Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating

Mario A. Santillo; Matthew S. Holzel; Jesse B. Hoagg; Dennis S. Bernstein

doi:10.1109/CDC.2009.5400822

Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating

Mario A. Santillo
, Matthew S. Holzel
, Jesse B. Hoagg
, Dennis S. Bernstein

Mechanical and Aerospace Engineering

Producción científica: Conference contribution › revisión exhaustiva

6 Citas (Scopus)

Resumen

We present a discrete-time adaptive control law that is effective for systems that are MIMO and either minimum phase or nonminimum phase. The adaptive control algorithm provides guidelines concerning the modeling information needed for implementation. This information includes a sufficient number of Markov parameters to capture the sign of the high-frequency gain as well as the nonminimum-phase zeros. No additional information about the poles or zeros need be known. In this paper, recursive least-squares estimation is used for concurrent Markov parameter estimation. We present numerical examples to illustrate the algorithm's effectiveness in handling nonminimum-phase zeros as plant changes occur.

Idioma original	English
Título de la publicación alojada	Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Páginas	3466-3471
Número de páginas	6
DOI	https://doi.org/10.1109/CDC.2009.5400822
Estado	Published - 2009
Evento	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 - Shanghai, China Duración: dic 15 2009 → dic 18 2009

Serie de la publicación

Nombre	Proceedings of the IEEE Conference on Decision and Control
ISSN (versión impresa)	0743-1546
ISSN (versión digital)	2576-2370

Conference

Conference	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
País/Territorio	China
Ciudad	Shanghai
Período	12/15/09 → 12/18/09

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Acceder al documento

10.1109/CDC.2009.5400822

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Santillo, M. A., Holzel, M. S., Hoagg, J. B., & Bernstein, D. S. (2009). Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating. En Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 (pp. 3466-3471). Artículo 5400822 (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2009.5400822

Santillo, Mario A. ; Holzel, Matthew S. ; Hoagg, Jesse B. et al. / Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating. Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. 2009. pp. 3466-3471 (Proceedings of the IEEE Conference on Decision and Control).

@inproceedings{cf289e0dcbb4489a9fdd34471ac93221,

title = "Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating",

abstract = "We present a discrete-time adaptive control law that is effective for systems that are MIMO and either minimum phase or nonminimum phase. The adaptive control algorithm provides guidelines concerning the modeling information needed for implementation. This information includes a sufficient number of Markov parameters to capture the sign of the high-frequency gain as well as the nonminimum-phase zeros. No additional information about the poles or zeros need be known. In this paper, recursive least-squares estimation is used for concurrent Markov parameter estimation. We present numerical examples to illustrate the algorithm's effectiveness in handling nonminimum-phase zeros as plant changes occur.",

author = "Santillo, \{Mario A.\} and Holzel, \{Matthew S.\} and Hoagg, \{Jesse B.\} and Bernstein, \{Dennis S.\}",

year = "2009",

doi = "10.1109/CDC.2009.5400822",

language = "English",

isbn = "9781424438716",

series = "Proceedings of the IEEE Conference on Decision and Control",

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booktitle = "Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009",

note = "48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 ; Conference date: 15-12-2009 Through 18-12-2009",

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Santillo, MA, Holzel, MS, Hoagg, JB & Bernstein, DS 2009, Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating. En Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009., 5400822, Proceedings of the IEEE Conference on Decision and Control, pp. 3466-3471, 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, Shanghai, China, 12/15/09. https://doi.org/10.1109/CDC.2009.5400822

Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating. / Santillo, Mario A.; Holzel, Matthew S.; Hoagg, Jesse B. et al.
Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. 2009. p. 3466-3471 5400822 (Proceedings of the IEEE Conference on Decision and Control).

Producción científica: Conference contribution › revisión exhaustiva

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AU - Hoagg, Jesse B.

AU - Bernstein, Dennis S.

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N2 - We present a discrete-time adaptive control law that is effective for systems that are MIMO and either minimum phase or nonminimum phase. The adaptive control algorithm provides guidelines concerning the modeling information needed for implementation. This information includes a sufficient number of Markov parameters to capture the sign of the high-frequency gain as well as the nonminimum-phase zeros. No additional information about the poles or zeros need be known. In this paper, recursive least-squares estimation is used for concurrent Markov parameter estimation. We present numerical examples to illustrate the algorithm's effectiveness in handling nonminimum-phase zeros as plant changes occur.

AB - We present a discrete-time adaptive control law that is effective for systems that are MIMO and either minimum phase or nonminimum phase. The adaptive control algorithm provides guidelines concerning the modeling information needed for implementation. This information includes a sufficient number of Markov parameters to capture the sign of the high-frequency gain as well as the nonminimum-phase zeros. No additional information about the poles or zeros need be known. In this paper, recursive least-squares estimation is used for concurrent Markov parameter estimation. We present numerical examples to illustrate the algorithm's effectiveness in handling nonminimum-phase zeros as plant changes occur.

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M3 - Conference contribution

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Santillo MA, Holzel MS, Hoagg JB, Bernstein DS. Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating. En Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. 2009. p. 3466-3471. 5400822. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC.2009.5400822

Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating

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