Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing

This paper proposes a two-level, data-driven, digital twin concept for the
autonomous landing of aircraft, under some assumptions. It features a
digital twin instance for model predictive control; and an innovative,
real-time, digital twin prototype for fluid-structure interaction and
flight dynamics to inform it. The latter digital twin is based on the
linearization about a pre-designed glideslope trajectory of a
high-fidelity, viscous, nonlinear computational model for flight dynamics;
and its projection onto a low-dimensional approximation subspace to
achieve real-time performance, while maintaining accuracy. Its main
purpose is to predict in real-time, during flight, the state of an
aircraft and the aerodynamic forces and moments acting on it. Unlike
static lookup tables or regression-based surrogate models based on
steady-state wind tunnel data, the aforementioned real-time digital twin
prototype allows the digital twin instance for model predictive control to
be informed by a truly dynamic flight model, rather than a less accurate
set of steady-state aerodynamic force and moment data points. The paper
describes in detail the construction of the proposed two-level digital
twin concept and its verification by numerical simulation. It also reports
on its preliminary flight validation in autonomous mode for an
off-the-shelf unmanned aerial vehicle instrumented at Stanford University.