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EMO
2006
Springer

Multi-objective Pole Placement with Evolutionary Algorithms

9 years 1 months ago
Multi-objective Pole Placement with Evolutionary Algorithms
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 complexvalued 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 H2/H control problem. Key words: Multi-objective control; Pole placement; Evolutionary Algorithms.
Gustavo Sánchez, Minaya Villasana, Miguel S
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where EMO
Authors Gustavo Sánchez, Minaya Villasana, Miguel Strefezza
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