Development of a new controller to optimize operation of a 150 watt, Half-Bridge DC-DC converter

Jafari, M; Malekjamshidi, Z; Zhu, JG

Development of a new controller to optimize operation of a 150 watt, Half-Bridge DC-DC converter

Jafari, M

Malekjamshidi, Z

Zhu, JG

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Publication Type:: Conference Proceeding
Citation:: 2013 International Conference on Electrical Machines and Systems, ICEMS 2013, 2013, pp. 1583 - 1588
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Jafari, M https://orcid.org/0000-0002-8521-8636	en_US
dc.contributor.author	Malekjamshidi, Z https://orcid.org/0000-0002-5951-620X	en_US
dc.contributor.author	Zhu, JG https://orcid.org/0000-0002-9763-4047	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 International Conference on Electrical Machines and Systems, ICEMS 2013, 2013, pp. 1583 - 1588	en_US
dc.identifier.isbn	9781479914470	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27273
dc.description.abstract	This paper presents simulation, design methodology and implementation of a Half Bridge DC-DC Converter using an optimized multi-layer feed-forward Artificial Neural Network (ANN). The ANN controller provides an appropriate control signal to keep the output voltage of the converter stable. It was trained, using back-propagation algorithm according to an ideal MATLAB SIMULINK model of the converter. The Ideal model is run 'off-line' to generate a record of 'perfect' control signals in response to input and output perturbations. The recorded data then are used as an 'off-line' training set for a Neural Network controller. A network pruning algorithm is used to reduce the size of controller and an online training process performed to fine-tune the controller parameters as well. Experimental results show that the ANN controller can guarantee an acceptable load and line regulation and provide improved performances. The proposed controller can be readily expanded to other topologies of DC converters. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 International Conference on Electrical Machines and Systems, ICEMS 2013	en_US
dc.relation.isbasedon	10.1109/ICEMS.2013.6713325	en_US
dc.title	Development of a new controller to optimize operation of a 150 watt, Half-Bridge DC-DC converter	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Busan,Korea	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents simulation, design methodology and implementation of a Half Bridge DC-DC Converter using an optimized multi-layer feed-forward Artificial Neural Network (ANN). The ANN controller provides an appropriate control signal to keep the output voltage of the converter stable. It was trained, using back-propagation algorithm according to an ideal MATLAB SIMULINK model of the converter. The Ideal model is run 'off-line' to generate a record of 'perfect' control signals in response to input and output perturbations. The recorded data then are used as an 'off-line' training set for a Neural Network controller. A network pruning algorithm is used to reduce the size of controller and an online training process performed to fine-tune the controller parameters as well. Experimental results show that the ANN controller can guarantee an acceptable load and line regulation and provide improved performances. The proposed controller can be readily expanded to other topologies of DC converters. © 2013 IEEE.

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http://hdl.handle.net/10453/27273