DocumentCode :
2243830
Title :
Applying RBF neural network to missile control system parameter optimization
Author :
Supeng, Zhu ; Wenxing, Fu ; Jun, Yang ; Jianjun, Luo
Author_Institution :
Coll. of Astronaut., Northwestern Polytech. Univ., Xi´´an, China
Volume :
2
fYear :
2010
fDate :
6-7 March 2010
Firstpage :
337
Lastpage :
340
Abstract :
The PID (proportional, integral, differential) control method is widely applied to missile attitude control. The usual empirical method for optimizing the three control parameters of Kp, Ki and Kd can not optimize them on line and in real time. The paper presents the PID parameter optimization method that uses RBF neural network, applies it to a missile´s longitudinal control system parameter optimization and verifies its effectiveness through numerical simulation. The simulation results demonstrate preliminarily that the use of RBF neural network can optimize the missile control system parameters on line and in real time.
Keywords :
attitude control; missile control; numerical analysis; optimal control; optimisation; radial basis function networks; three-term control; Kd control parameter; Ki control parameter; Kp control parameter; PID control; RBF neural network; attitude control; longitudinal control system; missile control system; numerical simulation; parameter optimization; proportional-integral-differential; radial basis function network; Control system synthesis; Control systems; Missiles; Neural networks; Numerical simulation; Optimization methods; Pi control; Proportional control; Real time systems; Three-term control; PID (proportional, integral, differential) control; RBF neural network; missile attitude control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location :
Wuhan
ISSN :
1948-3414
Print_ISBN :
978-1-4244-5192-0
Electronic_ISBN :
1948-3414
Type :
conf
DOI :
10.1109/CAR.2010.5456530
Filename :
5456530
Link To Document :
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