Online Adaptation for Flying Quadrotors in Tight Formations
This work solves the problem of maintaining stable, close-proximity flight for teams of quadrotors, which is incremental as it builds on existing MPC methods with adaptive learning components.
The paper tackles the challenge of flying quadrotors in tight formations by addressing destabilizing aerodynamic wake interactions, presenting L1 KNODE-DW MPC, an adaptive control framework that enables accurate trajectory tracking and adaptation to time-varying effects, outperforming MPC baselines in experiments with three-quadrotor teams.
The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as the team. Furthermore, these aerodynamic effects are highly nonlinear and fast-paced, making them difficult to model and predict. To overcome these challenges, we present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework that allows individual quadrotors to accurately track trajectories while adapting to time-varying aerodynamic interactions during formation flights. We evaluate L1 KNODE-DW MPC in two different three-quadrotor formations and show that it outperforms several MPC baselines. Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight. These findings show that the L1 adaptive module compensates for unmodeled disturbances most effectively when paired with an accurate dynamics model. A video showcasing our framework and the physical experiments is available here: https://youtu.be/9QX1Q5Ut9Rs