We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing environments. In these settings, learning is typically done during an initial (offline) design phase, where the vehicle is exposed to different environmental conditions and disturbances (e.g., a drone exposed to different winds) to collect training data. Our work is motivated by the observation that real-world disturbances fall into two categories: 1) those that can be directly monitored or controlled during training, which we call "manageable", and 2) those that cannot be directly measured or controlled (e.g., nominal model mismatch, air plate effects, and unpredictable wind), which we call "latent". Imprecise modeling of these effects can result in degraded control performance, particularly when latent disturbances continuously vary. This paper presents the Hierarchical Meta-learning-based Adaptive Controller (HMAC) to learn and adapt to such multi-source disturbances. Within HMAC, we develop two techniques: 1) Hierarchical Iterative Learning, which jointly trains representations to caption the various sources of disturbances, and 2) Smoothed Streaming Meta-Learning, which learns to capture the evolving structure of latent disturbances over time (in addition to standard meta-learning on the manageable disturbances). Experimental results demonstrate that HMAC exhibits more precise and rapid adaptation to multi-source disturbances than other adaptive controllers.

翻译：我们研究如何设计基于学习的自适应控制器，使其能在变化环境中实现快速且精确的在线适应。在此类场景中，学习通常发生在初始（离线）设计阶段——将飞行器暴露于不同环境条件和干扰（例如无人机受不同风力影响）以收集训练数据。本研究的动机源于以下观察：真实世界中的干扰可分为两类：1) 可在训练过程中直接监控或控制的干扰，我们称之为“可控干扰”；2) 无法直接测量或控制的干扰（如标称模型失配、气垫效应和不可预测风力），我们称之为“潜在干扰”。对这些效应的不精确建模会导致控制性能下降，尤其是当潜在干扰持续变化时。本文提出基于分层元学习的自适应控制器（HMAC），用于学习并适应此类多源干扰。HMAC包含两种技术：1) 分层迭代学习——联合训练表征以描述不同干扰源；2) 平滑流式元学习——学习捕获潜在干扰随时间演化的结构（在可控干扰的标准元学习基础上）。实验结果表明，相比其他自适应控制器，HMAC对多源干扰表现出更精确且更快速的适应能力。