Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics

TL;DR

This paper develops a nonlinear model predictive control approach for fixed-wing UAVs that uses identified control-augmented dynamics to improve path following and stability in challenging conditions.

Contribution

It introduces a method for modeling and identifying control-augmented dynamics and applies a high-level NMPC for fixed-wing UAVs with experimental validation.

Findings

Successful path following in high winds

Effective airspeed stabilization and path control

Robustness to motor failure scenarios

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.