Simon Genovese

University of Freiburg

Monday, July 27, 2026, 10:00 - 11:00

Building 102 - SR 02-012

In this thesis, we develop a model predictive contouring control (MPCC) algo rithm to control the flight of a unmanned aerial vehicle (UAV). Its performance is tested on a nano-quadcopter, namely the Crazyflie™ drone. It can be shown that this modern and nonlinear controller outperforms a linear tracking model predictive control (MPC) controller as well as a tracking nonlinear model predictive control (NMPC) controller. In combination with an onboard low-level attitude rate proportional integral differential (PID) controller, the MPCC tracks a given reference path with high accuracy and reproducibility. This thesis therefore contributes to the topic of drone racing, a promising field for the future, which drives technological advancements for scenarios like search and rescue, delivery or other time-critical use cases for UAVs. A video of the controller’s performance controlling the Crazyflie™ drone can be seen in https://youtu.be/JjgudZL0uIE .