Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control
  in Challenging Winds
Yuliang Gu Sheng Cheng Naira Hovakimyan
Quadrotors are increasingly used in the evolving field of aerial robotics for
their agility and mechanical simplicity. However, inherent uncertainties, such
as aerodynamic effects coupled with quadrotors' operation in dynamically
changing environments, pose significant challenges for traditional, nominal
model-based control designs. We propose a multi-task meta-learning method
called Encoder-Prototype-Decoder (EPD), which has the advantage of effectively
balancing shared and distinctive representations across diverse training tasks.
Subsequently, we integrate the EPD model into a model predictive control
problem (Proto-MPC) to enhance the quadrotor's ability to adapt and operate
across a spectrum of dynamically changing tasks with an efficient online
implementation. We validate the proposed method in simulations, which
demonstrates Proto-MPC's robust performance in trajectory tracking of a
quadrotor being subject to static and spatially varying side winds.