Paper Info
Title
Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics
Abstract
As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic platforms. Considering typical low-level attitude stabilization available on OTS flight controllers, this paper first develops an approach for modeling and identification of the control augmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV). A high-level Nonlinear Model Predictive Controller (NMPC) is subsequently formulated for simultaneous airspeed stabilization, path following, and soft constraint handling, using the identified model for horizon propagation. The approach is explored in several exemplary flight experiments including path following of helix and connected Dubins Aircraft segments in high winds as well as a motor failure scenario. The cost function, insights on its weighting, and additional soft constraints used throughout the experimentation are discussed.
Year
DOI
Venue
2018
10.1109/ICUAS.2018.8453377
2018 International Conference on Unmanned Aircraft Systems (ICUAS)
Keywords
Field
DocType
Nonlinear model predictive guidance,off-the-shelf autopilots,drone market,fixed-wing aerial robotic platforms,low-level attitude stabilization,OTS flight controllers,flight experiments,fixed-wing UAV,motion planning,unmanned aerial vehicle,high-level nonlinear model predictive controller,airspeed stabilization,soft constraint handling,path following,horizon propagation,Dubins Aircraft segments,motor failure scenario,cost function
Control theory,Wing,Weighting,Suite,Horizon,Control engineering,Autopilot,Engineering,Airspeed,Nonlinear model
Journal
Volume
ISSN
ISBN
abs/1802.02624
2373-6720
978-1-5386-1355-9
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Thomas Stastny1267.63
Roland Siegwart27640551.49