Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

R. Franco; H. Rios; D. Efimov; W. Perruquetti

doi:10.1109/CDC.2018.8619104

Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

R. Franco, H. Rios, D. Efimov, W. Perruquetti

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.

Idioma original	Inglés
Título de la publicación alojada	2018 IEEE Conference on Decision and Control, CDC 2018
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	5506-5511
Número de páginas	6
ISBN (versión digital)	9781538613955
DOI	https://doi.org/10.1109/CDC.2018.8619104
Estado	Publicada - 2 jul. 2018
Evento	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, Estados Unidos Duración: 17 dic. 2018 → 19 dic. 2018

Serie de la publicación

Nombre	Proceedings of the IEEE Conference on Decision and Control
Volumen	2018-December
ISSN (versión impresa)	0743-1546
ISSN (versión digital)	2576-2370

Conferencia

Conferencia	57th IEEE Conference on Decision and Control, CDC 2018
País/Territorio	Estados Unidos
Ciudad	Miami
Período	17/12/18 → 19/12/18

Nota bibliográfica

Publisher Copyright:
© 2018 IEEE.

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10.1109/CDC.2018.8619104

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Franco, R., Rios, H., Efimov, D., & Perruquetti, W. (2018). Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach. En 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 5506-5511). Artículo 8619104 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619104

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title = "Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach",

abstract = "This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.",

keywords = "Adaptive Observer, Nonlinear Systems, Sliding-Modes",

author = "R. Franco and H. Rios and D. Efimov and W. Perruquetti",

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year = "2018",

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doi = "10.1109/CDC.2018.8619104",

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Franco, R, Rios, H, Efimov, D & Perruquetti, W 2018, Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach. En 2018 IEEE Conference on Decision and Control, CDC 2018., 8619104, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 5506-5511, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, Estados Unidos, 17/12/18. https://doi.org/10.1109/CDC.2018.8619104

Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach. / Franco, R.; Rios, H.; Efimov, D. et al.
2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 5506-5511 8619104 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Adaptive Estimation for Uncertain Nonlinear Systems

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AU - Efimov, D.

AU - Perruquetti, W.

PY - 2018/7/2

Y1 - 2018/7/2

N2 - This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.

AB - This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.

KW - Adaptive Observer

KW - Nonlinear Systems

KW - Sliding-Modes

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BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 17 December 2018 through 19 December 2018

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Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

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