An adaptive non-zero mean damping model for trajectory tracking of hypersonic glide vehicles

Yunpeng Cheng, Xiaodong Yan, Shuo Tang, Manqiao Wu, Chaoyong Li

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

In this paper, we proposed an adaptive non-zero mean damping oscillation model, aiming to solve the trajectory tracking problem of hypersonic glide vehicles (HGVs). To this end, an adaptive non-zero mean damping oscillation model (ANMDO) is established based on the maneuver patterns of HGVs, the proposed model consists of the mean and maneuvering components of HGV's accelerations. In particular, a sine autocorrelation random process is applied to model the mean component, while a first-order Markov process is introduced to compensate its maneuvering counterpart that is taken as the perturbation. Moreover, we proceed to introduce the Kalman filter to estimate the trajectory, while the dynamic errors of the proposed model are analytically developed. Simulation results verified that the proposed model can achieve a better tracking accuracy and reasonable convergence compared with the conventional sine correlation model and the Singer model.

Original languageEnglish
Article number106529
JournalAerospace Science and Technology
Volume111
DOIs
StatePublished - Apr 2021

Keywords

  • Hypersonic glide vehicle
  • Kalman filtering
  • Maneuvering model
  • Target tracking

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