Addressing Unmodeled Path-Following Dynamics via Adaptive Vector Field: A UAV Test Case

Stefano Fari, Ximan Wang, Spandan Roy, Simone Baldi

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
14 Downloads (Pure)

Abstract

The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.

Original languageEnglish
Pages (from-to)1613-1622
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume56
Issue number2
DOIs
Publication statusPublished - 2020

Keywords

  • Adaptive vector field
  • fixed-wing unmanned aerial vehicles (UAV)
  • path-following
  • unmodeled course angle dynamics

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