Observer Synthesis for Switched Discrete-Time Linear Systems using Relaxed Dynamic Programming
Författare
Summary, in English
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.
Avdelning/ar
Publiceringsår
2006
Språk
Engelska
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Dokumenttyp
Konferensbidrag
Ämne
- Control Engineering
Conference name
17th International Symposium on Mathematical Theory of Networks and Systems, 2006
Conference date
2006-07-24 - 2006-07-28
Conference place
Kyoto, Japan
Status
Published