Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

In this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanica...

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Main Authors: Rakan K. Antar, Ahmed A. Allu
Format: Article
Language:English
Published: Tikrit University 2016-09-01
Series:Tikrit Journal of Engineering Sciences
Subjects:
Online Access:http://tj-es.com/vol23no3pa6/
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author Rakan K. Antar
Ahmed A. Allu
author_facet Rakan K. Antar
Ahmed A. Allu
author_sort Rakan K. Antar
collection DOAJ
description In this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS) method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm) and mechanical torque (±7 N.m) in steady state and dynamic conditions. The simulation results illustrate the effectiveness of the proposed controller without speed or torque sensors.
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spelling doaj.art-6a84b00ca1b347af94f3cfac051928102023-09-02T18:06:29ZengTikrit UniversityTikrit Journal of Engineering Sciences1813-162X2016-09-012335562Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network TechniqueRakan K. Antar0Ahmed A. Allu1Technical College, Mosul , IraqTechnical Institute, Mosul, IraqIn this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS) method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm) and mechanical torque (±7 N.m) in steady state and dynamic conditions. The simulation results illustrate the effectiveness of the proposed controller without speed or torque sensors.http://tj-es.com/vol23no3pa6/Speed/torque controlSensorless controlDC machine
spellingShingle Rakan K. Antar
Ahmed A. Allu
Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
Tikrit Journal of Engineering Sciences
Speed/torque control
Sensorless control
DC machine
title Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
title_full Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
title_fullStr Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
title_full_unstemmed Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
title_short Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique
title_sort sensorless speed torque control of dc machine using artificial neural network technique
topic Speed/torque control
Sensorless control
DC machine
url http://tj-es.com/vol23no3pa6/
work_keys_str_mv AT rakankantar sensorlessspeedtorquecontrolofdcmachineusingartificialneuralnetworktechnique
AT ahmedaallu sensorlessspeedtorquecontrolofdcmachineusingartificialneuralnetworktechnique