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. To address these challenges, 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.
Original language | English (US) |
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Pages (from-to) | 1765-1776 |
Number of pages | 12 |
Journal | Proceedings of Machine Learning Research |
Volume | 242 |
State | Published - 2024 |
Event | 6th Annual Learning for Dynamics and Control Conference, L4DC 2024 - Oxford, United Kingdom Duration: Jul 15 2024 → Jul 17 2024 |
Keywords
- Aerial Robotics
- Meta Learning
- Model Predictive Control
- Multi-task Learning
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability