Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2401.15508

Authors: Gu, Yuliang, Cheng, Sheng, Hovakimyan, Naira

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Aeronautics and Space Administration, Nuclear Safety and Security Commission

Awards: Collaborative Research: SLES: Guaranteed Tubes for Safe Learning across Autonomy Architectures, G3995854737, NSF-AoF: RI: Small: Safe Reinforcement Learning in Non-Stationary Environments With Fast Adaptation and Disturbance Prediction, G6860822721

OpenAlex ID: W4391376116

Cites:

Cited by: 3

Related to: 10