  This paper presents an aggressive trajectory tracking method for a small
lightweight nano-quadrotor using nonlinear model predictive control (NMPC)
based on acados. Controlling a nano quadrotor for accurate trajectory tracking
at high speed in dynamic environments is challenging due to complex aerodynamic
forces that introduce significant disturbances and large positional tracking
errors. These aerodynamic effects are difficult to be identified and require
feedback control that compensates for them in real time. NMPC allows the
nano-quadrotor to control its motion in real time based on onboard sensor
measurements, making it well-suited for tasks such as aggressive maneuvers and
navigation in complex and dynamic environments. The software package acados
enables the implementation of the NMPC algorithm on embedded systems, which is
particularly important for nano-quadrotor due to its limited computational
resources. Our autonomous navigation system is developed based on an AI-deck
that is a GAP8-based parallel ultra-low power computing platform with onboard
sensors of a multi-ranger deck and a flow deck. The proposed method of
NMPC-based trajectory tracking control is tested in simulation and the results
demonstrate its effectiveness in trajectory tracking while considering the
dynamic environments. It is also tested on a real nano quadrotor hardware, 27-g
Crazyflie 2.1, with a customized MCU running embedded NMPC, in which accurate
trajectory tracking results are achieved in dynamic real-world environments.
