Year: 2026
Type: preprint
Abstract: Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To compensate for these effects, we propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat. We leverage this preserved flatness to design a computationally efficient feedback linearization controller that is easily tunable with linear control techniques and cancels aerodynamic disturbances via feedforward compensation. Hardware experiments demonstrate our framework reduces average tracking errors by 31% compared to nominal baselines. Crucially, our lightweight approach matches the tracking performance of state-of-the-art nonlinear model predictive control (NMPC) while requiring an order of magnitude less computation. We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks. The video of our physical experiments can be found at https://www.youtube.com/watch?v=uF26IkRFQMk
Source: arXiv (Cornell University)
Institution: University of Pennsylvania
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1523888516, G3881636599, Collaborative Research: SLES: Bridging offline design and online adaptation in safe learning-enabled systems, G6098521345, Collaborative Research: Scalable & Communication Efficient Learning-Based Distributed Control, CAREER: Towards a Theory of Robust Learning & Control for Safety-Critical Autonomous Systems, G776205417
OpenAlex ID: W7168410686
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