An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines

This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to basel...

Full description

Saved in:
Bibliographic Details
Main Authors: Jie You, Wencen Wu, Yebin Wang
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Convergence
Induction motors
Observers
Rotors
Stators
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c223t-c0bef5ae799057c46080fdbfe05ca6451177fb8863c846da752dedddf92523ae3
cites
container_end_page 312
container_issue
container_start_page 307
container_title
container_volume
creator Jie You
Wencen Wu
Yebin Wang
description This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to baseline algorithm. To address the parameter variation problem, the Lyapunov redesign method is used to achieve an adaptation with respect to the parameter α. It is shown that the proposed observer can achieve guaranteed asymptotic stability and readily extend to the time-varying speed case. Advantages of the proposed observer include guaranteed asymptotic stability of estimation errors, parameter a adaptation, and better dynamic performance. Simulation results are presented to validate the proposed method.
doi_str_mv 10.23919/ACC.2018.8431006
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8431006</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8431006</ieee_id><sourcerecordid>8431006</sourcerecordid><originalsourceid>FETCH-LOGICAL-c223t-c0bef5ae799057c46080fdbfe05ca6451177fb8863c846da752dedddf92523ae3</originalsourceid><addsrcrecordid>eNotkMtOwzAURA0SEm3hAxAb_0DKtR2_llFUoFJQF6XryrGvIagkkZ1W4u-poKuZsxkdDSEPDJZcWGafqrpecmBmaUrBANQVmTMpjJIlN-aazLjQppBGsVsyz_kLgFmrYEZ2VU-r4MapOyFtjti3mD4w0U2bMZ3OJQ6JbkfEUGyxz0M6YM50lafu203d0NMh0nUfjv4P3pz_7HrMd-QmukPG-0suyO559V6_Fs3mZV1XTeE5F1PhocUoHWprQWpfKjAQQxsRpHeqlIxpHVtjlPCmVMFpyQOGEKLlkguHYkEe_3c7RNyP6SyVfvaXC8QvhgdRAA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines</title><source>IEEE Xplore All Conference Series</source><creator>Jie You ; Wencen Wu ; Yebin Wang</creator><creatorcontrib>Jie You ; Wencen Wu ; Yebin Wang</creatorcontrib><description>This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to baseline algorithm. To address the parameter variation problem, the Lyapunov redesign method is used to achieve an adaptation with respect to the parameter α. It is shown that the proposed observer can achieve guaranteed asymptotic stability and readily extend to the time-varying speed case. Advantages of the proposed observer include guaranteed asymptotic stability of estimation errors, parameter a adaptation, and better dynamic performance. Simulation results are presented to validate the proposed method.</description><identifier>EISSN: 2378-5861</identifier><identifier>EISBN: 1538654288</identifier><identifier>EISBN: 9781538654286</identifier><identifier>DOI: 10.23919/ACC.2018.8431006</identifier><language>eng</language><publisher>AACC</publisher><subject>Adaptation models ; Convergence ; Induction motors ; Observers ; Rotors ; Stators</subject><ispartof>2018 Annual American Control Conference (ACC), 2018, p.307-312</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c223t-c0bef5ae799057c46080fdbfe05ca6451177fb8863c846da752dedddf92523ae3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8431006$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8431006$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jie You</creatorcontrib><creatorcontrib>Wencen Wu</creatorcontrib><creatorcontrib>Yebin Wang</creatorcontrib><title>An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines</title><title>2018 Annual American Control Conference (ACC)</title><addtitle>ACC</addtitle><description>This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to baseline algorithm. To address the parameter variation problem, the Lyapunov redesign method is used to achieve an adaptation with respect to the parameter α. It is shown that the proposed observer can achieve guaranteed asymptotic stability and readily extend to the time-varying speed case. Advantages of the proposed observer include guaranteed asymptotic stability of estimation errors, parameter a adaptation, and better dynamic performance. Simulation results are presented to validate the proposed method.</description><subject>Adaptation models</subject><subject>Convergence</subject><subject>Induction motors</subject><subject>Observers</subject><subject>Rotors</subject><subject>Stators</subject><issn>2378-5861</issn><isbn>1538654288</isbn><isbn>9781538654286</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkMtOwzAURA0SEm3hAxAb_0DKtR2_llFUoFJQF6XryrGvIagkkZ1W4u-poKuZsxkdDSEPDJZcWGafqrpecmBmaUrBANQVmTMpjJIlN-aazLjQppBGsVsyz_kLgFmrYEZ2VU-r4MapOyFtjti3mD4w0U2bMZ3OJQ6JbkfEUGyxz0M6YM50lafu203d0NMh0nUfjv4P3pz_7HrMd-QmukPG-0suyO559V6_Fs3mZV1XTeE5F1PhocUoHWprQWpfKjAQQxsRpHeqlIxpHVtjlPCmVMFpyQOGEKLlkguHYkEe_3c7RNyP6SyVfvaXC8QvhgdRAA</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>Jie You</creator><creator>Wencen Wu</creator><creator>Yebin Wang</creator><general>AACC</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201806</creationdate><title>An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines</title><author>Jie You ; Wencen Wu ; Yebin Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-c0bef5ae799057c46080fdbfe05ca6451177fb8863c846da752dedddf92523ae3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptation models</topic><topic>Convergence</topic><topic>Induction motors</topic><topic>Observers</topic><topic>Rotors</topic><topic>Stators</topic><toplevel>online_resources</toplevel><creatorcontrib>Jie You</creatorcontrib><creatorcontrib>Wencen Wu</creatorcontrib><creatorcontrib>Yebin Wang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jie You</au><au>Wencen Wu</au><au>Yebin Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines</atitle><btitle>2018 Annual American Control Conference (ACC)</btitle><stitle>ACC</stitle><date>2018-06</date><risdate>2018</risdate><spage>307</spage><epage>312</epage><pages>307-312</pages><eissn>2378-5861</eissn><eisbn>1538654288</eisbn><eisbn>9781538654286</eisbn><abstract>This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to baseline algorithm. To address the parameter variation problem, the Lyapunov redesign method is used to achieve an adaptation with respect to the parameter α. It is shown that the proposed observer can achieve guaranteed asymptotic stability and readily extend to the time-varying speed case. Advantages of the proposed observer include guaranteed asymptotic stability of estimation errors, parameter a adaptation, and better dynamic performance. Simulation results are presented to validate the proposed method.</abstract><pub>AACC</pub><doi>10.23919/ACC.2018.8431006</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2378-5861
ispartof 2018 Annual American Control Conference (ACC), 2018, p.307-312
issn 2378-5861
language eng
recordid cdi_ieee_primary_8431006
source IEEE Xplore All Conference Series
subjects Adaptation models
Convergence
Induction motors
Observers
Rotors
Stators
title An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T12%3A54%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20Adaptive%20Luenberger%20Observer%20for%20Speed-Sensorless%20Estimation%20of%20Induction%20Machines&rft.btitle=2018%20Annual%20American%20Control%20Conference%20(ACC)&rft.au=Jie%20You&rft.date=2018-06&rft.spage=307&rft.epage=312&rft.pages=307-312&rft.eissn=2378-5861&rft_id=info:doi/10.23919/ACC.2018.8431006&rft.eisbn=1538654288&rft.eisbn_list=9781538654286&rft_dat=%3Cieee_CHZPO%3E8431006%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c223t-c0bef5ae799057c46080fdbfe05ca6451177fb8863c846da752dedddf92523ae3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8431006&rfr_iscdi=true