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

Contents

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

Academic literature 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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Journal articles on the topic "BLDC Motor; speed control; neural network controller"

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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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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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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:&nbsp;</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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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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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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&lt;span&gt;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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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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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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Dissertations / Theses on the topic "BLDC Motor; speed control; neural network controller"

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Jebelli, Ali. "Development of Sensors and Microcontrollers for Underwater Robots." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31283.

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Nowadays, small autonomous underwater robots are strongly preferred for remote exploration of unknown and unstructured environments. Such robots allow the exploration and monitoring of underwater environments where a long term underwater presence is required to cover a large area. Furthermore, reducing the robot size, embedding electrical board inside and reducing cost are some of the challenges designers of autonomous underwater robots are facing. As a key device for reliable operation-decision process of autonomous underwater robots, a relatively fast and cost effective controller based on F
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Tai, Cam Jun, and 戴鏗峻. "Design of Neural Network Controller via Genetic Algorithms with Application to Induction motor Speed Control." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/54123815108342168789.

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Chang, Ming-Hung, and 張明弘. "SPEED CONTROL APPROACH OF THE PERMANENT-MAGNET SYNCHRONOUS MOTOR USING AUTOMATIC GENERATION FUZZY NEURAL NETWORK CONTROLLER." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/69043319566167397621.

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碩士<br>大同大學<br>電機工程學系(所)<br>94<br>This thesis presents the design and implementation of an Automatic Generation Fuzzy Neural Network (ADFNN) controller suitable for real-time control of the speed control of the permanent-magnet synchronous motor (PMSM) to track periodic step and sinusoidal reference inputs. The structure and parameter learning are done automatic and online. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta law. Several simulation results are provided to demonstrate fast
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Book chapters on the topic "BLDC Motor; speed control; neural network controller"

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Megrini, Meriem, Ahmed Gaga, and Youness Mehdaoui. "A Comparative Study of Neural Network and Fuzzy Logic Controller Approaches for BLDC Motor Speed Control." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93448-3_25.

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Ali, Brwene Salah, Hany M. Hasanien, and Yasser Galal. "Speed Control of Switched Reluctance Motor Using Artificial Neural Network Controller." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_2.

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Prasad, Bhawesh, Raj Kumar, and Manmohan Singh. "Performance Analysis of Neural Network Predictive Controller for the Speed Control of DC Motor." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7384-8_53.

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Benayad, Nizar, Abdelaziz Aouiche, and Abdelghani Djeddi. "Artificial Neural Network Speed Controller for Squirrel Cage Induction Motor Based on Direct Torque Control." In Advances in Science, Technology & Innovation. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-71926-4_35.

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Miao, Jingli, Wangyu Qin, and Dawei Zheng. "Vector Control of Three Phase Permanent Magnet Synchronous Motor Based on Neural Network Sliding Mode Speed Controller." In Geo-informatics in Sustainable Ecosystem and Society. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7025-0_28.

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Trivedi, Ikshita, Abhishek Singh, Amit Katare, Vikram Saini, and Ankit Tiwari. "Comparative Study of Speed Control of Brushless DC Motor." In SCRS Proceedings of International Conference of Undergraduate Students. Soft Computing Research Society, 2023. http://dx.doi.org/10.52458/978-81-95502-01-1-3.

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This paper presents a comparative study on speed control of brushless DC motors, which has wide applications in electrical vehicles, manufacturing plants, aerospace, etc. Initially, the proportional controller is implemented using the developed mathematical model of BLDC motor. Then, the PID and PII controllers are implemented with speed as their returning path to increase the performance of speed control. The optimum values of PID and PII parameters are evaluated using performance index-based constrained optimization. The integral square error is used as a performance index to form the object
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Jeyashanthi, J., and J. Barsana Banu. "Performance Analysis of DTC-IM Drive Using Various Control Algorithms." In Futuristic Projects in Energy and Automation Sectors: A Brief Review of New Technologies Driving Sustainable Development. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815080537123010014.

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Direct Torque Control (DTC) is the dominant strategy used in three-phase induction motor control, thanks to its excellent and vibrant characteristics, consistent operation, fewer mathematical calculations, and rigidity in adjustable velocity drives. However, torque ripple is the main drawback of DTC, and it is challenging to reduce it. While DTC based conventional PID controller is utilized, it gets pretentious by lengthy settling time, maximum peak overshoot, and torque and speed curve oscillations. The current research aims to diminish the torque ripple and augment the DTC-enabled induction
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Conference papers on the topic "BLDC Motor; speed control; neural network controller"

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Mamadapur, Archana, and G. Unde Mahadev. "Speed Control of BLDC Motor Using Neural Network Controller and PID Controller." In 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). IEEE, 2019. http://dx.doi.org/10.1109/icpedc47771.2019.9036695.

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Wongkhead, Sompod. "State Space Model for Speed Control BLDC Motor Tuning by Combination of PI - Artificial Neural Network Controller." In 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2021. http://dx.doi.org/10.1109/ecti-con51831.2021.9454756.

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Saini, Sandeep, Jorge Hernandez, and Sameer Nayak. "Auto-Tuning PID Controller on Electromechanical Actuators Using Machine Learning." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0435.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The performance of an electromechanical actuator largely depends on the control strategy implemented and calibrated. The purpose of this paper is to analyze the shortcomings of existing methods and practices involved in the manual tuning of a Proportional-Integral-Derivative (PID) controller of a Brushless DC (BLDC) motor and therefore, to propose an improved auto-tuning method. The controller investigated is a 3-stage cascaded PID, where the outermost loop is a position-based control followed by speed and current loops,
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Arandhakar, Sairaj, Akshat Kant, and Muralidhar Nayak Bhukya. "Implementation of Convolutional Neural Network for Speed Control of BLDC Motor." In 2021 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2021. http://dx.doi.org/10.1109/icdi3c53598.2021.00016.

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Hamad, Ahmed Rifaat, Qassim A. Al-Jarwany, Bayan Mahdi Sabbar, Mohannad Jabbar Mnati, Adnan Hussein Ali, and Alex Van Den Bossche. "Speed Control of BLDC Motor: Design and Simulation Based on Neural Network." In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). IEEE, 2023. http://dx.doi.org/10.1109/eiceeai60672.2023.10590555.

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Kang Peng and Jin Zhao. "Speed control of induction motor using neural network sliding mode controller." In 2011 International Conference on Electric Information and Control Engineering (ICEICE). IEEE, 2011. http://dx.doi.org/10.1109/iceice.2011.5778343.

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M, Shanmugapriya, Suguna R, Senthil Kumar R, Sam Thomas S, Jayaprasath M, and Maichal Praveen R. "Cascaded Neural Network Controller for Speed Control in Idtc Based Switched Reluctance Motor." In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2023. http://dx.doi.org/10.1109/iccpct58313.2023.10245995.

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Naung, Ye, Schagin Anatolii, and Ye Htet Lin. "Speed Control of DC Motor by Using Neural Network Parameter Tuner for PI-controller." In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2019. http://dx.doi.org/10.1109/eiconrus.2019.8656911.

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Wasusatein, Wasu, Sukhumpat Nittayawan, and Waree Kongprawechnon. "Speed Control Under Load Uncertainty of Induction Motor Using Neural Network Auto-Tuning PID Controller." In 2018 11th International Conference on Embedded Systems and Intelligent Technology & 9th International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES). IEEE, 2018. http://dx.doi.org/10.1109/icesit-icictes.2018.8442062.

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Ishwarya, U., R. Srimathi, K. Nithishkumar, K. R. M. Vijaya Chandrakala, S. Saravanan, and V. K. Arun Shankar. "Optimum Speed Control of Permanent Magnet Synchronous Motor using Artificial Neural Network-Based Field-Oriented Controller." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574751.

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