Observer Synthesis for Switched Discrete-Time Linear Systems using Relaxed Dynamic Programming

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Abstract

In this paper, state estimation for Switched Discrete-Time Linear
Systems is performed using relaxed dynamic programming. Taking the Bayesian point of view, the estimation problem is transformed into an infinite dimension al optimization problem. The optimization problem is then solved using relaxed dynamic programming. The estimate of both the mode and the continuous state can then be computed from the value-function. From an unknown initial state the estimation error goes to zero as more measurements are collected.

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Subject classification (UKÄ) – MANDATORY

  • Control Engineering
Original languageEnglish
Publication statusPublished - 2006
Publication categoryResearch
Peer-reviewedYes
Event17th International Symposium on Mathematical Theory of Networks and Systems, 2006: MTNS 2006 - Kyoto, Japan
Duration: 2006 Jul 242006 Jul 28
Conference number: 17

Conference

Conference17th International Symposium on Mathematical Theory of Networks and Systems, 2006
CountryJapan
CityKyoto
Period2006/07/242006/07/28

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