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
Link To Document :
بازگشت