Neural network controllers for switch mode systems: off line training by an 'ideal controller' data set

Natalia Kodner, Daniel Adar, Sam Ben-Yaakov

Research output: Contribution to conferencePaperpeer-review

Abstract

A novel method is proposed for designing Neural Network Controllers (NNC) for switch mode systems. The method applies a new concept: 'The Ideal Controller' which is run 'off-line' to generate a record of 'perfect' control signals in response to input and output perturbations. The record is then used as an 'off-line' training set for a Neural Network controller. The advantages of the proposed method are three fold: (a) the training set is the best possible, (b) training is done 'off-line' by simulation and (c) there is no need to derive or guess the control law. The present study demonstrates by simulation the potential excellent performance of a Neural Network Controllers for DC-DC Switch-Mode converters when trained by the proposed methodology. The proposed methodology can be readily expanded to other Switch-Mode systems such as inverters and power factor conditioners.

Original languageEnglish
Pages4.3.3/1-5
StatePublished - 1 Jan 1995
EventProceedings of the 18th Convention of Electrical and Electronics Engineers in Israel - Tel Aviv, Isr
Duration: 7 Mar 19958 Mar 1995

Conference

ConferenceProceedings of the 18th Convention of Electrical and Electronics Engineers in Israel
CityTel Aviv, Isr
Period7/03/958/03/95

ASJC Scopus subject areas

  • Engineering (all)

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