Intelligent modeling of lateral aircraft dynamic based on flight test data

Zhigang Wang, Aijun Li, Yi Mi, Lihao Wang, Jingchao Sun

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

2 Scopus citations

Abstract

System identification technology is an important means of aircraft modeling. In order to make up for the shortcomings of traditional system identification technology, which are complicated in calculation and limited in applicability, this paper studies the aircraft intelligent system identification technology based on black and gray box modeling. The black box model which is based on the recurrent neural network establish the dynamic relationship between the input and output variables of the aircraft; while gray box model treats the motion equation and aerodynamic coefficients of the aircraft separately, assuming that the motion equation is known, and uses an extreme learning machine to establish the forward neural network model of the aerodynamic coefficients. Thus two intelligent models of the aircraft are established, the grey box model is mainly used for model prediction for a long period, and can be utilized for flight simulation, the black box model is mainly used for online model prediction, and can be applied for model predictive control.

Original languageEnglish
Title of host publicationProceeding - 2021 China Automation Congress, CAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2253-2257
Number of pages5
ISBN (Electronic)9781665426473
DOIs
StatePublished - 2021
Event2021 China Automation Congress, CAC 2021 - Beijing, China
Duration: 22 Oct 202124 Oct 2021

Publication series

NameProceeding - 2021 China Automation Congress, CAC 2021

Conference

Conference2021 China Automation Congress, CAC 2021
Country/TerritoryChina
CityBeijing
Period22/10/2124/10/21

Keywords

  • Aircraft modeling
  • extreme learning machine
  • intelligent system identification
  • recurrent neural network

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