Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems

ArXi:2605.22513v1 Announce Type: new In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often challenging, our objective is to design optimal controllers using limited target system data. Meta-learning provides a promising paradigm by leveraging offline data from source systems (systems sharing structural similarities with the target system) to accelerate