An enhanced operator-oriented genetic search algorithm used to solve nonlinear dynamic control problems

Author

Stumpf, Jeffrey D. ; Feng, Xin ; Kelnhofer, Richard W.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

Volume

2

fYear

1995

fDate

22-25 Oct 1995

Firstpage

1778

Abstract

An order-based enhanced operator oriented genetic algorithm (EOOGA) is used to find solutions to nonlinear dynamic control type problems. Solutions are expressed as a sequence of decision vectors that result in near optimal performance. The algorithm is tested on a satellite trajectory problem. The results are compared to randomly generated solutions, and solutions obtained using dynamic programming

Keywords

artificial satellites; attitude control; decision theory; discrete time systems; dynamic programming; genetic algorithms; nonlinear dynamical systems; optimal control; search problems; decision vectors; dynamic programming; near optimal performance; nonlinear dynamic control problems; order-based enhanced operator oriented genetic algorithm; satellite trajectory problem; Design optimization; Dynamic programming; Evolutionary computation; Genetic algorithms; Optimal control; Orbital robotics; Regulators; Robustness; Satellites; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.538033

Filename

538033