  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.
