## 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.

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 language | English |
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Publication status | Published - 2006 |

Event | 17th International Symposium on Mathematical Theory of Networks and Systems, 2006: MTNS 2006 - Kyoto, Japan Duration: 2006 Jul 24 → 2006 Jul 28 Conference number: 17 |

### Conference

Conference | 17th International Symposium on Mathematical Theory of Networks and Systems, 2006 |
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Country/Territory | Japan |

City | Kyoto |

Period | 2006/07/24 → 2006/07/28 |

## Subject classification (UKÄ)

- Control Engineering