Identification and control of a DC motor using back-propagation neural networks

Weerasooriya, S.; El-Sharkawi, M.A.

doi:10.1109/60.103639

Cited by 207 publications

(69 citation statements)

References 7 publications

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“…It has been found to be an effective system for learning discriminants, for patterns from a body of examples. MLP is used as the basic structure for a bunch of applications [4], [12], [17], [18], [32], [46], [56], [85], [94], [119], [127]. The Hopfield network can be used to identify problems of linear time-varying or time-invariant systems [28].…”

Section: A Modeling and Identificationmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

384

136

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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Section: A Modeling and Identificationmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

384

136

View full text Add to dashboard Cite

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“…The unknown nonlinear dynamics of the motor and the load are captured by an artificial neural network. Performance of the identification and control algorithms are evaluated by simulating them on a typical dc motor model [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Parameters Identification of a Permanent Magnet DC Motor

Salah¹,

Abdelati²

2010

Modelling, Identification, and Control

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In this thesis, driver circuits to drive DC motor are designed and built. We take in our design the safety for any damage, the isolation from any interference and the capability to provide the sufficient current needed to drive the motor. Also, methods and algorithms used for parameters identification are addressed. Data acquisition module form National Instruments is a suitable module used for acquiring signals since it's compatible with MATLAB and LABVIEW. To identify the parameters of the motor, an experimental measurement of armature voltage, armature current and rotor speed are performed using the NIDAQ USB-6008 data acquisition module. Trials are performed to apply some of the methods on the motor practically to identify the motor parameters. DAQ toolbox in MATLAB/Simulink is used to acquire the test signals and perform analysis based on the nonlinear least square method or pattern search method which they are suitable. Finally, we designed a GUI to be user friendly and to automate all the process of identification, in addition to the feature for undergraduate and graduate students to apply experiments on control and machine labs. The extracted motor parameters from simulation produced consistent simulation results with experimental data. The proposed approaches are compared and both are proved to be simple, fast and accurate.ii : ‫ﺔ‬ ‫ﺑﺎﻟﻌﺮﺑ‬ ‫اﻟﺒﺤﺚ‬ ‫ﻋﻨﻮان‬

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“…A first solution for this is to dynamically estimate the parameters of the model [7][8][9], but this generates a complex model that is usually nonlinear. A second solution is to use a nonlinear model of the brushed dc motor [10][11][12], or a technique that indirectly models the motor, such as Neural Networks [13,14] and the Kalman filter [15,16]. The problem with these solutions is that they have a high computational cost, and the estimation of the parameters used is not an easy task.…”

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confidence: 99%

A Novel Method for Sensorless Speed Detection of Brushed DC Motors

2016

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Many motor applications require accurate speed measurement. For brushed dc motors, speed can be measured with conventional observers or sensorless observers. Sensorless observers have the advantage of not requiring any external devices to be attached to the motor. Instead, voltage and/or current are measured and used to estimate the speed. The sensorless observers are usually divided into two groups: those based on the dynamic model, and those based on the ripple component. This paper proposes a method that measures the current of brushed dc motors and analyses the position of its spectral components. From these spectral components, the method estimates the motor speed. Three tests, performed each with the speeds ranging from 2000 to 3000 rpm either at constant-speed, at slowly changing speeds, or at rapidly changing speeds, showed that the average error was below 1 rpm and that the deviation error was below 1.5 rpm. The proposed method: (i) is a novel method that is not based on either the dynamic model or on the ripple component; (ii) requires only the measurement of the current for the speed estimation; (iii) can be used for brushed dc (direct current) motors with a large number of coils; and (iv) achieves a low error in the speed estimation.

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Identification and control of a DC motor using back-propagation neural networks

Cited by 207 publications

References 7 publications

A comprehensive review for industrial applicability of artificial neural networks

A comprehensive review for industrial applicability of artificial neural networks

Parameters Identification of a Permanent Magnet DC Motor

A Novel Method for Sensorless Speed Detection of Brushed DC Motors

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