Applying RBF neural network to missile control system parameter optimization

Supeng Zhu; Wenxing Fu; Jun Yang; Jianjun Luo

doi:10.1109/CAR.2010.5456530

Applying RBF neural network to missile control system parameter optimization

Supeng Zhu, Wenxing Fu, Jun Yang, Jianjun Luo

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
Pages	337-340
Number of pages	4
DOIs	https://doi.org/10.1109/CAR.2010.5456530
State	Published - 2010
Event	2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010 - Wuhan, China Duration: 6 Mar 2010 → 7 Mar 2010

Publication series

Name	CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
Volume	2

Conference

Conference	2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010
Country/Territory	China
City	Wuhan
Period	6/03/10 → 7/03/10

Keywords

Missile attitude control
PID (proportional, integral, differential) control
RBF neural network

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10.1109/CAR.2010.5456530

Cite this

Zhu, S., Fu, W., Yang, J., & Luo, J. (2010). Applying RBF neural network to missile control system parameter optimization. In CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (pp. 337-340). Article 5456530 (CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics; Vol. 2). https://doi.org/10.1109/CAR.2010.5456530

@inproceedings{eb39830083e248d498482e663134be41,

title = "Applying RBF neural network to missile control system parameter optimization",

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 = "Missile attitude control, PID (proportional, integral, differential) control, RBF neural network",

author = "Supeng Zhu and Wenxing Fu and Jun Yang and Jianjun Luo",

year = "2010",

doi = "10.1109/CAR.2010.5456530",

language = "英语",

isbn = "9781424451937",

series = "CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics",

pages = "337--340",

booktitle = "CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics",

note = "2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010 ; Conference date: 06-03-2010 Through 07-03-2010",

}

Zhu, S, Fu, W, Yang, J & Luo, J 2010, Applying RBF neural network to missile control system parameter optimization. in CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics., 5456530, CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, vol. 2, pp. 337-340, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010, Wuhan, China, 6/03/10. https://doi.org/10.1109/CAR.2010.5456530

Applying RBF neural network to missile control system parameter optimization. / Zhu, Supeng; Fu, Wenxing; Yang, Jun et al.
CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics. 2010. p. 337-340 5456530 (CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Applying RBF neural network to missile control system parameter optimization

AU - Zhu, Supeng

AU - Fu, Wenxing

AU - Yang, Jun

AU - Luo, Jianjun

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Missile attitude control

KW - PID (proportional, integral, differential) control

KW - RBF neural network

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U2 - 10.1109/CAR.2010.5456530

DO - 10.1109/CAR.2010.5456530

M3 - 会议稿件

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SN - 9781424451937

T3 - CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics

SP - 337

EP - 340

BT - CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics

T2 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010

Y2 - 6 March 2010 through 7 March 2010

ER -

Zhu S, Fu W, Yang J, Luo J. Applying RBF neural network to missile control system parameter optimization. In CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics. 2010. p. 337-340. 5456530. (CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics). doi: 10.1109/CAR.2010.5456530

Applying RBF neural network to missile control system parameter optimization

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