Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems
Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Gang [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media Dordrecht 2012 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Nonlinear dynamics - Springer Netherlands, 1990, 72(2012), 1-2 vom: 18. Dez., Seite 175-184 |
|---|---|
| Übergeordnetes Werk: |
volume:72 ; year:2012 ; number:1-2 ; day:18 ; month:12 ; pages:175-184 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11071-012-0701-y |
|---|
| Katalog-ID: |
OLC2051096996 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2051096996 | ||
| 003 | DE-627 | ||
| 005 | 20230503225726.0 | ||
| 007 | tu | ||
| 008 | 200820s2012 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11071-012-0701-y |2 doi | |
| 035 | |a (DE-627)OLC2051096996 | ||
| 035 | |a (DE-He213)s11071-012-0701-y-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q VZ |
| 084 | |a 11 |2 ssgn | ||
| 100 | 1 | |a Sun, Gang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| 264 | 1 | |c 2012 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media Dordrecht 2012 | ||
| 520 | |a Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. | ||
| 650 | 4 | |a Strict-feedback nonlinear systems | |
| 650 | 4 | |a Complexity growing problem | |
| 650 | 4 | |a Single neural network | |
| 650 | 4 | |a Adaptive control | |
| 700 | 1 | |a Wang, Dan |4 aut | |
| 700 | 1 | |a Li, Tieshan |4 aut | |
| 700 | 1 | |a Peng, Zhouhua |4 aut | |
| 700 | 1 | |a Wang, Hao |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nonlinear dynamics |d Springer Netherlands, 1990 |g 72(2012), 1-2 vom: 18. Dez., Seite 175-184 |w (DE-627)130936782 |w (DE-600)1058624-6 |w (DE-576)034188126 |x 0924-090X |7 nnns |
| 773 | 1 | 8 | |g volume:72 |g year:2012 |g number:1-2 |g day:18 |g month:12 |g pages:175-184 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11071-012-0701-y |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a SSG-OLC-CHE | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2006 | ||
| 951 | |a AR | ||
| 952 | |d 72 |j 2012 |e 1-2 |b 18 |c 12 |h 175-184 | ||
| author_variant |
g s gs d w dw t l tl z p zp h w hw |
|---|---|
| matchkey_str |
article:0924090X:2012----::igeerlewrapoiainaeaatvcnrloalsoucrant |
| hierarchy_sort_str |
2012 |
| publishDate |
2012 |
| allfields |
10.1007/s11071-012-0701-y doi (DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Sun, Gang verfasserin aut Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2012 Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control Wang, Dan aut Li, Tieshan aut Peng, Zhouhua aut Wang, Hao aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 72(2012), 1-2 vom: 18. Dez., Seite 175-184 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 https://doi.org/10.1007/s11071-012-0701-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 AR 72 2012 1-2 18 12 175-184 |
| spelling |
10.1007/s11071-012-0701-y doi (DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Sun, Gang verfasserin aut Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2012 Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control Wang, Dan aut Li, Tieshan aut Peng, Zhouhua aut Wang, Hao aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 72(2012), 1-2 vom: 18. Dez., Seite 175-184 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 https://doi.org/10.1007/s11071-012-0701-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 AR 72 2012 1-2 18 12 175-184 |
| allfields_unstemmed |
10.1007/s11071-012-0701-y doi (DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Sun, Gang verfasserin aut Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2012 Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control Wang, Dan aut Li, Tieshan aut Peng, Zhouhua aut Wang, Hao aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 72(2012), 1-2 vom: 18. Dez., Seite 175-184 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 https://doi.org/10.1007/s11071-012-0701-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 AR 72 2012 1-2 18 12 175-184 |
| allfieldsGer |
10.1007/s11071-012-0701-y doi (DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Sun, Gang verfasserin aut Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2012 Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control Wang, Dan aut Li, Tieshan aut Peng, Zhouhua aut Wang, Hao aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 72(2012), 1-2 vom: 18. Dez., Seite 175-184 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 https://doi.org/10.1007/s11071-012-0701-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 AR 72 2012 1-2 18 12 175-184 |
| allfieldsSound |
10.1007/s11071-012-0701-y doi (DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Sun, Gang verfasserin aut Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2012 Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control Wang, Dan aut Li, Tieshan aut Peng, Zhouhua aut Wang, Hao aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 72(2012), 1-2 vom: 18. Dez., Seite 175-184 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 https://doi.org/10.1007/s11071-012-0701-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 AR 72 2012 1-2 18 12 175-184 |
| language |
English |
| source |
Enthalten in Nonlinear dynamics 72(2012), 1-2 vom: 18. Dez., Seite 175-184 volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 |
| sourceStr |
Enthalten in Nonlinear dynamics 72(2012), 1-2 vom: 18. Dez., Seite 175-184 volume:72 year:2012 number:1-2 day:18 month:12 pages:175-184 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Nonlinear dynamics |
| authorswithroles_txt_mv |
Sun, Gang @@aut@@ Wang, Dan @@aut@@ Li, Tieshan @@aut@@ Peng, Zhouhua @@aut@@ Wang, Hao @@aut@@ |
| publishDateDaySort_date |
2012-12-18T00:00:00Z |
| hierarchy_top_id |
130936782 |
| dewey-sort |
3510 |
| id |
OLC2051096996 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2051096996</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503225726.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2012 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11071-012-0701-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2051096996</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11071-012-0701-y-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Gang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media Dordrecht 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strict-feedback nonlinear systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complexity growing problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive control</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Dan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Tieshan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Peng, Zhouhua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Nonlinear dynamics</subfield><subfield code="d">Springer Netherlands, 1990</subfield><subfield code="g">72(2012), 1-2 vom: 18. Dez., Seite 175-184</subfield><subfield code="w">(DE-627)130936782</subfield><subfield code="w">(DE-600)1058624-6</subfield><subfield code="w">(DE-576)034188126</subfield><subfield code="x">0924-090X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:72</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:1-2</subfield><subfield code="g">day:18</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:175-184</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11071-012-0701-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">72</subfield><subfield code="j">2012</subfield><subfield code="e">1-2</subfield><subfield code="b">18</subfield><subfield code="c">12</subfield><subfield code="h">175-184</subfield></datafield></record></collection>
|
| author |
Sun, Gang |
| spellingShingle |
Sun, Gang ddc 510 ssgn 11 misc Strict-feedback nonlinear systems misc Complexity growing problem misc Single neural network misc Adaptive control Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| authorStr |
Sun, Gang |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130936782 |
| format |
Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0924-090X |
| topic_title |
510 VZ 11 ssgn Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems Strict-feedback nonlinear systems Complexity growing problem Single neural network Adaptive control |
| topic |
ddc 510 ssgn 11 misc Strict-feedback nonlinear systems misc Complexity growing problem misc Single neural network misc Adaptive control |
| topic_unstemmed |
ddc 510 ssgn 11 misc Strict-feedback nonlinear systems misc Complexity growing problem misc Single neural network misc Adaptive control |
| topic_browse |
ddc 510 ssgn 11 misc Strict-feedback nonlinear systems misc Complexity growing problem misc Single neural network misc Adaptive control |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Nonlinear dynamics |
| hierarchy_parent_id |
130936782 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Nonlinear dynamics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 |
| title |
Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| ctrlnum |
(DE-627)OLC2051096996 (DE-He213)s11071-012-0701-y-p |
| title_full |
Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| author_sort |
Sun, Gang |
| journal |
Nonlinear dynamics |
| journalStr |
Nonlinear dynamics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2012 |
| contenttype_str_mv |
txt |
| container_start_page |
175 |
| author_browse |
Sun, Gang Wang, Dan Li, Tieshan Peng, Zhouhua Wang, Hao |
| container_volume |
72 |
| class |
510 VZ 11 ssgn |
| format_se |
Aufsätze |
| author-letter |
Sun, Gang |
| doi_str_mv |
10.1007/s11071-012-0701-y |
| dewey-full |
510 |
| title_sort |
single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| title_auth |
Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| abstract |
Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. © Springer Science+Business Media Dordrecht 2012 |
| abstractGer |
Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. © Springer Science+Business Media Dordrecht 2012 |
| abstract_unstemmed |
Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. © Springer Science+Business Media Dordrecht 2012 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2006 |
| container_issue |
1-2 |
| title_short |
Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems |
| url |
https://doi.org/10.1007/s11071-012-0701-y |
| remote_bool |
false |
| author2 |
Wang, Dan Li, Tieshan Peng, Zhouhua Wang, Hao |
| author2Str |
Wang, Dan Li, Tieshan Peng, Zhouhua Wang, Hao |
| ppnlink |
130936782 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11071-012-0701-y |
| up_date |
2024-07-04T03:34:33.583Z |
| _version_ |
1803617906028707840 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2051096996</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503225726.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2012 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11071-012-0701-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2051096996</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11071-012-0701-y-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Gang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media Dordrecht 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strict-feedback nonlinear systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complexity growing problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive control</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Dan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Tieshan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Peng, Zhouhua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Nonlinear dynamics</subfield><subfield code="d">Springer Netherlands, 1990</subfield><subfield code="g">72(2012), 1-2 vom: 18. Dez., Seite 175-184</subfield><subfield code="w">(DE-627)130936782</subfield><subfield code="w">(DE-600)1058624-6</subfield><subfield code="w">(DE-576)034188126</subfield><subfield code="x">0924-090X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:72</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:1-2</subfield><subfield code="g">day:18</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:175-184</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11071-012-0701-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">72</subfield><subfield code="j">2012</subfield><subfield code="e">1-2</subfield><subfield code="b">18</subfield><subfield code="c">12</subfield><subfield code="h">175-184</subfield></datafield></record></collection>
|
| score |
7.39966 |