End-to-end Neural Network Based Quadcopter control

TL;DR

This paper introduces G&CNets, an end-to-end neural network controller for quadcopters that learns energy-optimal flight commands and adapts to unmodeled external moments, improving energy efficiency and robustness without relying on inner-loop controllers.

Contribution

The paper presents the first end-to-end neural network controller for quadcopters that directly maps states to control commands and incorporates adaptive learning to handle external disturbances.

Findings

Energy-optimal hover-to-hover flights achieved.

Adaptive control improves robustness against external moments.

Outperforms differential-flatness-based controller in waypoint flights.

Abstract

Developing optimal controllers for aggressive high-speed quadcopter flight poses significant challenges in robotics. Recent trends in the field involve utilizing neural network controllers trained through supervised or reinforcement learning. However, the sim-to-real transfer introduces a reality gap, requiring the use of robust inner loop controllers during real flights, which limits the network's control authority and flight performance. In this paper, we investigate for the first time, an end-to-end neural network controller, addressing the reality gap issue without being restricted by an inner-loop controller. The networks, referred to as G\&CNets, are trained to learn an energy-optimal policy mapping the quadcopter's state to rpm commands using an optimal trajectory dataset. In hover-to-hover flights, we identified the unmodeled moments as a significant contributor to the reality gap. To mitigate this, we propose an adaptive control strategy that works by learning from optimal trajectories of a system affected by constant external pitch, roll and yaw moments. In real test flights, this model mismatch is estimated onboard and fed to the network to obtain the optimal rpm command. We demonstrate the effectiveness of our method by performing energy-optimal hover-to-hover flights with and without moment feedback. Finally, we compare the adaptive controller to a state-of-the-art differential-flatness-based controller in a consecutive waypoint flight and demonstrate the advantages of our method in terms of energy optimality and robustness.