Dimension reduction based adaptive dynamic programming for optimal control of discrete-time nonlinear control-affine systems

Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of memory is needed and the associated computational cost is high. Here, a search space...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of control Ročník 96; číslo 11; s. 2799 - 2811
Hlavní autori: Li, Qiang, Xu, Yunjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.11.2023
Taylor & Francis Ltd
Predmet:
Adaptive control
adaptive dynamic programming
Algorithms
Closed loops
Computational efficiency
Computing costs
Control theory
control-affine system
Discrete time systems
Dynamic programming
Feedback control
Inverse dynamics
Iterative methods
Nonlinear constrained optimal control
Nonlinear control
Nonlinear systems
Optimal control
Optimization
real-time control
uncertainty
ISSN:0020-7179, 1366-5820
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of memory is needed and the associated computational cost is high. Here, a search space dimension reduction strategy is proposed for a class of nonlinear discrete-time systems that are control-affine and invertible. Specifically, a bio-inspired motion rule is combined with inverse dynamics to reduce the value iteration search space to one dimension. The corresponding suboptimal control algorithm is developed and its optimality is analysed. An adaptation rule is developed to estimate uncertainties and improve the base policy. The closed-loop system is proven to be asymptotically stable. The advantages of the algorithm including much smaller computational cost and significantly reduced memory usage are demonstrated with two simulation examples.
AbstractList Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of memory is needed and the associated computational cost is high. Here, a search space dimension reduction strategy is proposed for a class of nonlinear discrete-time systems that are control-affine and invertible. Specifically, a bio-inspired motion rule is combined with inverse dynamics to reduce the value iteration search space to one dimension. The corresponding suboptimal control algorithm is developed and its optimality is analysed. An adaptation rule is developed to estimate uncertainties and improve the base policy. The closed-loop system is proven to be asymptotically stable. The advantages of the algorithm including much smaller computational cost and significantly reduced memory usage are demonstrated with two simulation examples.
Author Li, Qiang
Xu, Yunjun
Author_xml – sequence: 1
  givenname: Qiang
  surname: Li
  fullname: Li, Qiang
  organization: University of Central Florida
– sequence: 2
  givenname: Yunjun
  surname: Xu
  fullname: Xu, Yunjun
  email: yunjun.xu@ucf.edu
  organization: University of Central Florida
BookMark eNqFkE2LFDEQhoOs4OzqTxACnnusJNPdabwo69fCghc9h5qksmTpTsYkowz4400zuxcP7ilF6nkrqeeSXcQUibHXArYCNLwFkDCKcdpKkHIrhVA7pZ-xjVDD0PVawgXbrEy3Qi_YZSn3AEL1WmzYn49hoVhCijyTO9q6Vnss5Dg6PNTwi7g7RVyC5Yec7jIuS4h33KfMU2svOHObYs1p5slzF4rNVKlrHeLtn3OIhPkR6dD7dsHLqVRaykv23ONc6NXDecV-fP70_fprd_vty831h9vOKqVrJwCH3gFMEyqxH9SAXiM5v7eod4jj3k4K7ORIAXkvhetHJxUBjV73g9upK_bmPLdt8PNIpZr7dMyxPWmkHkErUL1q1LszZXMqJZM3NlRchdSMYTYCzKrbPOo2q27zoLul-3_Sh9zs5NOTuffnXIjN6YK_U56dqXiaU_YZow3FqP-P-AsLgZwc
CitedBy_id crossref_primary_10_1177_00368504241308957
Cites_doi 10.1090/qam/1944-02-03
10.1137/1.9780898718577
10.1002/9780470182963
10.1109/MRA.2012.2206474
10.1080/00207170903171314
10.1098/rspb.1995.0004
10.1016/j.automatica.2012.05.017
10.1109/CDC.1988.194354
10.1023/A:1009635226865
10.1080/00207179.2016.1266514
10.1115/1.4028849
10.1007/978-3-642-57760-4
10.1016/j.automatica.2018.10.038
10.1109/TNN.72
10.2514/3.21634
10.1073/pnas.40.4.231
10.1016/0893-6080(94)00053-O
10.1007/BF00992698
10.1007/BF00115009
10.23919/ACC45564.2020.9147911
10.1007/BFb0036070
10.1016/S0167-6911(97)00116-3
10.1109/TSMC.1983.6313077
10.1080/00207179.2013.790562
10.1515/9781400874668
10.1017/S0305004100030401
10.1016/S0893-9659(02)00148-9
10.1109/IJCNN.1989.118583
10.1007/978-1-4615-3618-5_3
10.1007/BFb0006203
10.1109/TCYB.2016.2586082
10.1016/0005-1098(87)90087-2
10.1109/JRA.1987.1087072
10.1201/9781439821091
10.1016/j.sysconle.2004.08.007
10.1137/1015071
10.1016/0893-6080(90)90088-3
10.1016/j.sysconle.2010.08.013
10.1109/9.262032
10.1016/j.conengprac.2019.104222
10.1007/978-1-4419-7997-1_8
10.1109/TCYB.2018.2821369
ContentType Journal Article
Copyright 2022 Informa UK Limited, trading as Taylor & Francis Group 2022
2022 Informa UK Limited, trading as Taylor & Francis Group
Copyright_xml – notice: 2022 Informa UK Limited, trading as Taylor & Francis Group 2022
– notice: 2022 Informa UK Limited, trading as Taylor & Francis Group
DBID AAYXX
CITATION
7SC
7SP
7TB
8FD
FR3
JQ2
L7M
L~C
L~D
DOI 10.1080/00207179.2022.2113438
DatabaseName CrossRef
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts Professional
DatabaseTitleList
Technology Research Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1366-5820
EndPage 2811
ExternalDocumentID 10_1080_00207179_2022_2113438
2113438
Genre Research Article
GroupedDBID -~X
.7F
.QJ
0BK
0R~
29J
2DF
30N
4.4
5GY
5VS
AAENE
AAGDL
AAHIA
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABCCY
ABFIM
ABHAV
ABJNI
ABLIJ
ABPAQ
ABPEM
ABTAI
ABXUL
ABXYU
ACBEA
ACGEJ
ACGFO
ACGFS
ACIWK
ACTIO
ADCVX
ADGTB
ADXPE
AEISY
AENEX
AEOZL
AEPSL
AEYOC
AFKVX
AFRVT
AGDLA
AGMYJ
AHDZW
AIJEM
AIYEW
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AMVHM
AQRUH
AQTUD
AVBZW
AWYRJ
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
DU5
EBS
E~A
E~B
F5P
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
KYCEM
LJTGL
M4Z
NA5
NX~
O9-
P2P
RIG
RNANH
ROSJB
RTWRZ
S-T
SNACF
TASJS
TBQAZ
TDBHL
TEN
TFL
TFT
TFW
TN5
TNC
TTHFI
TUROJ
TWF
UT5
UU3
ZGOLN
~S~
AAYXX
CITATION
7SC
7SP
7TB
8FD
FR3
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c338t-10a65d0099a31b636af8aedfbca84aa7bc930c9de30eff21d57d23e0e7f856d43
IEDL.DBID TFW
ISICitedReferencesCount 3
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000846699900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0020-7179
IngestDate Sun Nov 09 08:40:31 EST 2025
Sat Nov 29 05:46:18 EST 2025
Tue Nov 18 22:11:35 EST 2025
Mon Oct 20 23:45:29 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 11
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c338t-10a65d0099a31b636af8aedfbca84aa7bc930c9de30eff21d57d23e0e7f856d43
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
PQID 2870830353
PQPubID 176145
PageCount 13
ParticipantIDs crossref_citationtrail_10_1080_00207179_2022_2113438
crossref_primary_10_1080_00207179_2022_2113438
informaworld_taylorfrancis_310_1080_00207179_2022_2113438
proquest_journals_2870830353
PublicationCentury 2000
PublicationDate 2023-11-02
PublicationDateYYYYMMDD 2023-11-02
PublicationDate_xml – month: 11
  year: 2023
  text: 2023-11-02
  day: 02
PublicationDecade 2020
PublicationPlace Abingdon
PublicationPlace_xml – name: Abingdon
PublicationTitle International journal of control
PublicationYear 2023
Publisher Taylor & Francis
Taylor & Francis Ltd
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Ltd
References e_1_3_2_28_1
e_1_3_2_49_1
e_1_3_2_41_1
e_1_3_2_22_1
e_1_3_2_43_1
e_1_3_2_24_1
e_1_3_2_45_1
e_1_3_2_26_1
e_1_3_2_47_1
Ogata K. (e_1_3_2_33_1) 2010
e_1_3_2_16_1
e_1_3_2_39_1
e_1_3_2_9_1
e_1_3_2_18_1
e_1_3_2_31_1
e_1_3_2_54_1
e_1_3_2_10_1
e_1_3_2_52_1
Bertsekas D. P. (e_1_3_2_7_1) 1996
e_1_3_2_12_1
e_1_3_2_35_1
e_1_3_2_5_1
e_1_3_2_14_1
e_1_3_2_56_1
e_1_3_2_3_1
e_1_3_2_50_1
Luenberger D. G. (e_1_3_2_27_1) 1979
e_1_3_2_29_1
Pontryagin L. S. (e_1_3_2_37_1) 1986
Sutton R. S. (e_1_3_2_44_1) 2018
e_1_3_2_42_1
e_1_3_2_21_1
e_1_3_2_23_1
e_1_3_2_46_1
e_1_3_2_25_1
e_1_3_2_48_1
e_1_3_2_40_1
Bertsekas D. P. (e_1_3_2_6_1) 2020
e_1_3_2_17_1
e_1_3_2_38_1
e_1_3_2_8_1
e_1_3_2_19_1
e_1_3_2_2_1
e_1_3_2_30_1
e_1_3_2_55_1
e_1_3_2_11_1
e_1_3_2_32_1
e_1_3_2_53_1
e_1_3_2_13_1
e_1_3_2_34_1
e_1_3_2_4_1
e_1_3_2_15_1
e_1_3_2_36_1
e_1_3_2_51_1
Howard R. A. (e_1_3_2_20_1) 1960
References_xml – volume-title: Rollout, policy iteration, and distributed reinforcement learning
  year: 2020
  ident: e_1_3_2_6_1
– volume-title: Introduction to dynamic systems
  year: 1979
  ident: e_1_3_2_27_1
– ident: e_1_3_2_17_1
  doi: 10.1090/qam/1944-02-03
– ident: e_1_3_2_9_1
  doi: 10.1137/1.9780898718577
– ident: e_1_3_2_38_1
  doi: 10.1002/9780470182963
– ident: e_1_3_2_29_1
  doi: 10.1109/MRA.2012.2206474
– ident: e_1_3_2_54_1
  doi: 10.1080/00207170903171314
– ident: e_1_3_2_41_1
  doi: 10.1098/rspb.1995.0004
– ident: e_1_3_2_13_1
– ident: e_1_3_2_55_1
  doi: 10.1016/j.automatica.2012.05.017
– volume-title: Neuro-dynamic programming
  year: 1996
  ident: e_1_3_2_7_1
– ident: e_1_3_2_30_1
  doi: 10.1109/CDC.1988.194354
– volume-title: Mathematical theory of optimal processes
  year: 1986
  ident: e_1_3_2_37_1
– ident: e_1_3_2_8_1
  doi: 10.1023/A:1009635226865
– ident: e_1_3_2_19_1
  doi: 10.1080/00207179.2016.1266514
– ident: e_1_3_2_24_1
  doi: 10.1115/1.4028849
– ident: e_1_3_2_23_1
– ident: e_1_3_2_32_1
  doi: 10.1007/978-3-642-57760-4
– ident: e_1_3_2_56_1
  doi: 10.1016/j.automatica.2018.10.038
– ident: e_1_3_2_39_1
  doi: 10.1109/TNN.72
– ident: e_1_3_2_21_1
  doi: 10.2514/3.21634
– ident: e_1_3_2_3_1
  doi: 10.1073/pnas.40.4.231
– ident: e_1_3_2_11_1
  doi: 10.1016/0893-6080(94)00053-O
– ident: e_1_3_2_47_1
  doi: 10.1007/BF00992698
– ident: e_1_3_2_43_1
  doi: 10.1007/BF00115009
– ident: e_1_3_2_25_1
  doi: 10.23919/ACC45564.2020.9147911
– ident: e_1_3_2_22_1
  doi: 10.1007/BFb0036070
– ident: e_1_3_2_53_1
– ident: e_1_3_2_15_1
  doi: 10.1016/S0167-6911(97)00116-3
– ident: e_1_3_2_2_1
  doi: 10.1109/TSMC.1983.6313077
– ident: e_1_3_2_26_1
  doi: 10.1080/00207179.2013.790562
– ident: e_1_3_2_4_1
  doi: 10.1515/9781400874668
– ident: e_1_3_2_34_1
  doi: 10.1017/S0305004100030401
– ident: e_1_3_2_42_1
  doi: 10.1016/S0893-9659(02)00148-9
– volume-title: Reinforcement learning: An introduction
  year: 2018
  ident: e_1_3_2_44_1
– ident: e_1_3_2_50_1
  doi: 10.1109/IJCNN.1989.118583
– ident: e_1_3_2_45_1
  doi: 10.1007/978-1-4615-3618-5_3
– ident: e_1_3_2_49_1
  doi: 10.1007/BFb0006203
– ident: e_1_3_2_48_1
  doi: 10.1109/TCYB.2016.2586082
– ident: e_1_3_2_40_1
– ident: e_1_3_2_16_1
  doi: 10.1016/0005-1098(87)90087-2
– ident: e_1_3_2_18_1
  doi: 10.1109/JRA.1987.1087072
– ident: e_1_3_2_46_1
– ident: e_1_3_2_14_1
  doi: 10.1201/9781439821091
– ident: e_1_3_2_51_1
– volume-title: Modern control engineering
  year: 2010
  ident: e_1_3_2_33_1
– ident: e_1_3_2_12_1
  doi: 10.1016/j.sysconle.2004.08.007
– ident: e_1_3_2_36_1
  doi: 10.1137/1015071
– ident: e_1_3_2_52_1
  doi: 10.1016/0893-6080(90)90088-3
– ident: e_1_3_2_10_1
  doi: 10.1016/j.sysconle.2010.08.013
– ident: e_1_3_2_31_1
  doi: 10.1109/9.262032
– ident: e_1_3_2_35_1
  doi: 10.1016/j.conengprac.2019.104222
– ident: e_1_3_2_5_1
  doi: 10.1007/978-1-4419-7997-1_8
– volume-title: Dynamic programming and markov processes
  year: 1960
  ident: e_1_3_2_20_1
– ident: e_1_3_2_28_1
  doi: 10.1109/TCYB.2018.2821369
SSID ssj0013581
Score 2.4147468
Snippet Dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these...
SourceID proquest
crossref
informaworld
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 2799
SubjectTerms Adaptive control
adaptive dynamic programming
Algorithms
Closed loops
Computational efficiency
Computing costs
Control theory
control-affine system
Discrete time systems
Dynamic programming
Feedback control
Inverse dynamics
Iterative methods
Nonlinear constrained optimal control
Nonlinear control
Nonlinear systems
Optimal control
Optimization
real-time control
uncertainty
Title Dimension reduction based adaptive dynamic programming for optimal control of discrete-time nonlinear control-affine systems
URI https://www.tandfonline.com/doi/abs/10.1080/00207179.2022.2113438
https://www.proquest.com/docview/2870830353
Volume 96
WOSCitedRecordID wos000846699900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAWR
  databaseName: Taylor and Francis Online Journals
  customDbUrl:
  eissn: 1366-5820
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0013581
  issn: 0020-7179
  databaseCode: TFW
  dateStart: 19650101
  isFulltext: true
  titleUrlDefault: https://www.tandfonline.com
  providerName: Taylor & Francis
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELZQxQADb0ShIA-srhI7zmNEQMWAKoYC3Sw7tiUk-lBSmPjx-GKnFCHUAVYn5_iV851933cIXdKCW82VJNzalCQqiYjkGSPUaupKDEsb1PvTfTYc5uNx8RCiCesQVgk-tPVEEY2uhp9bqrqNiAMEN3ghADOhtO88GJYwgPs6yx6C-kaD5697BJ77nHnOSQKRFsPzWy3fdqdv3KU_dHWzAQ12_6Hpe2gnWJ_4yi-XfbRhpgdoe4WT8BB93ADfP5yh4QpoXWHiMOx1Gkst56AdsfZp7HEI7po4Qez6gGfu8cTVH-Lf8cxiQP1WzjAnkMQeT32jZdW-QqS1rgB7Qun6CD0ObkfXdySkaCCl820XTonLlGswMyWLVcpSaXNptFWlzBMpM1UWLCoLbVhkrKWx5pmmzEQmszlPdcKOUcd92pwg14fCKZM4U3FmkpQpqRhLqOLKRpFSRdRFSTs1ogz85ZBG41XES5pTP7gCBleEwe2i_lJs7gk81gkUq_MuFs3JifVpTgRbI9trF4kIuqAWcJWcO0uBs9M_VH2GtiDTfQODpD3UWVRv5hxtlu-Ll7q6aFb9J0QM_6Q
linkProvider Taylor & Francis
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELagIAEDb0ShgAfWVIkd5zEioCqidCrQzbJjW0KiD6WBiR-PL06gFUIdYLVzjp_3sO--Q-iSpMwoJoXHjIm8UIa-J1hMPWIUsSWaRmXU-1Mv7veT4TCdj4UBt0qwoY0Diih5NRxuuIyuXeIghBvMEIgzIaRtTRga0mQVrTEra2GvDzrP3y8JLHFZ86yZBDR1FM9vzSzIpwX00h_cuhRBnZ3_6Pwu2q4UUHzldsweWtHjfbQ1B0t4gD5uAPIfrtFwDsiusHYYxJ3CQokpMEisXCZ7XPl3jSwhtoPAE1s9su1XLvB4YjAE_uZWN_cgjz0eu16LvP7EE8bYAuwwpWeH6LFzO7juelWWBi-z5m1h-biImAJNU9BARjQSJhFaGZmJJBQilllK_SxVmvraGBIoFitCta9jk7BIhfQINeyv9TGyY0gtPwliGcQ6jKgUktKQSCaN70uZ-k0U1mvDswrCHDJpvPLgC-nUTS6HyeXV5DZR-4ts6jA8lhGk8wvPi_LyxLhMJ5wuoW3Vu4RX7GDG4TU5scoCoyd_aPoCbXQHDz3eu-vfn6JNSHxfRkWSFmoU-Zs-Q-vZe_Eyy8_LI_AJUsYD0w
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3JTsMwELXYhODAjlgK-MA1VWLHWY4IqEBUFQeW3iw7tiUklioJnPh4PLFTWiHUA1ydjBNv4xl75j2ETknOjGJSBMyYJIhlHAaCpTQgRhFbomnSZL0_9NPBIBsO81sfTVj5sErwoY0Dimh0NSzukTJtRBxkcIMXAmkmhHStB0Njms2jRWs6JxDVd9d7_L5IYJkjzbNeEsi0STy_VTO1PU2Bl_5Q1s0O1Fv_h3_fQGve_MRnbr5sojn9uoVWJ0AJt9HnBQD-wyEaLgHXFUYOw2ansFBiBOoRK8djj31014sVxLYN-M0-frH1-wB4_GYwpP2W1jIPgMUev7qfFmX7SiCMsQXYIUpXO-i-d3l3fhV4joagsM5tbbW4SJgCO1PQSCY0ESYTWhlZiCwWIpVFTsMiV5qG2hgSKZYqQnWoU5OxRMV0Fy3YT-s9ZNuQW20SpTJKdZxQKSSlMZFMmjCUMg_3UdwODS88gDnwaDzzaIxz6jqXQ-dy37n7qDsWGzkEj1kC-eS487o5OjGO54TTGbKddpJwrwwqDnfJmTUVGD34Q9UnaPn2osf714ObQ7QCrPdNSiTpoIW6fNdHaKn4qJ-q8rhZAF8_RQKF
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=Dimension+reduction+based+adaptive+dynamic+programming+for+optimal+control+of+discrete-time+nonlinear+control-affine+systems&rft.jtitle=International+journal+of+control&rft.au=Li%2C+Qiang&rft.au=Xu%2C+Yunjun&rft.date=2023-11-02&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=0020-7179&rft.eissn=1366-5820&rft.volume=96&rft.issue=11&rft.spage=2799&rft.epage=2811&rft_id=info:doi/10.1080%2F00207179.2022.2113438&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-7179&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-7179&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-7179&client=summon