Quantized control via locational optimization

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dc.creatorBullo, Francesco
dc.creatorLiberzon, Daniel
dc.date2002-12-22
dc.date.accessioned2026-07-07T04:53:59Z
dc.date.available2026-07-07T04:53:59Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/math/0212307
dc.identifierhttp://arxiv.org/abs/math/0212307
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/66073
dc.subjectOptimization and Control
dc.subject93C10, 93C41, 90C25
dc.titleQuantized control via locational optimization
dc.typetext

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