Authors:
Gustavo Sánchez
;
Miguel Strefezza
and
Minaya Villasana
Affiliation:
Universidad Simón Bolívar, Venezuela
Keyword(s):
Multi-objective control, Genetic algorithms, LMIs, Pole placement, COMPleib.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Representation Techniques
;
Soft Computing
Abstract:
One of the most relevant problems for control engineers is the so-called “mixed H2/H∞”. To solve it, different convexifying strategies became popular in the later 1990s, mainly based on Linear Matrix Inequalities (LMIs). On the other hand, genetic algorithms have also been applied for H2/H∞ synthesis. Indeed, several authors agree that they are able to find good solutions to this important control problem. However, in most of the published papers, only low-order SISO models have been considered. In the present paper a LMI-based algorithm is compared against a genetic algorithm, with respect to three performance indicators: Set Coverage, Maximum Distance and Efficient Set Spacing. Five open-loop MIMO models extracted from COMPleib are studied, for which the degree varies between 5 and 10. Based on numerical results, the genetic algorithm is not able to improve LMI solutions for problems with more than 42 variables, restricted to a budget of 20.000 function evaluations.