Online Learning for Supervisory Switching Control
We study supervisory switching control for partially-observed linear dynamical systems. The objective is to identify and deploy a suitable controller for the unknown system by periodically selecting among a collection of $N$ candidate controllers, some of which may destabilize the underlying system. While classical estimator-based supervisory control guarantees asymptotic stability, it lacks quantitative finite-time performance bounds. Conversely, current non-asymptotic methods in both online learning and system identification require restrictive assumptions that are incompatible in a control setting, such as system stability, which preclude testing potentially unstable controllers. To br...
| Authors | Haoyuan Sun, Ali Jadbabaie |
|---|---|
| Primary category | math.OC |
| Published | 16 March 2026 |
| Last updated at source | 9 June 2026 |
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