Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds

TL;DR

This paper introduces Proto-MPC, a novel control framework combining meta-learning and model predictive control to improve quadrotor performance in challenging, wind-affected environments, validated through simulation results.

Contribution

It presents a new Encoder-Prototype-Decoder meta-learning approach integrated into MPC for adaptive quadrotor control in dynamic conditions.

Findings

Proto-MPC achieves robust trajectory tracking in windy conditions.

The method effectively adapts to varying wind disturbances.

Simulation results confirm improved control performance.

Abstract

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.