Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation

This paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of...

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Main Authors: Łuczak Dominik, Wójcik Adrian
Format: Article
Language:English
Published: Sciendo 2018-12-01
Series:Power Electronics and Drives
Subjects:
Online Access:http://www.degruyter.com/view/j/pead.2018.3.issue-1/pead-2018-0017/pead-2018-0017.xml?format=INT
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author Łuczak Dominik
Wójcik Adrian
author_facet Łuczak Dominik
Wójcik Adrian
author_sort Łuczak Dominik
collection DOAJ
description This paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of the research was to find a set of neural network structures giving useful and repeatable results of the estimation. The mechanical resonance frequency of the system has been adopted at the level of 9.3-10.3 Hz. The selected state variables of the mechanical system are load, speed and stiffness torque of the shaft.
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spelling doaj.art-5060527fadbb497fb12f28f5ab6d6d002022-12-22T01:40:36ZengSciendoPower Electronics and Drives2451-02622543-42922018-12-013120521610.2478/pead-2018-0017pead-2018-0017Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State EstimationŁuczak Dominik0Wójcik Adrian1Poznan University of Technology, Institute of Control, Robotics and Information Engineering, M. Skłodowska-Curie SquarePoznan, PolandPoznan University of Technology, Institute of Control, Robotics and Information Engineering, M. Skłodowska-Curie SquarePoznan, PolandThis paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of the research was to find a set of neural network structures giving useful and repeatable results of the estimation. The mechanical resonance frequency of the system has been adopted at the level of 9.3-10.3 Hz. The selected state variables of the mechanical system are load, speed and stiffness torque of the shaft.http://www.degruyter.com/view/j/pead.2018.3.issue-1/pead-2018-0017/pead-2018-0017.xml?format=INTtwo-mass driveneural networksimulation studiesnon-linear state estimation
spellingShingle Łuczak Dominik
Wójcik Adrian
Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
Power Electronics and Drives
two-mass drive
neural network
simulation studies
non-linear state estimation
title Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
title_full Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
title_fullStr Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
title_full_unstemmed Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
title_short Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
title_sort neural state estimator for complex mechanical part of electrical drive neural network size and performance of state estimation
topic two-mass drive
neural network
simulation studies
non-linear state estimation
url http://www.degruyter.com/view/j/pead.2018.3.issue-1/pead-2018-0017/pead-2018-0017.xml?format=INT
work_keys_str_mv AT łuczakdominik neuralstateestimatorforcomplexmechanicalpartofelectricaldriveneuralnetworksizeandperformanceofstateestimation
AT wojcikadrian neuralstateestimatorforcomplexmechanicalpartofelectricaldriveneuralnetworksizeandperformanceofstateestimation