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Genetic algorithm enabled computer-automated design of QFT control systems

Authors: W.-H. Chen; D.J. Ballance; W. Feng; Y. Li;

Genetic algorithm enabled computer-automated design of QFT control systems

Abstract

This paper first reviews the existing quantitative feedback theory (QFT) based design techniques and difficulties that a designer encounters. Based on this analysis and taking into account the practical constraints, an evolutionary computation enabled automatic design procedure is developed. It can be employed to provide an initial controller quickly on which manual loop-shaping and refinements are based on, or be used to further tune existing controllers or start from scratch. A design example against a MATLAB QFT Toolbox benchmark problem shows that this methodology not only automates loop-shaping but also improves design quality and, most usefully, improves the quality with a reduced order controller. The entire design is targeted towards global optimisation, including the prefilter selection.

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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!
22
Average
Top 10%
Top 10%
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