Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm

Julio Viola; Jose Restrepo; Jose Aller

doi:10.1109/ISIE.2015.7281513

Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm

Julio Viola, Jose Restrepo, Jose Aller

Grupo de Investigación en Energías (GIE)

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

Resumen

This paper presents the use of a training algorithm based on a Lyapunov function approach applied to a stator current controller based on a state variable description of the induction machine plus a reference model. The results obtained with the proposed controller are compared with a previously reported method based on a Nonlinear Auto-Regressive Moving Average with eXogenous inputs (NARMAX) description of the induction machine. The proposed Lyapunov based training algorithm is used to ensure convergence of the weights towards a global minimum in the error function. Real time simulations employing a DSP based test bench are used to test the validity of the algorithms and the results are verified by a practical implementation of these controllers.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	468-475
Número de páginas	8
ISBN (versión digital)	9781467375542
DOI	https://doi.org/10.1109/ISIE.2015.7281513
Estado	Publicada - 28 sep. 2015
Evento	24th IEEE International Symposium on Industrial Electronics, ISIE 2015 - Buzios, Rio de Janeiro, Brasil Duración: 3 jun. 2015 → 5 jun. 2015

Serie de la publicación

Nombre	IEEE International Symposium on Industrial Electronics
Volumen	2015-September

Conferencia

Conferencia	24th IEEE International Symposium on Industrial Electronics, ISIE 2015
País/Territorio	Brasil
Ciudad	Buzios, Rio de Janeiro
Período	3/06/15 → 5/06/15

Nota bibliográfica

Publisher Copyright:
© 2015 IEEE.

Acceder al documento

10.1109/ISIE.2015.7281513

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Viola, J., Restrepo, J., & Aller, J. (2015). Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm. En Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015 (pp. 468-475). Artículo 7281513 (IEEE International Symposium on Industrial Electronics; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2015.7281513

Viola, Julio ; Restrepo, Jose ; Aller, Jose. / Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm. Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 468-475 (IEEE International Symposium on Industrial Electronics).

@inproceedings{8229ecaf47fb4338a85124165abacab3,

title = "Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm",

abstract = "This paper presents the use of a training algorithm based on a Lyapunov function approach applied to a stator current controller based on a state variable description of the induction machine plus a reference model. The results obtained with the proposed controller are compared with a previously reported method based on a Nonlinear Auto-Regressive Moving Average with eXogenous inputs (NARMAX) description of the induction machine. The proposed Lyapunov based training algorithm is used to ensure convergence of the weights towards a global minimum in the error function. Real time simulations employing a DSP based test bench are used to test the validity of the algorithms and the results are verified by a practical implementation of these controllers.",

keywords = "Backpropagation, Induction motor drives, Lyapunov methods, Neural Networks",

author = "Julio Viola and Jose Restrepo and Jose Aller",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 24th IEEE International Symposium on Industrial Electronics, ISIE 2015 ; Conference date: 03-06-2015 Through 05-06-2015",

year = "2015",

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day = "28",

doi = "10.1109/ISIE.2015.7281513",

language = "English",

series = "IEEE International Symposium on Industrial Electronics",

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Viola, J, Restrepo, J & Aller, J 2015, Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm. En Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015., 7281513, IEEE International Symposium on Industrial Electronics, vol. 2015-September, Institute of Electrical and Electronics Engineers Inc., pp. 468-475, 24th IEEE International Symposium on Industrial Electronics, ISIE 2015, Buzios, Rio de Janeiro, Brasil, 3/06/15. https://doi.org/10.1109/ISIE.2015.7281513

Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm. / Viola, Julio; Restrepo, Jose; Aller, Jose.
Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 468-475 7281513 (IEEE International Symposium on Industrial Electronics; Vol. 2015-September).

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

TY - GEN

T1 - Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm

AU - Viola, Julio

AU - Restrepo, Jose

AU - Aller, Jose

PY - 2015/9/28

Y1 - 2015/9/28

N2 - This paper presents the use of a training algorithm based on a Lyapunov function approach applied to a stator current controller based on a state variable description of the induction machine plus a reference model. The results obtained with the proposed controller are compared with a previously reported method based on a Nonlinear Auto-Regressive Moving Average with eXogenous inputs (NARMAX) description of the induction machine. The proposed Lyapunov based training algorithm is used to ensure convergence of the weights towards a global minimum in the error function. Real time simulations employing a DSP based test bench are used to test the validity of the algorithms and the results are verified by a practical implementation of these controllers.

AB - This paper presents the use of a training algorithm based on a Lyapunov function approach applied to a stator current controller based on a state variable description of the induction machine plus a reference model. The results obtained with the proposed controller are compared with a previously reported method based on a Nonlinear Auto-Regressive Moving Average with eXogenous inputs (NARMAX) description of the induction machine. The proposed Lyapunov based training algorithm is used to ensure convergence of the weights towards a global minimum in the error function. Real time simulations employing a DSP based test bench are used to test the validity of the algorithms and the results are verified by a practical implementation of these controllers.

KW - Backpropagation

KW - Induction motor drives

KW - Lyapunov methods

KW - Neural Networks

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U2 - 10.1109/ISIE.2015.7281513

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M3 - Conference contribution

AN - SCOPUS:84947241687

T3 - IEEE International Symposium on Industrial Electronics

SP - 468

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BT - Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th IEEE International Symposium on Industrial Electronics, ISIE 2015

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Viola J, Restrepo J, Aller J. Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm. En Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 468-475. 7281513. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE.2015.7281513

Current controller for induction motor using an Artificial Neural Network trained with a Lyapunov based algorithm

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