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.

翻译：近期数据可用性与可靠性的提升推动了面向机器人系统的基于学习的模型预测控制框架的快速发展。尽管此类框架相较于非学习方法实现了显著的性能提升，但许多框架仍依赖离线学习过程来合成动力学模型。这意味着机器人在部署过程中遭遇的不确定性并未被纳入学习过程。另一方面，在线学习动力学模型的基于学习的MPC方法计算开销大，且通常需要大量数据。为缓解这些不足，本文提出一种新颖的学习增强型MPC框架，该框架将$\mathcal{L}_1$自适应控制组件融入基于学习的MPC中。这种集成能以高样本效率的方式精确补偿匹配与非匹配不确定性，从而提升部署期间的闭环控制性能。在提出的框架中，我们给出两种变体，并将其应用于四旋翼飞行系统的控制。通过仿真与物理实验，我们证明该框架不仅能实时合成精确的动力学模型，还能在广泛的时空不确定性条件下显著改善闭环控制性能。