Advanced

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

Alriksson, Peter LU and Rantzer, Anders LU (2006) 17th International Symposium on Mathematical Theory of Networks and Systems, 2006
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.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
17th International Symposium on Mathematical Theory of Networks and Systems, 2006
language
English
LU publication?
yes
id
36746938-66cb-4b81-ac45-53b59ab6ba2b (old id 929458)
date added to LUP
2008-01-14 16:46:28
date last changed
2016-06-21 16:00:59
@misc{36746938-66cb-4b81-ac45-53b59ab6ba2b,
  abstract     = {In this paper, state estimation for Switched Discrete-Time Linear<br/><br>
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.},
  author       = {Alriksson, Peter and Rantzer, Anders},
  language     = {eng},
  title        = {Observer Synthesis for Switched Discrete-Time Linear Systems using Relaxed Dynamic Programming},
  year         = {2006},
}