,Fuzzy, PSO in the speed loop with current controllers .The simulation circuits for PMSM, inverter, speed and current controllers include all realistic components of the drive system. These results also confirmed that the transient torque and current never exceed the maximum permissible value.
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Verma, Sony, Anil Kumar, and A. K. Gupta. "Speed Control of Induction Motor using Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 788–97. http://dx.doi.org/10.22214/ijraset.2024.58052.
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Abstract: In this paper, speed control of induction motor using fuzzy logic controller is proposed. Speed control of induction motor takes place by, Direct torque control(DTC) method i.e. by directly controlling torque. Here we have used Voltage/Frequency speed control method of induction motor. The fuzzy logic controller (FLC) solves the problem of non linearity’s and parameter variation of induction motor. Unlike the conventional standard controllers, the proposed controller has much less computationally demanding. Direct torque control scheme of induction motor is firstly used. Then, the specified rule and their membership functions of proposed fuzzy logic system will be represented. The performance of a controller is evaluated under various operating conditions. A simplified FLC with relatively fewer rules will be implemented for perfect speed control.
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V N, Dr Trupti. "BLDC Motor Speed Control with PID Controller in Electric Vehicles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35332.
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This paper demonstrates that BLDC motors are the best motors for various electric vehicle control strategies. Synchronous motors are the group that includes BLDC motors. Future transportation is interested in electric vehicles because of their zero emissions, sustainability, efficiency, and variety of control methods. These motors are the best option for many applications; however, the majority of them need sensor-less control. Rotor-position sensing is necessary for BLDC motors operating in order to regulate the winding currents. A BLDC motor drive's speed can be controlled via sensor-less control, digital signal controllers, digital signal processors, fuzzy logic PI controllers, PID controllers, adaptive neuro-fuzzy controllers, genetic algorithm controllers, and other methods. The overarching goal of this research is to provide a simulation of the PID controller approach for BLDC motors in electric vehicles. Keywords: BLDC Motor, sensor, controller, sustainability
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Abdolzadeh, Somayeh, and Seyed Mohammad Ali Mohammadi. "Implementation of adaptive fuzzy controller on the variable speed wind turbines in comparison with conventional methods." Ciência e Natura 37 (December 21, 2015): 388. http://dx.doi.org/10.5902/2179460x20869.
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This paper introduces a linear structure of wind turbine, operator and the pitch angle controller. Then, a new method of adaptive fuzzy on the variable speed wind turbines was provided and it was compared with PID and Fuzzy Logic Controller and the simulation results were analyzed. So when the results of each simulation using PID and Fuzzy controllers and adaptive fuzzy PID controllers provided, the observed advantages and disadvantages of the rotor speed and wind turbine power can be introduced. It can be seen that in PID method the high overshoot is discussed as a disadvantage while it is overcome by using fuzzy controller and overshoot will decrease. The time of reaching to a sustained speed increases slightly and in adaptive fuzzy PID controller, the less overshoot has provided a good and effective performance for system response. The use of adaptive fuzzy PID controller causes the system does not have any steady-state error and at all moments of time, the response rate is better than PID and fuzzy controller. The importance of the amount of overshoot and rate fluctuations is that by reducing these parameters in addition to reducing the cost of preventive care, maintenance and depreciation, the fluctuations in electricity generated by induction generator also greatly reduced.
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Nguyen, Tat-Bao-Thien, Teh-Lu Liao, Hang-Hong Kuo, and Jun-Juh Yan. "An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/987308.
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This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties.
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Faizal, Ahmad. "Perancangan Pengendalian Kecepatan Motor Induksi Tiga Fasa Pada Mesin Sentrifugal Dengan Pendekatan Model Viteckova Orde Dua Menggunakan Metode Hybridfuzzy-SMC." Jurnal Sains dan Teknologi Industri 15, no. 2 (2018): 138. http://dx.doi.org/10.24014/sitekin.v15i2.5116.
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Induction motor has a weakness in the speed settings, the speed will change when there is a change in load or control signal disturbance, it takes a controller that is able to overcome the shortcomings of the induction motor, one of the controller is fuzzy. Fuzzy controllers have the advantage of modeling a complex non-linear function. But fuzzy controllers have weaknesses in the form of overshoot and system oscillation. One of the controllers that is able to overcome the weakness of overshoot and oscillation of the system from the fuzzy controller is the Sliding Mode Control (SMC) controller. SMC has the advantage of being robust and able to work on non-linear system systems that have model or parameter uncertainty. Based on simulation results from fuzzy hybrid controller and SMC able to cover the weakness of fuzzy and robust controller in overcoming the load and disturbance changes. Proven with time response analysis on overshoot and steady state error better than fuzzy controller with longer transient time value at maximum load with steady state error 0,0085 Rpm with Maximum overshoot 0,38% and without system oscillation.
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R. Nagarajan, M. Gokulkannan, T. Dinesh, S. Murugesan, and M. Naveenprasanth. "PERFORMANCE IMPROVEMENTS OF SEPARATELY EXCITED DC MOTOR USING FUZZY LOGIC CONTROL." International Journal of Engineering Technologies and Management Research 6, no. 2 (2020): 47–58. http://dx.doi.org/10.29121/ijetmr.v6.i2.2019.355.
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This paper demonstrates the importance of a fuzzy logic controller over conventional method. The performance of the separately excited DC motor is analyzed by using fuzzy logic controller (FLC) in MATLAB/SIMULINK environment. The FLC speed controller is designed based on the expert knowledge of the fuzzy rules system. The proposed DC motor speed control fuzzy rules are designed for fuzzy logic controller. The output response of the system is obtained by using fuzzy logic controller. The designed fuzzy controller for speed control performance is investigated. Significantly reducing the overshoot and shortening the settling time of the speed response of the motor. They validate different control of approaches, the simulation results show improvement in motor efficiency and speed performance.
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Hayashi, Kenichiro, Akifumi Otsubo, and Kazuhiko Shiranita. "Improvement of Control Performance for Low-Dimensional Number of Fuzzy Labeling Using Simplified Inference." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 431–38. http://dx.doi.org/10.20965/jaciii.1999.p0431.
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One of the important concepts in fuzzy control is fuzzy inference, and simplified inference, which increases the speed of the fuzzy inference, has been used to realize a high-speed fuzzy controller. In designing a fuzzy controller, a high dimension, such as 7 x 7 or 5 x 5 partitions, is frequently used for the number of fuzzy labeling. However, as the number of fuzzy labeling increases, the number of control parameters increases rapidly and tuning of the fuzzy controller becomes difficult. Therefore, a fuzzy controller is required to be partitioned into a low number of fuzzy labeling, such as 3 x 3 partitions. With this in mind, first, a method of improving control performance for a low number of fuzzy labeling using simplified inference which enables high-speed inference, is proposed in this paper. Next, the effectiveness of this improvement method is studied based on the results of several simulations where a first-order lag system with dead time, a representative model for plant characteristics, is used as the controlled system.
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Ryu, Seung-Min, Kang-Hyeon Choi, and Hyuk-Jun Chang. "Interval type-2 intelligent fuzzy vehicle speed controller design using headlamp reflection detection and an adaptive neuro–fuzzy inference system." PLOS One 20, no. 6 (2025): e0323913. https://doi.org/10.1371/journal.pone.0323913.
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In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. In situations with limited distance data, we also design a fuzzy controller using the adaptive neuro–fuzzy inference system (ANFIS). To enhance robustness against disturbances, the interval type-2 approach is used. For the distance estimation algorithm, the vehicle is positioned at predefined intervals from the target object, capturing images of the headlights at each point. The region of interest containing the light is extracted from each image and segmented by light intensity. Weighted values are then assigned to each segment based on intensity, producing an image value that correlates with the distance. This image-derived value is then used as distance data for the design of the fuzzy controller. The controller is implemented using the interval type-2 fuzzy logic toolbox in MATLAB/SIMULINK, with vehicle speed and image intensity values as inputs and control torque as the output to adjust vehicle speed. The noise from the vehicle speed sensor is treated as a disturbance, and the performance of the interval type-2 fuzzy controller is evaluated under these disturbance conditions. Additionally, fuzzy controllers are designed for vehicle positions between 41–43 m and 47–49 m, and these controllers are trained using ANFIS to function effectively across the entire 41–49 m range. Simulation results demonstrate that, with the controller integrated into the vehicle system, the vehicle is successfully controlled to reach the target position.
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Li, Ruifeng, and Peifen Gong. "Fuzzy PID Speed Controller of DC Motor Based on MATLAB." Journal of Physics: Conference Series 2417, no. 1 (2022): 012037. http://dx.doi.org/10.1088/1742-6596/2417/1/012037.
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Because of its few of components and low price, DC motors frequently serve in the field of automatic control. The control accuracy and stability of the DC motor depend on the parameters of the control system, so it is of great significance to design the controller and optimize the control parameters. This paper presents a Fuzzy PID control framework, which successfully works on the performance of DC motor. The controller is based on the basic rules of fuzzy control theory and uses MATLAB simulation to realize its function. To look at the control precision of Fuzzy PID and the dependability of motor activity, Fuzzy PID algorithm and PID algorithm are simulated in MATLAB programming, separately. The simulation data show that the control effect of Fuzzy PID controllor is better than PID controllor in the DC motor control system, so Fuzzy PID controllor is worth popularizing.
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Zheng, Xue Qin. "Control of Hybrid Stepping Motor Drive Using Computational Verb PID Controllers." Advanced Materials Research 853 (December 2013): 428–34. http://dx.doi.org/10.4028/www.scientific.net/amr.853.428.
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Thanks to the development of microprocessors, hybrid stepping motors have been widely used in many areas where they perform positioning operations. However, the stepping motor suffers from system variations, low performance and lack of adaptability to load variations, which slow down their responding speed of high-precision positioning operations. In this paper, a computational verb PID controller is proposed to control the position of a stepping motor drive. The simulation results show that the computational verb PID controller has better performances than conventional and fuzzy PID controllers. The simulation results also show that the responding speed and positioning accuracy of the controlled hybrid stepping motor were greatly improved. Computational verb PID controller has much less computational complexity than fuzzy PID controller.
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Tu, Zhihao, Jiehong Wu, Xin Huang, Ruei-Yuan Wang, and Ho-Sheng Chen. "The Study of Numerical Simulation Based on Fuzzy PID Controller." International Journal of Advanced Engineering Research and Science 10, no. 10 (2023): 108–13. http://dx.doi.org/10.22161/ijaers.1010.10.
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This study aims to explore the application of an adaptive fuzzy PID (proportional integral differential) controller in system control. This controller combines the characteristics of fuzzy control and PID control and dynamically adjusts the parameters of the PID controller through a fuzzy inference mechanism to achieve adaptive adjustment of the system's dynamic characteristics. Firstly, by taking the control object of an industrial process as an example, the transfer function of the controlled object is determined to determine the initial parameters of the PID controller. Subsequently, a fuzzy inference module was introduced to adjust the proportional, integral, and differential coefficients of the PID controller through fuzzy rules based on the current state and error situation of the system. The simulation results show that, compared to traditional PID controllers, the adaptive fuzzy PID controller has achieved significant improvements in dynamic response speed and stability. Especially in the face of complex and rapidly changing control systems.
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Fan, Liping, and Yi Liu. "Fuzzy Self-Tuning PID Control of the Main Drive System for Four-High Hot Rolling Mill." Journal of Advanced Manufacturing Systems 14, no. 01 (2015): 11–22. http://dx.doi.org/10.1142/s021968671550002x.
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The main drive system used for four-high mill is usually a double closed-loop DC motor speed regulating system. Generally, the speed controller and current controller are proportional-integral-derivative (PID) controllers. The parameters of these controllers are usually determined by the engineering design method and are constant in the whole process. So the controllers can give good control effect in the rated operating condition. Once disturbance occurs, the control effects are often just passable. In this paper, fuzzy logic is used to adjust the parameters of the speed PID controller to improve the disturbance rejection ability. Simulation results show that the fuzzy tuning PID controller can give better control effect than the regular designed PID control.
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Ahmed, Akram H., Abd El Samie B. Kotb, and Ayman M. Ali. "Comparison between Fuzzy Logic and PI Control for The Speed Of BLDC Motor." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 3 (2018): 1116. http://dx.doi.org/10.11591/ijpeds.v9.i3.pp1116-1123.
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In this paper the analytical comparison of brushless DC (BLDC) motor drive with proportional integral (PI) and fuzzy logic controller (FLC) based speed controllers is estimated. Proportional integral (PI) has disadvantages like it do not operate properly when the system has a high degree of load disturbances.<em> </em>In recent years, the application of fuzzy logic controller (FLC) for high dynamic performance of motor drives has become an important tool. FLC is a good for load disturbances and can be easily implemented. The modeling and simulation of both the speed controllers have been made by MATLAB/SIMULINK. The dynamic characteristics of the BLDC motor (speed and torque) response, obtained under PI and Fuzzy logic based speed controller, are compared for various operating condition.
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Akbatı, Onur, Hatice Didem Üzgün, and Sirin Akkaya. "Hardware-in-the-loop simulation and implementation of a fuzzy logic controller with FPGA: case study of a magnetic levitation system." Transactions of the Institute of Measurement and Control 41, no. 8 (2018): 2150–59. http://dx.doi.org/10.1177/0142331218813425.
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This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.