Minimax dual control with finite-dimensional information state
Abstract
This article considers output-feedback control of systems where the function mapping states to measurements has a set-valued inverse. We show that if the set has a bounded number of elements, then minimax dual control of such systems admits finite-dimensional information states. We specialize our results to a discrete-time integrator with magnitude measurements and derive a surprisingly simple sub-optimal control policy that ensures finite gain of the closed loop. The sub-optimal policy is a proportional controller where the magnitude of the gain is computed offline, but the sign is learned, forgotten, and relearned online. The discrete-time integrator with magnitude measurements captures real-world applications such as antenna alignment, and despite its simplicity, it defies established control-design methods. For example, whether a stabilizing linear time-invariant controller exists for this system is unknown, and we conjecture that none exists.
Keywords
Cite
@article{arxiv.2312.05156,
title = {Minimax dual control with finite-dimensional information state},
author = {Olle Kjellqvist},
journal= {arXiv preprint arXiv:2312.05156},
year = {2024}
}
Comments
Accepted for the 6th annual Learning for Dynamics & Control Conference, Added examples 1--3 to further motivate the problem class and merged propositions 1 and 2 on page 2. Rewrote the explanation of the worst-case history and its recursive update rules. Also corrected typos and inconsistent notation detected by the L4DC reviewers. No changes to the theoretical results