Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems
Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately o...
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| Autor*in: |
Wei, Qinglai [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 24(2013), 6 vom: 19. Feb., Seite 1355-1367 |
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| Übergeordnetes Werk: |
volume:24 ; year:2013 ; number:6 ; day:19 ; month:02 ; pages:1355-1367 |
| Links: |
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| DOI / URN: |
10.1007/s00521-013-1361-7 |
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OLC202559318X |
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| 520 | |a Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. | ||
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10.1007/s00521-013-1361-7 doi (DE-627)OLC202559318X (DE-He213)s00521-013-1361-7-p DE-627 ger DE-627 rakwb eng 004 VZ Wei, Qinglai verfasserin aut Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Liu, Derong aut Enthalten in Neural computing & applications Springer London, 1993 24(2013), 6 vom: 19. Feb., Seite 1355-1367 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:24 year:2013 number:6 day:19 month:02 pages:1355-1367 https://doi.org/10.1007/s00521-013-1361-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 24 2013 6 19 02 1355-1367 |
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10.1007/s00521-013-1361-7 doi (DE-627)OLC202559318X (DE-He213)s00521-013-1361-7-p DE-627 ger DE-627 rakwb eng 004 VZ Wei, Qinglai verfasserin aut Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Liu, Derong aut Enthalten in Neural computing & applications Springer London, 1993 24(2013), 6 vom: 19. Feb., Seite 1355-1367 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:24 year:2013 number:6 day:19 month:02 pages:1355-1367 https://doi.org/10.1007/s00521-013-1361-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 24 2013 6 19 02 1355-1367 |
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10.1007/s00521-013-1361-7 doi (DE-627)OLC202559318X (DE-He213)s00521-013-1361-7-p DE-627 ger DE-627 rakwb eng 004 VZ Wei, Qinglai verfasserin aut Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Liu, Derong aut Enthalten in Neural computing & applications Springer London, 1993 24(2013), 6 vom: 19. Feb., Seite 1355-1367 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:24 year:2013 number:6 day:19 month:02 pages:1355-1367 https://doi.org/10.1007/s00521-013-1361-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 24 2013 6 19 02 1355-1367 |
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10.1007/s00521-013-1361-7 doi (DE-627)OLC202559318X (DE-He213)s00521-013-1361-7-p DE-627 ger DE-627 rakwb eng 004 VZ Wei, Qinglai verfasserin aut Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Liu, Derong aut Enthalten in Neural computing & applications Springer London, 1993 24(2013), 6 vom: 19. Feb., Seite 1355-1367 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:24 year:2013 number:6 day:19 month:02 pages:1355-1367 https://doi.org/10.1007/s00521-013-1361-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 24 2013 6 19 02 1355-1367 |
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10.1007/s00521-013-1361-7 doi (DE-627)OLC202559318X (DE-He213)s00521-013-1361-7-p DE-627 ger DE-627 rakwb eng 004 VZ Wei, Qinglai verfasserin aut Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2013 Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. Adaptive dynamic programming Approximate dynamic programming Adaptive critic designs Optimal control Neural networks Nonlinear systems Liu, Derong aut Enthalten in Neural computing & applications Springer London, 1993 24(2013), 6 vom: 19. Feb., Seite 1355-1367 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:24 year:2013 number:6 day:19 month:02 pages:1355-1367 https://doi.org/10.1007/s00521-013-1361-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 24 2013 6 19 02 1355-1367 |
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Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. © Springer-Verlag London 2013 |
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Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. © Springer-Verlag London 2013 |
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Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method. © Springer-Verlag London 2013 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC202559318X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114557.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2013 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-013-1361-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC202559318X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-013-1361-7-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wei, Qinglai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</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-Verlag London 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive dynamic programming</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Approximate dynamic programming</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive critic designs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Derong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">24(2013), 6 vom: 19. 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