2018
DOI: 10.11591/ijeecs.v9.i3.pp696-702
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Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Abstract: <p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Me… Show more

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Cited by 9 publications
(13 citation statements)
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“…Taking into account a large number of works devoted to development of a model of pulsating current traction electric motor demanded models are those that take into account nonlinearity of magnetization characteristics and occurrence of eddy currents and magnetic losses in the motor magnetic circuit. This can be confirmed by works on approximation of magnetization characteristic [1,2] and simulation of pulsating current traction electric motors of pulsating current taking into consideration influence of eddy currents on the nature of the magnetization curve [3]. Simulation models obtained in the result of applying these methods give an opportunity to determine with high accuracy such traction motor parameters as motor shaft speed at any load, currents flowing in traction motor circuits, electromotive force (EMF) generated by the motor, but taking into account all power losses in traction motor.…”
mentioning
confidence: 56%
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“…Taking into account a large number of works devoted to development of a model of pulsating current traction electric motor demanded models are those that take into account nonlinearity of magnetization characteristics and occurrence of eddy currents and magnetic losses in the motor magnetic circuit. This can be confirmed by works on approximation of magnetization characteristic [1,2] and simulation of pulsating current traction electric motors of pulsating current taking into consideration influence of eddy currents on the nature of the magnetization curve [3]. Simulation models obtained in the result of applying these methods give an opportunity to determine with high accuracy such traction motor parameters as motor shaft speed at any load, currents flowing in traction motor circuits, electromotive force (EMF) generated by the motor, but taking into account all power losses in traction motor.…”
mentioning
confidence: 56%
“…When modeling a direct current traction motor (DCM) of series excitation the main problem is to reproduce the dependence of magnetic flux on currents of field winding and armature currents; and this implies presence of load characteristics. In the work by [1] the authors propose to use universal magnetization curve for DCM of series excitation, and on the basis of universal magnetic characteristic to calculate universal expressions for the intrusive parameters of direct current traction electric machines and those of pulsating current traction motors. Since the universal magnetic characteristic is built for machines working under load, in order to reduce the error of approximation the author proposed to approximate the universal magnetic characteristic by means of two functions: magnetomotive force of the field winding and magnetomotive force of the armature reaction.…”
mentioning
confidence: 99%
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“…In many areas of industrial control, it is hard to get the ideal value using the ZN method. The Nelder-Mead (NM) simplex algorithm [4] is intended to solve the traditional unrestricted optimization issues to reduce a provided nonlinear function such as PID tuning.…”
Section: Introductionmentioning
confidence: 99%
“…Grey Wolf Optimization [21] and Bio -Inspired Optimization Algorithm [22] is used for parameter estimation of PMDC coreless micro-motor. The dc motor parameter is evaluated accurately by the recently published Flower Pollination Algorithm (FPA) [23] and Nelder -Mead Optimisation [24].…”
Section: Introductionmentioning
confidence: 99%