Data-Enabled Finite State Predictive Control for Power Converters via Adaline Neural Network

Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schem...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on industrial electronics (1982) Vol. 72; no. 3; pp. 2244 - 2253
Main Authors Wu, Wenjie, Qiu, Lin, Liu, Xing, Ma, Jien, Rodriguez, Jose, Fang, Youtong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schemes in recent decades. This article, at hand, presents a data-enabled finite set predictive control solution subject to model dependence issues from the dynamic modeling point of view. In this regard, a dynamic-linearization data model is utilized to equivalently reformulate the governed power converter at each operation point. In pursuit of the accurate modeling of the plant, the time-varying parameters of the data model are updated online by an adaptive linear neural network, rendering a favorable influence on implementation. Additionally, an improved capacitance-less voltage balancing method is proposed to regulate the neutral point potential. Since the parameterless prediction process for both load currents and capacitor voltage relies solely on measured and historical input-output data of the plant, the destructive effect of parameter variations can be circumvented. To evaluate the correctness of the proposed solution, the comparative simulation and experimentation with the conventional method and state-of-the-art solutions are examined on a classic three-level neutral-point-clamped inverter.
AbstractList Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schemes in recent decades. This article, at hand, presents a data-enabled finite set predictive control solution subject to model dependence issues from the dynamic modeling point of view. In this regard, a dynamic-linearization data model is utilized to equivalently reformulate the governed power converter at each operation point. In pursuit of the accurate modeling of the plant, the time-varying parameters of the data model are updated online by an adaptive linear neural network, rendering a favorable influence on implementation. Additionally, an improved capacitance-less voltage balancing method is proposed to regulate the neutral point potential. Since the parameterless prediction process for both load currents and capacitor voltage relies solely on measured and historical input–output data of the plant, the destructive effect of parameter variations can be circumvented. To evaluate the correctness of the proposed solution, the comparative simulation and experimentation with the conventional method and state-of-the-art solutions are examined on a classic three-level neutral-point-clamped inverter.
Author Qiu, Lin
Liu, Xing
Ma, Jien
Fang, Youtong
Rodriguez, Jose
Wu, Wenjie
Author_xml – sequence: 1
  givenname: Wenjie
  orcidid: 0000-0001-9173-7099
  surname: Wu
  fullname: Wu, Wenjie
  email: wuwenjie@zju.edu.cn
  organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China
– sequence: 2
  givenname: Lin
  orcidid: 0000-0003-1236-2191
  surname: Qiu
  fullname: Qiu, Lin
  email: qiu_lin@zju.edu.cn
  organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China
– sequence: 3
  givenname: Xing
  orcidid: 0000-0001-9685-2862
  surname: Liu
  fullname: Liu, Xing
  email: xingldl@zju.edu.cn
  organization: College of Electrical Engineering, Shanghai Dianji University, Shanghai, China
– sequence: 4
  givenname: Jien
  orcidid: 0000-0001-6970-3634
  surname: Ma
  fullname: Ma, Jien
  email: majien@zju.edu.cn
  organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China
– sequence: 5
  givenname: Jose
  orcidid: 0000-0002-1410-4121
  surname: Rodriguez
  fullname: Rodriguez, Jose
  email: jose.rodriguezp@uss.cl
  organization: Faculty of Engineering, Universidad San Sebastian Santiago, Santiago, Chile
– sequence: 6
  givenname: Youtong
  orcidid: 0000-0002-8521-4184
  surname: Fang
  fullname: Fang, Youtong
  email: youtong@zju.edu.cn
  organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China
BookMark eNp9kM9LwzAUx4NMcE7vHjwEPHfmV9PmOOamg6ED500IafoKmbWZabbhf2_LdhAPXt4XHt_Pe_C5RIPGN4DQDSVjSom6Xy9mY0aYGHNBec6zMzSkaZolSol8gIaEZXlCiJAX6LJtN4RQkdJ0iN4fTDTJrDFFDSWeu8ZFwK_RdHMVoHQ2uj3gqW9i8DWufMArf4DQb_YQIoQW753Bk9LUrgH8DLtg6i7iwYePK3RembqF61OO0Nt8tp4-JcuXx8V0skwsYywmpmKkqMBIQ0QuVF7kwDmk1KaqNFYxTjOQqhSkLBXPAKqisKrKrLQlybiVfITujne3wX_toI1643eh6V5qTmUuJBWEdy1ybNng2zZApbfBfZrwrSnRvUPdOdS9Q31y2CHyD2Jd58b1Ooyr_wNvj6ADgF9_pOCcp_wHUbyBAw
CODEN ITIED6
CitedBy_id crossref_primary_10_1007_s43236_024_00975_2
crossref_primary_10_3390_electronics13244969
Cites_doi 10.1109/TIE.2022.3222629
10.1109/TPEL.2020.3024227
10.1109/TPEL.2022.3142244
10.1109/TPEL.2020.3006779
10.1109/JESTPE.2022.3192064
10.1109/TIE.2022.3159951
10.1109/TIE.2022.3183354
10.1109/TIE.2023.3274861
10.1109/TPEL.2021.3121532
10.1109/TIE.2016.2636126
10.1109/PRECEDE57319.2023.10174599
10.1109/TII.2021.3060469
10.1109/TIE.2023.3303646
10.1109/TIE.2016.2527629
10.1109/TIE.2020.2977511
10.1109/TIE.2021.3095816
10.1109/TIE.2016.2515072
10.1109/TIE.2022.3176288
10.1109/TIE.2023.3303626
10.1109/TPEL.2016.2630274
10.1109/JESTPE.2018.2880137
10.1109/TIE.2017.2760840
10.1109/TPEL.2019.2954357
10.1109/TIE.2020.3036214
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID 97E
RIA
RIE
AAYXX
CITATION
7SP
8FD
L7M
DOI 10.1109/TIE.2024.3413837
DatabaseName IEEE Xplore (IEEE)
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore
CrossRef
Electronics & Communications Abstracts
Technology Research Database
Advanced Technologies Database with Aerospace
DatabaseTitle CrossRef
Technology Research Database
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList Technology Research Database

Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1557-9948
EndPage 2253
ExternalDocumentID 10_1109_TIE_2024_3413837
10643335
Genre orig-research
GrantInformation_xml – fundername: State Key Laboratory of High-speed Maglev Transportation Technology
  grantid: SKLM-SFCF-2023-020
– fundername: Agencia Nacional de Investigación y Desarrollo; ANID
  grantid: FB0008; 1210208; 1221293
  funderid: 10.13039/501100020884
– fundername: National Natural Science Foundation of China
  grantid: 52293424
  funderid: 10.13039/501100001809
– fundername: Key R & D Plan Projects in Zhejiang Province
  grantid: 2023C01243
GroupedDBID -~X
.DC
0R~
29I
4.4
5GY
5VS
6IK
97E
9M8
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACKIV
ACNCT
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
HZ~
H~9
IBMZZ
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
RIA
RIE
RNS
TAE
TN5
TWZ
VH1
VJK
AAYXX
CITATION
RIG
7SP
8FD
L7M
ID FETCH-LOGICAL-c222t-af20bfea6a048498b8e33e51c59dac92317e69d40dd937eefbbc9f7c6cd073c63
IEDL.DBID RIE
ISSN 0278-0046
IngestDate Tue Jul 22 18:41:51 EDT 2025
Tue Jul 01 05:40:48 EDT 2025
Thu Apr 24 23:08:32 EDT 2025
Wed Aug 27 01:50:32 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 3
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c222t-af20bfea6a048498b8e33e51c59dac92317e69d40dd937eefbbc9f7c6cd073c63
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-8521-4184
0000-0001-9173-7099
0000-0001-9685-2862
0000-0001-6970-3634
0000-0003-1236-2191
0000-0002-1410-4121
PQID 3168461403
PQPubID 85464
PageCount 10
ParticipantIDs crossref_primary_10_1109_TIE_2024_3413837
crossref_citationtrail_10_1109_TIE_2024_3413837
proquest_journals_3168461403
ieee_primary_10643335
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2025-03-01
PublicationDateYYYYMMDD 2025-03-01
PublicationDate_xml – month: 03
  year: 2025
  text: 2025-03-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE transactions on industrial electronics (1982)
PublicationTitleAbbrev TIE
PublicationYear 2025
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref24
ref12
ref23
ref15
ref14
ref20
ref11
ref22
ref10
ref21
ref2
ref1
ref17
ref16
ref19
ref18
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref8
  doi: 10.1109/TIE.2022.3222629
– ident: ref11
  doi: 10.1109/TPEL.2020.3024227
– ident: ref18
  doi: 10.1109/TPEL.2022.3142244
– ident: ref19
  doi: 10.1109/TPEL.2020.3006779
– ident: ref15
  doi: 10.1109/JESTPE.2022.3192064
– ident: ref14
  doi: 10.1109/TIE.2022.3159951
– ident: ref5
  doi: 10.1109/TIE.2022.3183354
– ident: ref21
  doi: 10.1109/TIE.2023.3274861
– ident: ref2
  doi: 10.1109/TPEL.2021.3121532
– ident: ref24
  doi: 10.1109/TIE.2016.2636126
– ident: ref20
  doi: 10.1109/PRECEDE57319.2023.10174599
– ident: ref10
  doi: 10.1109/TII.2021.3060469
– ident: ref7
  doi: 10.1109/TIE.2023.3303646
– ident: ref13
  doi: 10.1109/TIE.2016.2527629
– ident: ref23
  doi: 10.1109/TIE.2020.2977511
– ident: ref17
  doi: 10.1109/TIE.2021.3095816
– ident: ref6
  doi: 10.1109/TIE.2016.2515072
– ident: ref9
  doi: 10.1109/TIE.2022.3176288
– ident: ref3
  doi: 10.1109/TIE.2023.3303626
– ident: ref12
  doi: 10.1109/TPEL.2016.2630274
– ident: ref4
  doi: 10.1109/JESTPE.2018.2880137
– ident: ref22
  doi: 10.1109/TIE.2017.2760840
– ident: ref1
  doi: 10.1109/TPEL.2019.2954357
– ident: ref16
  doi: 10.1109/TIE.2020.3036214
SSID ssj0014515
Score 2.4893646
Snippet Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 2244
SubjectTerms Adaline
Adaptation models
Capacitors
Data models
Dynamic models
dynamic-linearization
Electric potential
Inverters
model predictive control (MPC)
Neural networks
Parameters
Power converters
Predictive control
Predictive models
robustness
Simulation
Switches
Voltage
Voltage control
Title Data-Enabled Finite State Predictive Control for Power Converters via Adaline Neural Network
URI https://ieeexplore.ieee.org/document/10643335
https://www.proquest.com/docview/3168461403
Volume 72
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEF60Jz34rFitsgcvHtLGZDePY6ktVbD00EIPQtjHBIrSSk09-Oud2aSlKIq3EHbJsrM7803m8TF2o4S1OUDiSamFhxY_8FIVgRfrXCZJqC2EVCj8NIwGE_E4ldOqWN3VwgCASz6DFj26WL5dmBX9KsMbjvYzDOUu20XPrSzW2oQMhCzpCgJqGYte3zom6aft8UMPPcFAtEhlJ0R5vmWDHKnKD03szEv_kA3XCyuzSl5aq0K3zOe3no3_XvkRO6iAJu-UJ-OY7cD8hO1vtR88Zc_3qlBez1VPWd6fEfzkDn3y0ZICOKQKebdMZueIbvmIONXoDbE4I3DkHzPFO1YRVuXU5wO_OCwTy-ts0u-NuwOvYlvwDGKEwlN54OscVKTwUos00QmEIcg7I1OrDOHAGKLUCt9ahDQAudYmzWMTGYtqwkThGavNF3M4Z1z6oNLAB4knAeGYUBAFsQEbC6GovX6Dtdf7n5mqFTkxYrxmziXx0wwllpHEskpiDXa7mfFWtuH4Y2ydBLA1rtz7BmuuZZxVF_U9I94uEVHTwotfpl2yvYA4f13eWZPViuUKrhCIFPraHcAvoijZsA
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEB58HNSDb3F95uDFQ9faJn0cRXdZX4uHFTwIJY8piLKKdj34651Ju7IoirdSEhoySeZLZ-b7AA60dK5EzAKljAzI40dBrhMMUlOqLIuNw5gLha_7Se9WXtypu6ZY3dfCIKJPPsM2P_pYvnu2I_5VRjuc_Gccq2mYJcevjutyra-ggVS1YEHEpLF07xtHJcP8aHDeobtgJNt8aGcsej7hhbysyo-z2DuY7hL0x0Or80oe26PKtO3HN9bGf499GRYbqClO6rWxAlM4XIWFCQLCNbg_05UOOr5-yonuAwNQ4fGnuHnlEA4fhuK0TmcXhG_FDauq8RvWcSboKN4ftDhxmtGqYKYP-mK_Ti1fh9tuZ3DaCxq9hcASSqgCXUahKVEnmra1zDOTYRyjOrYqd9oyEkwxyZ0MnSNQg1gaY_MytYl1dFDYJN6AmeHzEDdBqBB1HoWoaC0QIJMakyi16FIpNRPst-BoPP-FbcjIWRPjqfCXkjAvyGIFW6xoLNaCw68eLzURxx9t19kAE-3quW_BztjGRbNV3wpW7pIJ0xZu_dJtH-Z6g-ur4uq8f7kN8xErAPsstB2YqV5HuEuwpDJ7fjF-Aif13Pk
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data-Enabled+Finite+State+Predictive+Control+for+Power+Converters+via+Adaline+Neural+Network&rft.jtitle=IEEE+transactions+on+industrial+electronics+%281982%29&rft.au=Wu%2C+Wenjie&rft.au=Qiu%2C+Lin&rft.au=Liu%2C+Xing&rft.au=Ma%2C+Jien&rft.date=2025-03-01&rft.issn=0278-0046&rft.eissn=1557-9948&rft.volume=72&rft.issue=3&rft.spage=2244&rft.epage=2253&rft_id=info:doi/10.1109%2FTIE.2024.3413837&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TIE_2024_3413837
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0046&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0046&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0046&client=summon