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Relevant bibliographies by topics / BLDC Motor; speed control; neural network controller / Journal articles

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Journal articles on the topic 'BLDC Motor; speed control; neural network controller'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Obed, Adel, and Ameer Saleh. "Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network." Iraqi Journal for Electrical and Electronic Engineering 10, no. 2 (2014): 118–29. http://dx.doi.org/10.37917/ijeee.10.2.7.

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In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learnin

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2

Bektaş, Yasin, Hulusi Karaca, and Taner Dindar. "PI CONTROLLER WITH NEURAL NETWORK ADJUSTMENT FOR SPEED REGULATION IN BRUSHLESS DIRECT CURRENT MOTOR." Applied Researches in Technics, Technologies and Education 6, no. 1 (2018): 16–29. http://dx.doi.org/10.15547/artte.2018.01.003.

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Brushless DC motor (BLDCM) has been widely used in many different fields such as high efficiency and dynamic response and high speed range in recent years. Since the BLDC motor driver does not behave, it is complex to control it via the proportional-integral (PI) controller. In this article, the mathematical model of the BLDC motor and artificial neural network algorithm is derived to make the BLDC motor control. On the proposed drive, the controller synchronizes quickly with speed, learning the motor speed to follow and load quickly. The effectiveness of the proposed method is demonstrated by

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3

Lokesh, Kumar Agrawal, Kumar Chauhan Bhavesh, Kumar Saxena Nitin, and Joshi Puneet. "Speed control of BLDC motor with neural controller." Indian Journal of Science and Technology 14, no. 4 (2021): 373–81. https://doi.org/10.17485/IJST/v14i4.2164.

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Abstract <strong>Objective: </strong>BLDC motor specifically used for high speed applications and needs sophisticated speed control. Conventional controls techniques require more physical involvement for assisting BLDC motor to operate with precise speed value. However, soft tuning based controller may give comparatively better results for that and the same has been elaborated in present paper using neural controller of speed as per requirements.the main objective of this study may be summarized as (i) MATLAB simulink model for BLDC motor incorporated with soft tuned controller and invert

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Diyah, Kammel Shary, Jaber Nekad Habeeb, and Abdulelah Alawan Mazin. "Speed control of brushless DC motors using (conventional, heuristic, and intelligent) methods-based PID controllers." Speed control of brushless DC motors using (conventional, heuristic, and intelligent) methods-based PID controllers 30, no. 3 (2023): 1359–68. https://doi.org/10.11591/ijeecs.v30.i3.pp1359-1368.

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One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best respons

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Bishnu, Prasad Panda. "SPEED CONTROL OF DC BRUSHLESS MOTOR FED BY SIX STEP INVERTER USING MATLAB." International Journal For Technological Research In Engineering 10, no. 10 (2023): 160–70. https://doi.org/10.5281/zenodo.10435204.

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This project proposed a control scheme of a neural network for the brushless direct current (BLDC) permanent magnet motor drives. Brushless DC motors rely on semiconductor switches to turn stator windings on and off at the appropriate time. The process is called electronic commutation. The behavior of BLDC motor drive is nonlinear, cause it is complex to handle by using conventional proportional-integral (PI) controller. In order to overcome this main problem, artificial neural network controller technique is developed. The neural network control learned continuously and gradually becomes the

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Kammel Shary, Diyah, Habeeb Jaber Nekad, and Mazin Abdulelah Alawan. "Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1359. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1359-1368.

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One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best respons

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7

G. Madhusudhana Rao, A. Raghu Ram, Ch Vinay Kumar,. "AI based Vector Control Method for BLDC Motor with Multi Switch Three-Phase Topology." Psychology and Education Journal 58, no. 1 (2021): 3132–41. http://dx.doi.org/10.17762/pae.v58i1.1218.

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Brushless direct current (BLDC) Motors are extensively used because of their characteristics. Such characteristics are high dynamic response and high-power density. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. In this paper it is proposed that a neural network controller is used for the five level switch of the BLDC motor to enhance the power factor and reduce the current distortions with respect to its rise time, startup torque. This method is also done in comparison with the PID controllers. The working principle of the BLDC is with the help of five-switch con

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8

Obed, Adel Ahmed, Ameer Lateef Saleh, and Abbas Kareem Kadhim. "Speed performance evaluation of BLDC motor based on dynamic wavelet neural network and PSO algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 10, no. 4 (2019): 1742. http://dx.doi.org/10.11591/ijpeds.v10.i4.pp1742-1750.

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<span>In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain an novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting

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9

Adel, Ahmed Obed, Lateef Saleh Ameer, and Kareem Kadhim Abbas. "Speed performance evaluation of BLDC motor based on dynamic wavelet neural network and PSO algorithm." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 4 (2019): 1742–50. https://doi.org/10.11591/ijpeds.v10.i4.pp1742-1750.

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In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain a novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting and selflearn

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10

Vinay Kumar, C. H., G. Madhusudhana Rao, A. Raghu Ram, and Y. Prasanna Kumar. "Designing of Neuro-Fuzzy Controllers for Brushless DC Motor Drives Operating with Multiswitch Three-Phase Topology." Journal of Electrical and Computer Engineering 2022 (July 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/7001448.

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Brushless DC motors are simple in construction, high efficiency, less noise, and high reliability, which are not replaceable motors in specific applications compared to other motor drives. It has a facility for its multivariable system, nonlinear control process, and powerful coupling system. This paper proposes to design the neuro-fuzzy controllers for its multiple converters switching to improve the power factor and minimize the BLDC motor switching losses. Compared with conventional controllers, this controller will develop the power factor and optimize the current ripples concerning time a

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11

Widjonarko, W., Cries Avian, Prakosa Widyawan, and Bayu Rudiyanto. "Performance improvement on motor BLDC speed controller by using multi controller with summation technique." Journal of Applied Engineering Science 19, no. 4 (2021): 902–9. http://dx.doi.org/10.5937/jaes0-30440.

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BLDC motor is the most widely used in the industrial world, especially in electric vehicles. With this increasing demand, a variety of research topics emerged in BLDC motors. One popular research is on BLDC motor speed control topics to maintain speed for its application, such as intelligent cruise technology in electric cars and conveyors for line assembly. However, from several existing studies, the BLDC Motor controller still uses a single controller model. The controller's output is purely from the controller without any improvement in characteristics and has a problem with the oscillating

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12

Wang, Tingting, Hongzhi Wang, Huangshui Hu, and Chuhang Wang. "LQR optimized BP neural network PI controller for speed control of brushless DC motor." Advances in Mechanical Engineering 12, no. 10 (2020): 168781402096898. http://dx.doi.org/10.1177/1687814020968980.

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This paper proposes a linear quadratic regulator (LQR) optimized back propagation neural network (BPNN) PI controller called LN-PI for the speed control of brushless direct current (BLDC) motor. The controller adopts BPNN to adjust the gain [Formula: see text] and [Formula: see text] of PI, which improves the dynamic characteristics and robustness of the controller. Moreover, LQR is adopted to optimize the output of BPNN so as to make it close to the target PI gains. Finally, the optimized control output is inputted into the BLDC motor system to achieve speed control. The performance analysis

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13

Chaithanakulwat, A., N. Thungsuk, T. Savangboon, S. Kaewpetch, and T. Tanaram. "ARTIFICIAL INTELLIGENCE BASED ON THE OPTIMAL STRATEGY OF MULTILEVEL CONVERTER FOR BRUSHLESS DC MOTOR." Journal of Southwest Jiaotong University 57, no. 1 (2022): 246–56. http://dx.doi.org/10.35741/issn.0258-2724.57.1.23.

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This paper presents an interesting novelty in the implementation of artificial intelligence (AI) to apply multi-level converter control, the best strategy for the Brushless DC motor control (BLDC). The interesting highlight of this research is the importance of bringing artificial intelligence (AI) to develop multi-level converter mechanisms using the artificial neural network algorithm. The neural network is a branch of artificial intelligence and is used to detect the magnitude of the input voltage from batteries. The operation of this DC-DC voltage conversion mechanism detects the output vo

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14

P. Rajesh, Francis H Shajin, V. Ansal, and Vijay Kumar B. "Enhanced artificial transgender longicorn algorithm & recurrent neural network based enhanced DC-DC converter for torque ripple minimization of BLDC motor." Journal of Current Science and Technology 13, no. 2 (2023): 182–204. http://dx.doi.org/10.59796/jcst.v13n2.2023.1735.

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This paper proposes an enhanced DC-DC converter with hybrid control method for torque ripple minimization of BLDC motor. Initially, a BLDC motor is controlled with an enhanced Cuk converter. The application of a switched inductor is used to update the Cuk converter operation. In this method, the control mechanism incorporates two control loops, namely, the speed control loop and torque control loop, which are utilized to recover the execution of BLDC. Thus, the proposed system is the combined performance of the Enhanced Artificial Transgender Longicorn Algorithm (EATLA) and Recurrent Neural Ne

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15

Jawad Ali, Husam, Diyah Kammel Shary, and Hayder Dawood Abbood. "A Review of Intelligent Techniques Based Speed Control of Brushless DC Motor (BLDC)." Basrah journal for engineering science 24, no. 1 (2024): 109–19. http://dx.doi.org/10.33971/bjes.24.1.12.

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This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified u

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16

Owusu, George, John Kojo Annan, and Solomon Nunoo. "Neural Network-Based Optimisation of Sinusoidal PWM Controller for VSI-Driven BLDC Motor." Power Electronics and Drives 8, no. 1 (2023): 275–98. http://dx.doi.org/10.2478/pead-2023-0018.

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Abstract Although increasing the number of switches increases the switch losses, most designed controllers focus on controlling an inverter circuit with more than six switches. The paper aims to address this issue that arises in implementation of the voltage source inverter (VSI) for brushless DC (BLDC) motors. It optimises the sinusoidal pulse width modulation (PWM) controller, minimising total harmonic distortion (THD) while keeping the VSI’s circuit at six switches to avoid increased switching losses. This was achieved by applying an artificial neural network (ANN) to generate a signal, whi

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17

Ahmed Baba, Mariem, Mohamed Naoui, and Mohamed Cherkaoui. "Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications." Sustainability 15, no. 13 (2023): 10430. http://dx.doi.org/10.3390/su151310430.

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The adoption of the brushless DC motor in the electric drive vehicle industry continues to grow due to its robustness and ability to meet torque–speed requirements. This work presents the implementation of a fault-tolerant control (FTC) for a BLDC motor designed for electric vehicles. This paper focuses on studying the defect in the Ha sensor and its signal reconstruction, assuming possible cases, but the same principle is applied to the other two sensors (Hb and Hc ). In this case, the fault diagnosis allows for the correction and reconstruction of the signal in order to compel the system to

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18

Bagavathy, S., and P. Maruthu Pandi. "Improved design of an intelligent controller for speed control of brushless dc motor (BLDCM)." Journal of Electrical Engineering 21, no. 3 (2021): 125–32. http://dx.doi.org/10.59168/npnl5947.

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Brushless Direct Current Motor (BLDCM) drives are progressively prominent in traction as well as in industrial operations. The rheostat of BLDCM in multi quadrants is significant. By utilizing smart controller, the adaptability of the drive mechanism expanded. In this paper, the solar panel is encouraged by the BLDCM, and the for driving the power inverter channel signals of PWM for BLDCM have been effectively executed utilizing a controller with and the motor controlled by all the four quadrants with no consumption of the energy power is preserved through re-forming braking time interval. By

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19

Wang, Xin. "Research on BLDCM Speed Control System Based on MFNN-PID Control Method." Applied Mechanics and Materials 121-126 (October 2011): 3714–18. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3714.

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It is very difficult to solve brushless DC motor(BLDCM) overshooting and short time oscillation during keeping speed of BLDCM.In view of shortages of normal PID controller, Modified Fuzzy Neural Network adjusting PID parameters and its Algorithm are researched.The MFNN’s computation and convergence rates are better than FNN adjusting PID parameters.At last,mathematical expression of MFNN adjusting PID parameters is given.With Matlab sumulation,conclusion is the MFNN adjusting PID parameters is better in BLDCM speed control system robustness and real-time to solve interference of the system tha

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20

He, Ying, Bo Mo, and Yang Hua Li. "Precise Control of High-Speed BLDCM Based on BP Neural Network." Applied Mechanics and Materials 615 (August 2014): 365–69. http://dx.doi.org/10.4028/www.scientific.net/amm.615.365.

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Brushless DC Motor (BLDCM) is a strongly nonlinear systems, which means that we can’t get a satisfactory result by the traditional PID Controller. This paper presents an improved BP Neural Network algorithm with adaptive adjustment of learning rate and range adjustment of the output. At last, using MatLab/Simulink to verify the algorithm that it has better control precision and adaptability.

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21

Ravipati, Srikanth, Venkatesan Mani, and SrinivasaRao Yarlagadda. "Performance Evaluation of PI/RBFN ANN Controllers for Sensor Less BLDC Motor Control Based Fuel Cell/PV Hybrid Electric Vehicle." Journal of New Materials for Electrochemical Systems 24, no. 3 (2021): 183–94. http://dx.doi.org/10.14447/jnmes.v24i3.a06.

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The present society suffers with the problem of the greenhouse effect due to the emission of huge amount of carbon dioxide. And almost 70% of emission of carbon dioxide will be due to the usage of automotive vehicles. It is required to reduce the utilization of automotive vehicles to protect the life of earth for the coming years. This manuscript presents the design of electric vehicle with the utilization of renewable energy source like solar energy with the combination of fuel cell energy. It involves the design of maximum power point tracking system with intelligent fuzzy controller to trac

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22

Gobinath, S., and M. Madheswaran. "Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor." Soft Computing 24, no. 13 (2019): 10161–80. http://dx.doi.org/10.1007/s00500-019-04532-z.

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23

Agrawal, Lokesh Kumar. "Speed control of BLDC motor with neural controller." Indian Journal of Science and Technology 14, no. 4 (2021): 373–81. http://dx.doi.org/10.17485/ijst/v14i4.2164.

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Yin, Hongqiao, Wenjun Yi, Jintao Wu, Kangjian Wang, and Jun Guan. "Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System." Mathematics 10, no. 1 (2021): 118. http://dx.doi.org/10.3390/math10010118.

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Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and appl

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25

Han, Hong Pei, and Wu Wang. "WNN Optimizing PID Controller for BLDC Control System." Applied Mechanics and Materials 278-280 (January 2013): 1529–32. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1529.

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Brushless DC motors (BLDC) are widely used for many industrial applications because of their high efficiency, high torque and low volume. This paper presents the PID control for BLDC Motor, because good control effect cannot be acquired by using the traditional PID control in the non-linear variable time servomechanism and it is difficult to tune the parameters and get satisfied control characteristics, some intelligent techniques should be taken. Wavelet Neural Network (WNN) was constrictive and fluctuant of wavelet transform and has self-study, self adjustment and nonlinear mapping functions

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26

Shelke, Mr Nikhil S. "Artificial Neural Network for Speed Control of BLDC Motor." International Journal for Research in Applied Science and Engineering Technology 7, no. 7 (2019): 52–56. http://dx.doi.org/10.22214/ijraset.2019.7010.

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27

Yao, Guozhong, Jiayu Feng, Guiyong Wang, and Shaojun Han. "BLDC Motors Sensorless Control Based on MLP Topology Neural Network." Energies 16, no. 10 (2023): 4027. http://dx.doi.org/10.3390/en16104027.

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In order to reduce the complexity of the brushless DC motor (BLDC)-control-system algorithm while improving the estimation performance of the rotor phase position and the speed of the sensorless motor, a neural network (ANN) control algorithm based on multi-layer perceptron (MLP) topology is proposed. The phase voltage of the motor is conditioned to obtain the phase-voltage signal with a high signal-to-noise ratio, which is used as the input eigenvalue of the multi-layer-perceptron-topology neural network algorithm. The encoder signal is used as the training test data of the MLP-ANN. The first

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Rif'an, Muhammad, Feri Yusivar, and Benyamin Kusumoputro. "Sensorless-BLDC motor speed control with ensemble Kalman filter and neural network." Journal of Mechatronics, Electrical Power, and Vehicular Technology 10, no. 1 (2019): 1. http://dx.doi.org/10.14203/j.mev.2019.v10.1-6.

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The use of sensorless technology at BLDC is mainly to improve operational reliability and play a role for wider use of BLDC motors in the future. This research aims to predict load changes and to improve the accuracy of estimation results of sensorless-BLDC. In this paper, a new filtering algorithm is proposed for sensorless brushless DC motor based on Ensemble Kalman filter (EnKF) and neural network. The proposed EnKF algorithm is used to estimate speed and rotor position, while neural network is used to estimate the disturbance by simulation. The proposed algorithm requires only the terminal

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29

AHMADI, SOFYAN, KHAIRUL ANAM, and WIDJONARKO WIDJONARKO. "Peningkatan Efisiensi Energi pada Kendaraan Listrik dengan Elektronik Diferensial Berbasis ANN (Artificial Neural Network)." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 642. http://dx.doi.org/10.26760/elkomika.v8i3.642.

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ABSTRAKSeiring dengan perkembangan teknologi kendaraan listrik yang saat ini semakin canggih dan berkembang sangat cepat, upaya pengembangan kendaraan listrik terus dilakukan, salah satunya penggunaan motor BLDC dalam kendaraan listrik untuk meningkatkan efisiensi. Penelitian ini menggunakan kontrol ANN (Artificial Neural Network) pada mikrokontroler serta metode differential untuk pengontrolan kecepatan putar motor BLDC. Pengujian Percepatan dengan menempuh jarak 200 meter arus rata-rata sebesar 1,05 ampere. Daya rata-rata pada pengujian efisiensi sebesar 101 watt. Hasil efisiensi dari penguj

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A. Obed, Adel, and Ameer L. Saleh. "Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network." Iraqi Journal for Electrical And Electronic Engineering 10, no. 2 (2014): 118–29. http://dx.doi.org/10.33762/eeej.2014.95599.

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Ji, H., and Zhi Yong Li. "Study on Intelligent Controller of Permanent Magnet Synchronous Motor." Advanced Materials Research 764 (September 2013): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amr.764.149.

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This paper puts forward a novel design method of controller based on BP neural network, which is applied to the permanent magnet synchronous motor (PMSM) double closed loop speed regulation system of speed regulator, by using the neural network controller instead of traditional PID controller. It applies the nonlinear adaptive ability of neural network for optimizing the control parameters of PID controller for PMSM. The simulation model was established in Matlab/Simulink. The simulation results indicate that the neutral network PID controller, compared with the traditional PID, has strong rob

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Quan, Xun Zhong, Xiao Wei Liao, Yan Wu, and Li Wang. "Study on Chaotic Operation and Control System of Ultrasonic Motor." Applied Mechanics and Materials 462-463 (November 2013): 782–87. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.782.

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Ccording to the running speed of ultrasonic motor instability, design a motor testing system, the closed-loop controller embedded improved neural networkalgorithm, to suppress chaos. In open-loop state, input constant parameters to the motor, the motor rotating speed detection, the test data is chaotic analysis,found the speed has chaotic characteristics; in the closed-loop state, the feedback signal through the controller for processing, effectively inhibited theultrasonic motor speed jitter phenomenon, so as to improve the smoothness of motion motor. Experimental results show that, the neura

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Nguyen, Trong-Thang. "The neural network-based control system of direct current motor driver." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1445–52. https://doi.org/10.11591/ijece.v9i2.pp1445-1452.

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This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on th

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Nguyen, Trong-Thang. "The neural network-based control system of direct current motor driver." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1445. http://dx.doi.org/10.11591/ijece.v9i2.pp1445-1452.

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<span lang="EN-US">This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive c

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M., Elgohary, Gouda E., and S. Eskander S. "Intelligent control of induction motor without speed sensor." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 2 (2021): 715–25. https://doi.org/10.11591/ijpeds.v12.i2.pp715-725.

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This paper presents a proposed sensorless algorithm for induction motor (IM) speed control based on artificial neural networks (ANNs). The Indirect rotor field oriented (IRFO) technique is applied to control the motor. It is designed based on the proportional integral (PI) controller. The particle swarm optimization (PSO) algorithm is used as a good solution for the problems associated with the design of the proportional integral (PI) controller gains. The PSO is compared with the conventional methods. The proposed controller (PSO-PI) is then integrated with the artificial neural network (ANN)

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Wang, Fu Zhong, and Qiong Xia Yu. "Speed-Regulation System of PMLSM Based on BP Neural Network PID Control." Advanced Materials Research 466-467 (February 2012): 1217–21. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1217.

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For the characteristics of permanent magnet linear synchronous motor (PMLSM) hoisting system’s nonlinear, time-varying and vulnerable to disturbance, based on the established PMLSM d-q axis dynamic model, designed of an improved BP neural network PID speed controller. Modified the fixed learning rate in BP neural network to adaptive adjustable, and added the momentum to reduce the oscillation tendency in the learning process, greatly improved the convergence speed and avoided the network into a local minimum. Compared with the simulations of traditional PID and the improved BP network PID spee

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Zhang, Hui Min, Hai Yan Wang, Jing Zhuo Shi, and Xun Liu. "Start Control of Ultrasonic Motor Based on Neural Network." Advanced Materials Research 383-390 (November 2011): 1623–28. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1623.

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It is very hard for the traveling wave ultrasonic motor to start directly with high speed, because of their special running mechanism and the unique features. To solve this problem and realize the uitrasonic motor’s high-speed start control, speed controller with on-line self-tuning parameters is designed. Neural network is used to realize the online adjustment of controller’s parameters, to agree with the different starting request of different speed references, and make best use of motor’s ability. The experiments indicated that the motor start fast and accurately, the control algorithm is e

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Hassan, Farhan Rashag. "Improved speed response of DC motor via intelligent techniques." International Journal of Advances in Applied Sciences (IJAAS) 8, no. 3 (2019): 204–7. https://doi.org/10.11591/ijaas.v8.i3.pp204-207.

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The classical Proportional – Integral (PI) control for Direct Current (DC) motor causes slow response of actual speed with high overshoot and undershoot which leads to sluggishness of the system. To minimize the problem of PI controller, intelligent technique based on hybrid neural network sliding mode control NN-SMC is suggested. The benefits of SMC are that it is simple, and tough to parameter deviations as compared with other controllers. In this paper, the neural network NN is used to minimize the error between reference speed and actual speed. In addition, the SMC aim is to control

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Wang, Li Huo, Wei Yao, Ke Wei Hu, and Wei Zhang. "Fuzzy Neural Network Applied in Sensorless BLDC Motor Control Based on MC56F8013." Applied Mechanics and Materials 63-64 (June 2011): 841–45. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.841.

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This paper describes Freescale company’s DSP device MC56F8013 implementation of a control method for sensorless BLDC motor, which is according to fuzzy neural network. The inductance of stator core varies according to the variation of rotor position, and the rotor position can be estimated with the variation of current response caused by six direction voltage vectors. Then use fuzzy neural network PI control method to optimize the dynamic performance, accelerate the rotor to a certain speed, and switch to EMF mode. The experiment results show that the proposed method can have fast response, st

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Elgohary, M., E. Gouda, and S. S. Eskander. "Intelligent control of induction motor without speed sensor." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (2021): 715. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp715-725.

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<p>This paper presents a proposed sensorless algorithm for induction motor(IM)speed control based on artificial neural networks (ANNs).The Indirect rotor field oriented (IRFO) technique is applied to control the motor. It is designed based on the proportional-integral (PI) controller. The particle swarm optimization (PSO) algorithm is used as a good solution for the problems associated with the design of the proportional-integral (PI) controller gains.The PSO is compared with the conventional methods. The proposed controller (PSO-PI) is then integrated with the artificial neural network(

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N. Abd, Aula. "Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA." DJES 11, no. 3 (2018): 71–78. http://dx.doi.org/10.24237/djes.2018.11311.

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In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse ne

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Tien-Chi Chen and Tsong-Terng Sheu. "Model reference neural network controller for induction motor speed control." IEEE Transactions on Energy Conversion 17, no. 2 (2002): 157–63. http://dx.doi.org/10.1109/tec.2002.1009462.

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Chen, T. C., and T. T. Sheu. "Model Reference Neural Network Controller for Induction Motor Speed Control." IEEE Power Engineering Review 22, no. 4 (2002): 73. http://dx.doi.org/10.1109/mper.2002.4312121.

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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

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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

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Sabouni, E., B. Merah, and I. K. Bousserhane. "Adaptive backstepping controller design based on neural network for PMSM speed control." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1940. http://dx.doi.org/10.11591/ijpeds.v12.i3.pp1940-1952.

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<span lang="EN-US">The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque a

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E., Sabouni, Merah B., and K. Bousserhane I. "Adaptive backstepping controller design based on neural network for PMSM speed control." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1940–52. https://doi.org/10.11591/ijpeds.v12.i3.pp1940-1952.

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The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load tor

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Dmitrii, Shprekher, Babokin Gennadii, Kolesnikov Evgenii, and Zelenkov Aleksandr. "Rating the speed of the shearer’s electric motor drive load automatic control." Izvestiya vysshikh uchebnykh zavedenii Gornyi zhurnal, no. 6 (September 24, 2020): 109–20. http://dx.doi.org/10.21440/0536-1028-2020-6-109-120.

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Introduction. It is possible to improve productivity, effectiveness, and cost-efficiency of coal extraction due to the efficient use of physical resources, technical upgrade of mechanized longwall equipment, and introduction of advanced technologies and control methods. The existing method of shearer electric motor drive automation based on the automated load controller of Uran type has a significant drawback of low speed. In case the actuator (A) meets solid rock and the shearer’s (S) speed is not changed, it may result in heavy shock loads on A and its transmission, therefore, increased wear

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Rashag, Hassan Farahan. "Improved speed response of DC motor via intelligent techniques." International Journal of Advances in Applied Sciences 8, no. 3 (2019): 204. http://dx.doi.org/10.11591/ijaas.v8.i3.pp204-207.

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<p>The classical Proportional- Integral (PI) control for Direct Current (DC) motor causes slow response of actual speed with high overshoot and undershoot which leads to sluggishness of the system. To minimize the problem of PI controller, intelligent technique based on hybrid neural network sliding mode control NN-SMC is suggested. The benefits of SMC are that it is simple, and tough to parameter deviations as compared with other controllers. In this paper, the neural network NN is used to minimize the error between reference speed and actual speed. In addition, the SMC aim is to contro

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Nishiumi, T., and J. Watton. "Model reference adaptive control of an electrohydraulic motor drive using an artificial neural network compensator." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 2 (1997): 111–22. http://dx.doi.org/10.1243/0959651971539939.

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An artificial neural network is used as the feedback compensation element of a servovalve/ motor speed control system. The network is established on a variable gain/acceleration feedback principle and trained using computer simulation techniques. The network is then emulated as a real-time controller to significantly improve the speed characteristic of the motor. It is then shown how a feedforward network utilizing the motor pressure differential and speed may be readily adapted, in response to changes in motor speed, supply pressure and load torque, to track the prescribed input/output dynami

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