Recursive least squares method in parameters identification of DC motors models

Krneta, Radojka; Antić, Sanja; Stojanović, Danilo

doi:10.2298/fuee0503467k

Cited by 40 publications

(18 citation statements)

References 1 publication

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“…The validity of the proposed method was shown by simulation an experiments. By comparing a graphic of real motor speed and a graphics of speed of investigated models in a Z and S domains it can be concluded that a satisfying quality of DC motor parameters identification has been achieved [13].…”

Section: Literature Reviewmentioning

confidence: 94%

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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Section: Literature Reviewmentioning

confidence: 94%

Parameters Identification of a Permanent Magnet DC Motor

Salah¹,

Abdelati²

2010

Modelling, Identification, and Control

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“…In order to show the performance comparison of the constrained estimators discussed above, we consider an example of a DC motor (adopted in [9] and [10]) whose dynamic behavior can be described using the two following equations:…”

Section: Numerical Simulationsmentioning

confidence: 99%

Sliding window identification with linear-equality constraints

Thabet

Ayadi

Rotella

2015

2015 IEEE 12th International Multi-Conference on Systems, Signals &Amp; Devices (SSD15)

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“…According to the researches overviewed, motor parameters have been identified by different methods. For example, the authors in [3,4] used the RLS method to identify the DC motor parameters. G. Mamani et al proposed an online algebraic DC motor parameter estimation in [5].…”

Section: Introductionmentioning

confidence: 99%

Permanent magnet DC motor (PMDC) model identification and controller design

Alkamachi

2019

Journal of Electrical Engineering

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System modeling is a set of mathematical equations that describe the dynamical behavior of a system. It is considered as a primary concern in determining a suitable controller to meet specific requirements. An autoregressive with exogenous terms (ARX) model for a PMDC motor is identified experimentally based on the recursive least square (RLS) method. Adaptive discrete pole placement controller (APPC) is proposed and designed aiming to control the motor revolving speed. For the comparison purpose, a discrete Proportional Integral (PI) controller is also considered in this work. The steady step response, transient response, and the mean squared error (MSE) is counted throughout the comparison. The e ect of the uncertainties in the PMDC model is also investigated in this paper. The result shows a superiority in the performance of the proposed controller compared to that obtained using PI controller.

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Recursive least squares method in parameters identification of DC motors models

Cited by 40 publications

References 1 publication

Parameters Identification of a Permanent Magnet DC Motor

Parameters Identification of a Permanent Magnet DC Motor

Sliding window identification with linear-equality constraints

Permanent magnet DC motor (PMDC) model identification and controller design

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