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LPV-MRAC Method for Aircraft with Structural Damage

  • Beihang University

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

Abstract

Abrupt structural damage poses significant challenges to the flight safety and flight quality of aircraft. In this paper, a direct model reference adaptive control (MRAC) method based on a linear parameter-varying (LPV) model is proposed to recover the control performance and flight quality of damaged aircraft. The design of the controller is based on a polytopic LPV model and the higher order singular value decomposition (HOSVD) model reduction method to reduce the computational cost of identifying the damaged aircraft model. The proposed controller also extends a previous MRAC method which assumes the input matrix is unchanged for different damage cases by identifying an uncertain input parameter online. The developed LPV-MRAC method is validated by simulation on NASA's generic transport model (GTM) with left wing tip loss damage and shown to be capable of compensating the damage effects and restoring the aircraft's control performance shortly after the damage. The flight quality of the closed-loop damaged aircraft is also evaluated by the C-star criterion and shown to be within Class I under the proposed controller.

Original languageEnglish
Title of host publicationProceedings of the 35th Chinese Control Conference, CCC 2016
EditorsJie Chen, Qianchuan Zhao, Jie Chen
PublisherIEEE Computer Society
Pages3262-3267
Number of pages6
ISBN (Electronic)9789881563910
DOIs
StatePublished - 26 Aug 2016
Event35th Chinese Control Conference, CCC 2016 - Chengdu, China
Duration: 27 Jul 201629 Jul 2016

Publication series

NameChinese Control Conference, CCC
Volume2016-August
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference35th Chinese Control Conference, CCC 2016
Country/TerritoryChina
CityChengdu
Period27/07/1629/07/16

Keywords

  • Aircraft with structural damage
  • Model reference adaptive control
  • Polytopic LPV models
  • Uncertainty of input matrix

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