LQG based model predictive control for gust load alleviation

Xiang Liu, Qin Sun*, Jonathan Cooper

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

17 Citations (Scopus)
149 Downloads (Pure)

Abstract

A linear quadratic Gaussian (LQG) based model predictive control (MPC) method is proposed to alleviate the dynamic gust loads of flexible aircrafts flying through turbulence, utilizing look-ahead information of the turbulence via light detection and ranging (LIDAR) systems or on board alpha probe. The new method features both infinite prediction horizon and infinite control horizon. The forepart of the infinite control sequence consists of a few online optimized variables, and the rest are outputs of an LQG controller, designed offline using an improved LQG method. The advantages of the proposed method are twofold. Firstly, the stability property of the controlled system is improved due to application of the infinite prediction horizon and the LQG controller. Secondly, adoption of an infinite control horizon not only improves the control performance, but also greatly reduces the number of online optimized control variables whilst retaining control performance. Furthermore, a technique to tackle the effects of control delay is also designed. The effectiveness and advantages of the proposed approach are demonstrated through numerical results using a general transport aircraft model.
Original languageEnglish
Pages (from-to)499-509
Number of pages11
JournalAerospace Science and Technology
Volume71
Early online date9 Oct 2017
DOIs
Publication statusPublished - Dec 2017

Keywords

  • Control delay
  • Gust load alleviation
  • Linear quadratic Gaussian method
  • Model predictive control

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