DocumentCode
1090401
Title
A Genetic Algorithm Convergence and Models for Eigenstructure Assignment via Linear Quadratic Regulator (LQR)
Author
Viana Fonseca, J. ; Silva Abreu, I. ; Moraes Rego, P.H. ; de Paulo Melo Wolff, M. ; Silva, O.F.
Volume
6
Issue
1
fYear
2008
fDate
3/1/2008 12:00:00 AM
Firstpage
1
Lastpage
9
Abstract
In this article, models and a genetic algorithm convergence analysis method for the LQR weight matrices determination are presented. The control goal is the eigenstructures allocation in multivariable dynamic systems imposed by the optimal control law, and the analysis procedure goal is the speeding up of the convergence through metrics based upon first and second order statistical momentum. The proposal performance is evaluated under the initial populations building point of view and the populations search process, whereas the convergence analysis leads to the development of rules based upon fitness function metrics. Tests for the genetic search models performance evaluation and for the control law efficiency are conducted with a 6th order dynamic model representing an aircraft.
Keywords
eigenvalues and eigenfunctions; genetic algorithms; linear quadratic control; matrix algebra; multivariable control systems; eigenstructure assignment; eigenstructures allocation; fitness function metrics; genetic algorithm convergence; linear quadratic regulator; multivariable dynamic systems; optimal control; Aircraft; Algorithm design and analysis; Control systems; Convergence; Genetic algorithms; Optimal control; Performance analysis; Proposals; Regulators; Testing; Eigenstructure assignment; convergence analysis; genetic algorithm; linear quadratic regulator;
fLanguage
English
Journal_Title
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher
ieee
ISSN
1548-0992
Type
jour
DOI
10.1109/TLA.2008.4461626
Filename
4461626
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