Skip to main navigation Skip to search Skip to main content

A fuzzy-neural network sliding mode control for flexible spacecraft

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A fuzzy-neural network sliding mode control (FNNSMC) is proposed for the attitude stabilization of flexible spacecraft during large angle slew maneuver. The dynamic model of the spacecraft with flexible appendages is derived by Lagrange equation. To make the system state reach sliding mode surface in finite time, a sliding mode controller is designed, so the system is robustness against uncertainties and disturbances during the sliding phase. A fuzzy-neural network system is used to approximate the strong coupling nonlinear dynamics between rigid hub and flexible appendages, so that the elastic vibration of flexible spacecraft during maneuver is suppressed and the attitude of flexible spacecraft is stabilized. Simulation results show that not only high-precision attitude stabilization of flexible spacecraft is achieved, but also the elastic vibration of flexible spacecraft during maneuver is suppressed effectively.

Original languageEnglish
Title of host publication2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010
DOIs
StatePublished - 2010
Event2nd International Conference on Information Engineering and Computer Science, ICIECS 2010 - Wuhan, China
Duration: 25 Dec 201026 Dec 2010

Publication series

Name2nd International Conference on Information Engineering and Computer Science - Proceedings, ICIECS 2010

Conference

Conference2nd International Conference on Information Engineering and Computer Science, ICIECS 2010
Country/TerritoryChina
CityWuhan
Period25/12/1026/12/10

Keywords

  • Elastic vibration suppression
  • Flexible spacecraft
  • Fuzzy-neural network
  • Sliding mode control

Fingerprint

Dive into the research topics of 'A fuzzy-neural network sliding mode control for flexible spacecraft'. Together they form a unique fingerprint.

Cite this