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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Multi-objective Pole Placement with Evolutionary Algorithms
verfasst von : Gustavo Sánchez, Minaya Villasana, Miguel Strefezza
Erschienen in: Evolutionary Multi-Criterion Optimization
Verlag: Springer Berlin Heidelberg
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Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complex-valued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective
$\mathcal{H}_{2}$
/
$\mathcal{H}_{\infty }$
control problem.