Adaptive Neural Network Quadrotor Trajectory Tracking Controller Tolerant to Propeller Damage

Abstract

Accurate trajectory tracking with quadrotors is a challenging task that requires a trade-off between accuracy and complexity to run onboard. Stateof- the-art adaptive controllers achieve impressive trajectory tracking results with slight performance degradation in varying winds or payloads but at the cost of computational complexity. This work proposes a lightweight combination of adaptive and neural control and shows its performance when flying with propeller damage. The neural architecture consists of offline learning of a condition-invariant representation of the aerodynamic forces through Deep Neural Networks. The second part consists of fast online adaptation using a composite adaptation law. We deploy this flight controller fully onboard the flight controller of the Parrot Bebop 1, showcasing its computational efficiency. The adaptive neural controller improves tracking performance by ≈ 60% over the nonlinear baseline, with minimal performance degradation of just ≈ 20% with increasing propeller damage.