Adaptive Vector Field Guidance Without a Priori Knowledge of Course Dynamics and Wind

Journal article (2022)

Authors

X. Wang Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Spandan Roy International Institute of Information Technology
S. Fari Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
S. Baldi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 X. Wang, Spandan Roy, S. Fari, S. Baldi
DOI
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https://doi.org/10.1109/TMECH.2022.3160480
Navigation Uncertainty Aerodynamics Unknown dynamics Standards Autonomous aerial vehicles Orbits Adaptive guidance Adaptive sliding-mode control Adaptive systems Fixed-wing unmanned aerial vehicles (UAV) Vector field (VF)
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Published Date

2022

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 high maneuverability of fixed-wing unmanned aerial vehicles (UAVs) exposes these systems to several dynamical and parametric uncertainties, severely affecting the fidelity of modeling and causing limited guidance autonomy. This article shows enhanced autonomy via adaptation mechanisms embedded in the guidance law: a vector-field method is proposed that does not require a priori knowledge of the UAV course time constant, coupling effects, and wind amplitude/direction. Stability and performance are assessed using the Lyapunov theory. The method is tested on software-in-the loop and hardware-in-the-loop UAV platforms, showing that the proposed guidance law outperforms state-of-the-art guidance controllers and standard vector-field approaches in the presence of significant uncertainty.