Multivariable optimal point tracking algorithm

Carlos Andrés Devia; Diego Patino

doi:10.1109/CCA.2016.7587985

Multivariable optimal point tracking algorithm

Carlos Andrés Devia, Diego Patino

Departamento de Ingeniería Electrónica

Universidad Javeriana

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

1 Cita (Scopus)

Resumen

This paper presents a method to track local optimal points in unknown multivariable non-convex time-varying cost functions. The main contribution of this method is that is model-free, the tracking property is proven and an error bound is given. Next several simulations are performed. An example of Maximum Power Point Tracking in Photovoltaic Panels is presented including the successful tracking of the Maximum Power Point. Finally conclusions and further work are discussed.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE Conference on Control Applications, CCA 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1294-1299
Número de páginas	6
ISBN (versión digital)	9781509007554
DOI	https://doi.org/10.1109/CCA.2016.7587985
Estado	Publicada - 10 oct. 2016
Evento	2016 IEEE Conference on Control Applications, CCA 2016 - Buenos Aires, Argentina Duración: 19 sep. 2016 → 22 sep. 2016

Serie de la publicación

Nombre	2016 IEEE Conference on Control Applications, CCA 2016

Conferencia

Conferencia	2016 IEEE Conference on Control Applications, CCA 2016
País/Territorio	Argentina
Ciudad	Buenos Aires
Período	19/09/16 → 22/09/16

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title = "Multivariable optimal point tracking algorithm",

abstract = "This paper presents a method to track local optimal points in unknown multivariable non-convex time-varying cost functions. The main contribution of this method is that is model-free, the tracking property is proven and an error bound is given. Next several simulations are performed. An example of Maximum Power Point Tracking in Photovoltaic Panels is presented including the successful tracking of the Maximum Power Point. Finally conclusions and further work are discussed.",

author = "Devia, {Carlos Andr{\'e}s} and Diego Patino",

note = "Publisher Copyright: {\textcopyright}2016 IEEE; 2016 IEEE Conference on Control Applications, CCA 2016 ; Conference date: 19-09-2016 Through 22-09-2016",

year = "2016",

month = oct,

day = "10",

doi = "10.1109/CCA.2016.7587985",

language = "English",

series = "2016 IEEE Conference on Control Applications, CCA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2016 IEEE Conference on Control Applications, CCA 2016",

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Devia, CA & Patino, D 2016, Multivariable optimal point tracking algorithm. En 2016 IEEE Conference on Control Applications, CCA 2016., 7587985, 2016 IEEE Conference on Control Applications, CCA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1294-1299, 2016 IEEE Conference on Control Applications, CCA 2016, Buenos Aires, Argentina, 19/09/16. https://doi.org/10.1109/CCA.2016.7587985

Multivariable optimal point tracking algorithm. / Devia, Carlos Andrés; Patino, Diego.
2016 IEEE Conference on Control Applications, CCA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1294-1299 7587985 (2016 IEEE Conference on Control Applications, CCA 2016).

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

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T1 - Multivariable optimal point tracking algorithm

AU - Devia, Carlos Andrés

AU - Patino, Diego

PY - 2016/10/10

Y1 - 2016/10/10

N2 - This paper presents a method to track local optimal points in unknown multivariable non-convex time-varying cost functions. The main contribution of this method is that is model-free, the tracking property is proven and an error bound is given. Next several simulations are performed. An example of Maximum Power Point Tracking in Photovoltaic Panels is presented including the successful tracking of the Maximum Power Point. Finally conclusions and further work are discussed.

AB - This paper presents a method to track local optimal points in unknown multivariable non-convex time-varying cost functions. The main contribution of this method is that is model-free, the tracking property is proven and an error bound is given. Next several simulations are performed. An example of Maximum Power Point Tracking in Photovoltaic Panels is presented including the successful tracking of the Maximum Power Point. Finally conclusions and further work are discussed.

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DO - 10.1109/CCA.2016.7587985

M3 - Conference contribution

AN - SCOPUS:85080456364

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SP - 1294

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BT - 2016 IEEE Conference on Control Applications, CCA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Conference on Control Applications, CCA 2016

Y2 - 19 September 2016 through 22 September 2016

ER -

Multivariable optimal point tracking algorithm

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