Addressing Unmodeled Path-Following Dynamics via Adaptive Vector Field

A UAV Test Case

Journal article (2020)

Authors

S. Fari Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Politecnico di Milano, Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
X. Wang Team Bart De Schutter - Mechanical, Maritime and Materials Engineering , China State Shipbuilding Corporation
S. Roy Ship Design, Production and Operations - Mechanical, Maritime and Materials Engineering
S. Baldi Southeast University, Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 S. Fari, X. Wang, S. Roy, S. Baldi
DOI
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https://doi.org/10.1109/TAES.2019.2925487
Path-following Fixed-wing unmanned aerial vehicles (UAV) Adaptive vector field Unmodeled course angle dynamics
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

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.