Title :
Genetic model reference adaptive control
Author :
Porter, La Moyne L, II ; Passino, Kevin M.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
Abstract :
A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. Motivated by the past GA´s use in off-line design of controllers we introduce a new approach to adaptive control, which we call “genetic model reference adaptive control” (GMRAC), where a GA manipulates a set (population) of parameterized controllers and selects the controller most capable of providing good performance for current plant operating conditions. The performance of GMRAC is evaluated for a cargo ship steering application
Keywords :
adaptive control; genetic algorithms; model reference adaptive control systems; search problems; ships; MRAC; cargo ship steering; complex spaces; genetic algorithm; genetic model reference adaptive control; parallel search; parameterized controllers; Adaptive control; Automatic control; Eigenvalues and eigenfunctions; Fuzzy control; Fuzzy reasoning; Fuzzy systems; Game theory; Genetic algorithms; Marine vehicles; Programmable control;
Conference_Titel :
Intelligent Control, 1994., Proceedings of the 1994 IEEE International Symposium on
Conference_Location :
Columbus, OH
Print_ISBN :
0-7803-1990-7
DOI :
10.1109/ISIC.1994.367814
