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

Peter Alriksson, Anders Rantzer

Research output: Contribution to conferencePaper, not in proceedingpeer-review

85 Downloads (Pure)

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.
Original languageEnglish
Publication statusPublished - 2006
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
Country/TerritoryJapan
CityKyoto
Period2006/07/242006/07/28

Subject classification (UKÄ)

  • Control Engineering

Fingerprint

Dive into the research topics of 'Observer Synthesis for Switched Discrete-Time Linear Systems using Relaxed Dynamic Programming'. Together they form a unique fingerprint.

Cite this