Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results
The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fagiano, Lorenzo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on automatic control - New York, NY : Inst., 1963, 61(2016), 7, Seite 1854-1868 |
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| Übergeordnetes Werk: |
volume:61 ; year:2016 ; number:7 ; pages:1854-1868 |
| Links: |
|---|
| DOI / URN: |
10.1109/TAC.2015.2479520 |
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OLC1978730640 |
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| 520 | |a The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. | ||
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10.1109/TAC.2015.2479520 doi PQ20160720 (DE-627)OLC1978730640 (DE-599)GBVOLC1978730640 (PRQ)c706-9389fed10c33ba352642e9b99210383cc57f7a6ce29fcb77f9f72456928776280 (KEY)0005057120160000061000701854learninganonlinearcontrollerfromdatatheorycomputat DE-627 ger DE-627 rakwb eng 620 DNB Fagiano, Lorenzo verfasserin aut Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. Stability analysis Control design Numerical stability Approximation methods Noise Closed loop systems Data models Novara, Carlo oth Enthalten in IEEE transactions on automatic control New York, NY : Inst., 1963 61(2016), 7, Seite 1854-1868 (DE-627)129601705 (DE-600)241443-0 (DE-576)015095320 0018-9286 nnns volume:61 year:2016 number:7 pages:1854-1868 http://dx.doi.org/10.1109/TAC.2015.2479520 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_30 GBV_ILN_70 GBV_ILN_120 GBV_ILN_193 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2333 GBV_ILN_4193 AR 61 2016 7 1854-1868 |
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10.1109/TAC.2015.2479520 doi PQ20160720 (DE-627)OLC1978730640 (DE-599)GBVOLC1978730640 (PRQ)c706-9389fed10c33ba352642e9b99210383cc57f7a6ce29fcb77f9f72456928776280 (KEY)0005057120160000061000701854learninganonlinearcontrollerfromdatatheorycomputat DE-627 ger DE-627 rakwb eng 620 DNB Fagiano, Lorenzo verfasserin aut Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. Stability analysis Control design Numerical stability Approximation methods Noise Closed loop systems Data models Novara, Carlo oth Enthalten in IEEE transactions on automatic control New York, NY : Inst., 1963 61(2016), 7, Seite 1854-1868 (DE-627)129601705 (DE-600)241443-0 (DE-576)015095320 0018-9286 nnns volume:61 year:2016 number:7 pages:1854-1868 http://dx.doi.org/10.1109/TAC.2015.2479520 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_30 GBV_ILN_70 GBV_ILN_120 GBV_ILN_193 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2333 GBV_ILN_4193 AR 61 2016 7 1854-1868 |
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10.1109/TAC.2015.2479520 doi PQ20160720 (DE-627)OLC1978730640 (DE-599)GBVOLC1978730640 (PRQ)c706-9389fed10c33ba352642e9b99210383cc57f7a6ce29fcb77f9f72456928776280 (KEY)0005057120160000061000701854learninganonlinearcontrollerfromdatatheorycomputat DE-627 ger DE-627 rakwb eng 620 DNB Fagiano, Lorenzo verfasserin aut Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. Stability analysis Control design Numerical stability Approximation methods Noise Closed loop systems Data models Novara, Carlo oth Enthalten in IEEE transactions on automatic control New York, NY : Inst., 1963 61(2016), 7, Seite 1854-1868 (DE-627)129601705 (DE-600)241443-0 (DE-576)015095320 0018-9286 nnns volume:61 year:2016 number:7 pages:1854-1868 http://dx.doi.org/10.1109/TAC.2015.2479520 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_30 GBV_ILN_70 GBV_ILN_120 GBV_ILN_193 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2333 GBV_ILN_4193 AR 61 2016 7 1854-1868 |
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10.1109/TAC.2015.2479520 doi PQ20160720 (DE-627)OLC1978730640 (DE-599)GBVOLC1978730640 (PRQ)c706-9389fed10c33ba352642e9b99210383cc57f7a6ce29fcb77f9f72456928776280 (KEY)0005057120160000061000701854learninganonlinearcontrollerfromdatatheorycomputat DE-627 ger DE-627 rakwb eng 620 DNB Fagiano, Lorenzo verfasserin aut Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. Stability analysis Control design Numerical stability Approximation methods Noise Closed loop systems Data models Novara, Carlo oth Enthalten in IEEE transactions on automatic control New York, NY : Inst., 1963 61(2016), 7, Seite 1854-1868 (DE-627)129601705 (DE-600)241443-0 (DE-576)015095320 0018-9286 nnns volume:61 year:2016 number:7 pages:1854-1868 http://dx.doi.org/10.1109/TAC.2015.2479520 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_30 GBV_ILN_70 GBV_ILN_120 GBV_ILN_193 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2333 GBV_ILN_4193 AR 61 2016 7 1854-1868 |
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10.1109/TAC.2015.2479520 doi PQ20160720 (DE-627)OLC1978730640 (DE-599)GBVOLC1978730640 (PRQ)c706-9389fed10c33ba352642e9b99210383cc57f7a6ce29fcb77f9f72456928776280 (KEY)0005057120160000061000701854learninganonlinearcontrollerfromdatatheorycomputat DE-627 ger DE-627 rakwb eng 620 DNB Fagiano, Lorenzo verfasserin aut Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. Stability analysis Control design Numerical stability Approximation methods Noise Closed loop systems Data models Novara, Carlo oth Enthalten in IEEE transactions on automatic control New York, NY : Inst., 1963 61(2016), 7, Seite 1854-1868 (DE-627)129601705 (DE-600)241443-0 (DE-576)015095320 0018-9286 nnns volume:61 year:2016 number:7 pages:1854-1868 http://dx.doi.org/10.1109/TAC.2015.2479520 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_30 GBV_ILN_70 GBV_ILN_120 GBV_ILN_193 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2333 GBV_ILN_4193 AR 61 2016 7 1854-1868 |
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Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results |
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Fagiano, Lorenzo |
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IEEE transactions on automatic control |
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IEEE transactions on automatic control |
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eng |
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2016 |
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Fagiano, Lorenzo |
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Fagiano, Lorenzo |
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10.1109/TAC.2015.2479520 |
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620 |
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learning a nonlinear controller from data: theory, computation, and experimental results |
| title_auth |
Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results |
| abstract |
The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. |
| abstractGer |
The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. |
| abstract_unstemmed |
The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a <inline-formula><tex-math notation="LaTeX">\ell_{1}</tex-math></inline-formula>-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances. |
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| title_short |
Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results |
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http://dx.doi.org/10.1109/TAC.2015.2479520 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7271025 |
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2024-07-03T22:39:52.436Z |
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