Switched Reluctance Machine fuzzy modeling applied on a MRAC scheme

Arnaldo Matute; Julio Viola; José Restrepo; José Manuel Aller; Flavio Quizhpi

doi:10.1109/LASCAS.2015.7250442

Switched Reluctance Machine fuzzy modeling applied on a MRAC scheme

Arnaldo Matute
, Julio Viola
, José Restrepo
, José Manuel Aller
, Flavio Quizhpi

Research Group in Energy (GIE)

Research output: Contribution to conference › Paper

Abstract

© 2015 IEEE. This paper proposes an approach for modeling a Switched Reluctance Machine with fuzzy logic. The proposed model is verified under a Model Reference Adaptive Control scheme. Calculating Fuzzy Inference Systems instead of the nonlinear terms of state equations decreases the model complexity and computing costs, which is important for the MRAC scheme, and provides a mathematical model for adaptive control. Amodel architecture is presented where linear equations interact with a Fuzzy Inference System. An angular speed control experiment was successfully simulated using the resulting fuzzy model as reference.

Original language	English
DOIs	https://doi.org/10.1109/LASCAS.2015.7250442
State	Published - 1 Jan 2015
Event	2015 IEEE 6th Latin American Symposium on Circuits and Systems, LASCAS 2015 - Conference Proceedings - Duration: 1 Jan 2015 → …

Conference

Conference	2015 IEEE 6th Latin American Symposium on Circuits and Systems, LASCAS 2015 - Conference Proceedings
Period	1/01/15 → …

CACES Knowledge Areas

317A Electricity and Energy

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10.1109/LASCAS.2015.7250442

Cite this

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AU - Viola, Julio

AU - Restrepo, José

AU - Aller, José Manuel

AU - Quizhpi, Flavio

PY - 2015/1/1

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AB - © 2015 IEEE. This paper proposes an approach for modeling a Switched Reluctance Machine with fuzzy logic. The proposed model is verified under a Model Reference Adaptive Control scheme. Calculating Fuzzy Inference Systems instead of the nonlinear terms of state equations decreases the model complexity and computing costs, which is important for the MRAC scheme, and provides a mathematical model for adaptive control. Amodel architecture is presented where linear equations interact with a Fuzzy Inference System. An angular speed control experiment was successfully simulated using the resulting fuzzy model as reference.

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ER -

Switched Reluctance Machine fuzzy modeling applied on a MRAC scheme

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