Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions

Understand

Realtime model learning proves challenging for complex dynamical systems, such as drones flying in variable wind conditions.

• Machine learning technique such as deep neural networks have high representation power but is often too slow to update onboard.
• On the other hand, adaptive control relies on simple linear parameter models can update as fast as the feedback control loop.
• We propose an online composite adaptation method that treats outputs from a deep neural network as a set of basis functions capable of representing different wind conditions.