Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning
Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers...
Ausführliche Beschreibung
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| Autor*in: |
Zhu, Lemei M [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on control systems technology - New York, NY : IEEE, 1993, 23(2015), 1, Seite 264-273 |
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| Übergeordnetes Werk: |
volume:23 ; year:2015 ; number:1 ; pages:264-273 |
| Links: |
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| DOI / URN: |
10.1109/TCST.2014.2322778 |
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| Katalog-ID: |
OLC1959560093 |
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| 520 | |a Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. | ||
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a online learning algorithm | |
| 650 | 4 | |a continuous-time systems | |
| 650 | 4 | |a learning systems | |
| 650 | 4 | |a adaptive control | |
| 650 | 4 | |a observers | |
| 650 | 4 | |a optimal state-feedback controllers | |
| 650 | 4 | |a Convergence | |
| 650 | 4 | |a Control systems | |
| 650 | 4 | |a IRL Bellman equation | |
| 650 | 4 | |a suboptimal output-feedback solution | |
| 650 | 4 | |a F-16 aircraft | |
| 650 | 4 | |a Integral reinforcement learning (IRL) | |
| 650 | 4 | |a IRL-based algorithm | |
| 650 | 4 | |a Equations | |
| 650 | 4 | |a output-feedback gain | |
| 650 | 4 | |a Heuristic algorithms | |
| 650 | 4 | |a linear systems | |
| 650 | 4 | |a output-feedback controllers | |
| 650 | 4 | |a optimal control | |
| 650 | 4 | |a adaptive suboptimal output-feedback control | |
| 650 | 4 | |a partially unknown CT linear systems | |
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a output-feedback policy | |
| 650 | 4 | |a CT systems | |
| 650 | 4 | |a output feedback | |
| 650 | 4 | |a integral reinforcement learning technique | |
| 650 | 4 | |a X-Y table | |
| 650 | 4 | |a state feedback | |
| 650 | 4 | |a suboptimal control | |
| 650 | 4 | |a linear continuous-time (CT) systems | |
| 650 | 4 | |a IRL technique | |
| 650 | 4 | |a continuous time systems | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Distance learning | |
| 650 | 4 | |a Feedback | |
| 700 | 1 | |a Modares, Hamidreza |4 oth | |
| 700 | 1 | |a Peen, Gan Oon |4 oth | |
| 700 | 1 | |a Lewis, Frank L |4 oth | |
| 700 | 0 | |a Baozeng Yue |4 oth | |
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10.1109/TCST.2014.2322778 doi PQ20160617 (DE-627)OLC1959560093 (DE-599)GBVOLC1959560093 (PRQ)c2112-2ac35ac992406e3539935e36e932f972210f11cc993d5fd62fec6fe25fd324ed0 (KEY)0226256820150000023000100264adaptivesuboptimaloutputfeedbackcontrolforlinearsy DE-627 ger DE-627 rakwb eng 004 DNB Zhu, Lemei M verfasserin aut Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback Modares, Hamidreza oth Peen, Gan Oon oth Lewis, Frank L oth Baozeng Yue oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 1, Seite 264-273 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:1 pages:264-273 http://dx.doi.org/10.1109/TCST.2014.2322778 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6824757 http://search.proquest.com/docview/1638471433 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 1 264-273 |
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10.1109/TCST.2014.2322778 doi PQ20160617 (DE-627)OLC1959560093 (DE-599)GBVOLC1959560093 (PRQ)c2112-2ac35ac992406e3539935e36e932f972210f11cc993d5fd62fec6fe25fd324ed0 (KEY)0226256820150000023000100264adaptivesuboptimaloutputfeedbackcontrolforlinearsy DE-627 ger DE-627 rakwb eng 004 DNB Zhu, Lemei M verfasserin aut Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback Modares, Hamidreza oth Peen, Gan Oon oth Lewis, Frank L oth Baozeng Yue oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 1, Seite 264-273 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:1 pages:264-273 http://dx.doi.org/10.1109/TCST.2014.2322778 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6824757 http://search.proquest.com/docview/1638471433 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 1 264-273 |
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10.1109/TCST.2014.2322778 doi PQ20160617 (DE-627)OLC1959560093 (DE-599)GBVOLC1959560093 (PRQ)c2112-2ac35ac992406e3539935e36e932f972210f11cc993d5fd62fec6fe25fd324ed0 (KEY)0226256820150000023000100264adaptivesuboptimaloutputfeedbackcontrolforlinearsy DE-627 ger DE-627 rakwb eng 004 DNB Zhu, Lemei M verfasserin aut Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback Modares, Hamidreza oth Peen, Gan Oon oth Lewis, Frank L oth Baozeng Yue oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 1, Seite 264-273 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:1 pages:264-273 http://dx.doi.org/10.1109/TCST.2014.2322778 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6824757 http://search.proquest.com/docview/1638471433 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 1 264-273 |
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10.1109/TCST.2014.2322778 doi PQ20160617 (DE-627)OLC1959560093 (DE-599)GBVOLC1959560093 (PRQ)c2112-2ac35ac992406e3539935e36e932f972210f11cc993d5fd62fec6fe25fd324ed0 (KEY)0226256820150000023000100264adaptivesuboptimaloutputfeedbackcontrolforlinearsy DE-627 ger DE-627 rakwb eng 004 DNB Zhu, Lemei M verfasserin aut Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback Modares, Hamidreza oth Peen, Gan Oon oth Lewis, Frank L oth Baozeng Yue oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 1, Seite 264-273 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:1 pages:264-273 http://dx.doi.org/10.1109/TCST.2014.2322778 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6824757 http://search.proquest.com/docview/1638471433 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 1 264-273 |
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10.1109/TCST.2014.2322778 doi PQ20160617 (DE-627)OLC1959560093 (DE-599)GBVOLC1959560093 (PRQ)c2112-2ac35ac992406e3539935e36e932f972210f11cc993d5fd62fec6fe25fd324ed0 (KEY)0226256820150000023000100264adaptivesuboptimaloutputfeedbackcontrolforlinearsy DE-627 ger DE-627 rakwb eng 004 DNB Zhu, Lemei M verfasserin aut Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback Modares, Hamidreza oth Peen, Gan Oon oth Lewis, Frank L oth Baozeng Yue oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 23(2015), 1, Seite 264-273 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:23 year:2015 number:1 pages:264-273 http://dx.doi.org/10.1109/TCST.2014.2322778 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6824757 http://search.proquest.com/docview/1638471433 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 AR 23 2015 1 264-273 |
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Zhu, Lemei M ddc 004 misc learning (artificial intelligence) misc online learning algorithm misc continuous-time systems misc learning systems misc adaptive control misc observers misc optimal state-feedback controllers misc Convergence misc Control systems misc IRL Bellman equation misc suboptimal output-feedback solution misc F-16 aircraft misc Integral reinforcement learning (IRL) misc IRL-based algorithm misc Equations misc output-feedback gain misc Heuristic algorithms misc linear systems misc output-feedback controllers misc optimal control misc adaptive suboptimal output-feedback control misc partially unknown CT linear systems misc Mathematical model misc output-feedback policy misc CT systems misc output feedback misc integral reinforcement learning technique misc X-Y table misc state feedback misc suboptimal control misc linear continuous-time (CT) systems misc IRL technique misc continuous time systems misc Algorithms misc Distance learning misc Feedback Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning |
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004 DNB Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning learning (artificial intelligence) online learning algorithm continuous-time systems learning systems adaptive control observers optimal state-feedback controllers Convergence Control systems IRL Bellman equation suboptimal output-feedback solution F-16 aircraft Integral reinforcement learning (IRL) IRL-based algorithm Equations output-feedback gain Heuristic algorithms linear systems output-feedback controllers optimal control adaptive suboptimal output-feedback control partially unknown CT linear systems Mathematical model output-feedback policy CT systems output feedback integral reinforcement learning technique X-Y table state feedback suboptimal control linear continuous-time (CT) systems IRL technique continuous time systems Algorithms Distance learning Feedback |
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ddc 004 misc learning (artificial intelligence) misc online learning algorithm misc continuous-time systems misc learning systems misc adaptive control misc observers misc optimal state-feedback controllers misc Convergence misc Control systems misc IRL Bellman equation misc suboptimal output-feedback solution misc F-16 aircraft misc Integral reinforcement learning (IRL) misc IRL-based algorithm misc Equations misc output-feedback gain misc Heuristic algorithms misc linear systems misc output-feedback controllers misc optimal control misc adaptive suboptimal output-feedback control misc partially unknown CT linear systems misc Mathematical model misc output-feedback policy misc CT systems misc output feedback misc integral reinforcement learning technique misc X-Y table misc state feedback misc suboptimal control misc linear continuous-time (CT) systems misc IRL technique misc continuous time systems misc Algorithms misc Distance learning misc Feedback |
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Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning |
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adaptive suboptimal output-feedback control for linear systems using integral reinforcement learning |
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Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning |
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Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. |
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Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. |
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Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft. |
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Adaptive Suboptimal Output-Feedback Control for Linear Systems Using Integral Reinforcement Learning |
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Modares, Hamidreza Peen, Gan Oon Lewis, Frank L Baozeng Yue |
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