Quantized control via locational optimization
Abstract
This paper studies state quantization schemes for feedback stabilization of control systems with limited information. The focus is on designing the least destabilizing quantizer subject to a given information constraint. We explore several ways of measuring the destabilizing effect of a quantizer on the closed-loop system, including (but not limited to) the worst-case quantization error. In each case, we show how quantizer design can be naturally reduced to a version of the so-called multicenter problem from locational optimization. Algorithms for solving such problems are discussed. In particular, an iterative solver is developed for a novel weighted multicenter problem which most accurately represents the least destabilizing quantizer design.
Keywords
Cite
@article{arxiv.math/0212307,
title = {Quantized control via locational optimization},
author = {Francesco Bullo and Daniel Liberzon},
journal= {arXiv preprint arXiv:math/0212307},
year = {2007}
}