Application of Iterative Learning Genetic Algorithms for PID Parameters Auto-Optimization of Missile controller

Author

Yunan, Hu ; Qu, Bin

Author_Institution

Dept. of Control Eng., Naval Aeronaut. Eng. Acad., Shandong

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3435

Lastpage

3439

Abstract

Aiming at the problem of a great deal of blindly searching the proportional, differential, integral parameters in the process of designing PID controller, the new algorithm combining genetic algorithm with iterative learning algorithm is named as an iterative learning genetic algorithm (ILGA), which can be used to optimize three controller parameters, thus the optimal parameters can be achieved swiftly by virtue of less iterative learning times, and the design of the PID controller is simplified. As the simulation results shown, the effectiveness of the method is verified in optimizing the PID controller parameters

Keywords

adaptive control; control system synthesis; genetic algorithms; iterative methods; learning systems; missile control; three-term control; PID controller design; PID parameter autooptimization; iterative learning genetic algorithms; missile controller; Algorithm design and analysis; Design optimization; Genetic algorithms; Iterative algorithms; Missiles; Optimal control; Pi control; Process design; Proportional control; Three-term control; ILGA; PID controller; genetic algorithms; iterative learning control; parameters auto-optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713006

Filename

1713006