  For many tasks, predictive path-following control can significantly improve
the performance and robustness of autonomous robots over traditional trajectory
tracking control. It does this by prioritizing closeness to the path over timed
progress along the path and by looking ahead to account for changes in the
path. We propose a novel predictive path-following approach that couples
feedforward linearization with path-based model predictive control. Our
approach has a few key advantages. By utilizing the differential flatness
property, we reduce the path-based model predictive control problem from a
nonlinear to a convex optimization problem. Robustness to disturbances is
achieved by a dynamic path reference, which adjusts its speed based on the
robot's progress. We also account for key system constraints. We demonstrate
these advantages in experiment on a quadrotor. We show improved performance
over a baseline trajectory tracking controller by keeping the quadrotor closer
to the desired path under nominal conditions, with an initial offset and under
a wind disturbance.
