Abstract:
This will be a dry run of my upcoming plenary talk at the CDC. It focuses on nonlinear control systems in which only limited amounts of data can be exchanged between different parts of the system. In such systems, a control designer is faced not just with the task of developing a control algorithm, but also with deciding whether a given objective is achievable with the available data, or with characterizing the minimal data needed to meet the objective. In this context, we consider two specific problem settings. The first one concerns the tasks of state estimation and model detection with finite data rate, both of which we approach using a notion of entropy for dynamical systems. The second scenario is about observer design with guaranteed robustness to measurement errors, a task motivated in part by applications involving synchronization with limited data.
