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Found 4 papers, 3 papers with code
Neural Lander: Stable Drone Landing Control using Learned Dynamics
To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.
Meta-Adaptive Nonlinear Control: Theory and Algorithms
We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.
