Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach
Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value i...
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| Autor*in: |
Wei, Qinglai [verfasserIn] Liu, Derong [verfasserIn] Xu, Yancai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 20(2014), 2 vom: 25. Nov., Seite 697-706 |
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| Übergeordnetes Werk: |
volume:20 ; year:2014 ; number:2 ; day:25 ; month:11 ; pages:697-706 |
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| DOI / URN: |
10.1007/s00500-014-1533-0 |
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SPR006489095 |
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| 520 | |a Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. | ||
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10.1007/s00500-014-1533-0 doi (DE-627)SPR006489095 (SPR)s00500-014-1533-0-e DE-627 ger DE-627 rakwb eng Wei, Qinglai verfasserin aut Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. Adaptive dynamic programming (dpeaa)DE-He213 Approximate dynamic programming (dpeaa)DE-He213 Adaptive critic designs (dpeaa)DE-He213 Optimal control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Liu, Derong verfasserin aut Xu, Yancai verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2014), 2 vom: 25. Nov., Seite 697-706 (DE-627)SPR006469531 nnns volume:20 year:2014 number:2 day:25 month:11 pages:697-706 https://dx.doi.org/10.1007/s00500-014-1533-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2014 2 25 11 697-706 |
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10.1007/s00500-014-1533-0 doi (DE-627)SPR006489095 (SPR)s00500-014-1533-0-e DE-627 ger DE-627 rakwb eng Wei, Qinglai verfasserin aut Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. Adaptive dynamic programming (dpeaa)DE-He213 Approximate dynamic programming (dpeaa)DE-He213 Adaptive critic designs (dpeaa)DE-He213 Optimal control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Liu, Derong verfasserin aut Xu, Yancai verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2014), 2 vom: 25. Nov., Seite 697-706 (DE-627)SPR006469531 nnns volume:20 year:2014 number:2 day:25 month:11 pages:697-706 https://dx.doi.org/10.1007/s00500-014-1533-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2014 2 25 11 697-706 |
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10.1007/s00500-014-1533-0 doi (DE-627)SPR006489095 (SPR)s00500-014-1533-0-e DE-627 ger DE-627 rakwb eng Wei, Qinglai verfasserin aut Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. Adaptive dynamic programming (dpeaa)DE-He213 Approximate dynamic programming (dpeaa)DE-He213 Adaptive critic designs (dpeaa)DE-He213 Optimal control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Liu, Derong verfasserin aut Xu, Yancai verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2014), 2 vom: 25. Nov., Seite 697-706 (DE-627)SPR006469531 nnns volume:20 year:2014 number:2 day:25 month:11 pages:697-706 https://dx.doi.org/10.1007/s00500-014-1533-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2014 2 25 11 697-706 |
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10.1007/s00500-014-1533-0 doi (DE-627)SPR006489095 (SPR)s00500-014-1533-0-e DE-627 ger DE-627 rakwb eng Wei, Qinglai verfasserin aut Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. Adaptive dynamic programming (dpeaa)DE-He213 Approximate dynamic programming (dpeaa)DE-He213 Adaptive critic designs (dpeaa)DE-He213 Optimal control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Liu, Derong verfasserin aut Xu, Yancai verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2014), 2 vom: 25. Nov., Seite 697-706 (DE-627)SPR006469531 nnns volume:20 year:2014 number:2 day:25 month:11 pages:697-706 https://dx.doi.org/10.1007/s00500-014-1533-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2014 2 25 11 697-706 |
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10.1007/s00500-014-1533-0 doi (DE-627)SPR006489095 (SPR)s00500-014-1533-0-e DE-627 ger DE-627 rakwb eng Wei, Qinglai verfasserin aut Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. Adaptive dynamic programming (dpeaa)DE-He213 Approximate dynamic programming (dpeaa)DE-He213 Adaptive critic designs (dpeaa)DE-He213 Optimal control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Liu, Derong verfasserin aut Xu, Yancai verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2014), 2 vom: 25. Nov., Seite 697-706 (DE-627)SPR006469531 nnns volume:20 year:2014 number:2 day:25 month:11 pages:697-706 https://dx.doi.org/10.1007/s00500-014-1533-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2014 2 25 11 697-706 |
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Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. |
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Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. |
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Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm. |
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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">SPR006489095</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002815.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-014-1533-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006489095</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-014-1533-0-e</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="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">Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, a novel value iteration adaptive dynamic programming (ADP) algorithm, called “generalized value iteration ADP” algorithm, is developed to solve infinite horizon optimal tracking control problems for a class of discrete-time nonlinear systems. The developed generalized value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. Convergence property is developed to guarantee that the iterative performance index function will converge to the optimum. 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, a simulation example is given to illustrate the performance of the developed algorithm.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive dynamic programming</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Approximate dynamic programming</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive critic designs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reinforcement learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Derong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Yancai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">20(2014), 2 vom: 25. Nov., Seite 697-706</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:2</subfield><subfield code="g">day:25</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:697-706</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-014-1533-0</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_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2014</subfield><subfield code="e">2</subfield><subfield code="b">25</subfield><subfield code="c">11</subfield><subfield code="h">697-706</subfield></datafield></record></collection>
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