Applying RBF neural network to missile control system parameter optimization

Supeng Zhu, Wenxing Fu, Jun Yang, Jianjun Luo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationCAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
Pages337-340
Number of pages4
DOIs
StatePublished - 2010
Event2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010 - Wuhan, China
Duration: 6 Mar 20107 Mar 2010

Publication series

NameCAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
Volume2

Conference

Conference2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010
Country/TerritoryChina
CityWuhan
Period6/03/107/03/10

Keywords

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

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