Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots

TL;DR

This paper introduces a novel learning-enhanced MPC framework that combines $ ext{L}_1$ adaptive control to improve sample efficiency and uncertainty compensation, significantly enhancing quadrotor control performance during deployment.

Contribution

The paper presents a new MPC framework integrating $ ext{L}_1$ adaptive control to effectively handle uncertainties and improve sample efficiency in real-time robot control.

Findings

Enhanced control performance in simulations and experiments.

Effective compensation of matched and unmatched uncertainties.

Improved sample efficiency for dynamics model synthesis.

Abstract

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on an offline learning procedure to synthesize a dynamics model. This implies that uncertainties encountered by the robot during deployment are not accounted for in the learning process. On the other hand, learning-based MPC methods that learn dynamics models online are computationally expensive and often require a significant amount of data. To alleviate these shortcomings, we propose a novel learning-enhanced MPC framework that incorporates components from $\mathcal{L}_1$ adaptive control into learning-based MPC. This integration enables the accurate compensation of both matched and unmatched uncertainties in a sample-efficient way, enhancing the control performance during deployment. In our proposed framework, we present two variants and apply them to the control of a quadrotor system. Through simulations and physical experiments, we demonstrate that the proposed framework not only allows the synthesis of an accurate dynamics model on-the-fly, but also significantly improves the closed-loop control performance under a wide range of spatio-temporal uncertainties.