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Fast Finite-State Predictive Current Control of Electric Drives

Authors: Manuel R. Arahal; Federico Barrero; Manuel G. Satué; Mario Bermudez;

Fast Finite-State Predictive Current Control of Electric Drives

Abstract

This work presents a novel optimization method for the implementation of finite-state modelbased predictive current controllers in electrical drives. The proposal avoids the usual exhaustive search to find the control action, reducing the computational burden. The method is based on physical considerations of the power converter voltage vectors and is easy to implement on digital signal processors. The proposal is applied to a five-phase induction machine. Experimental results are compared with those obtained by a standard model-based controller, showing the feasibility of the proposal and the improvements in terms of sampling time reduction and control accuracy.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Country
Spain
Keywords

sampling time reduction, Multi-phase systems, Voltage vectors, Induction machines, voltage vectors, Electrical engineering. Electronics. Nuclear engineering, Predictive control, multi-phase systems, predictive control, Sampling time reduction, TK1-9971

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
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gold
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