Difference between revisions of "Packet-based Control with Norm Bounded Uncertainties"

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<!-- Enter a 1 paragraph description of the contents of the lecture.  Make sure to include any key concepts, so that the wiki search feature will pick them up -->
 
<!-- Enter a 1 paragraph description of the contents of the lecture.  Make sure to include any key concepts, so that the wiki search feature will pick them up -->
In this lecture, we study the effect of data loss on the performance of the Kalman filter for discrete-time linear systems. Observations are lost according to a bernoulli independent process, modeling this way the presence of a lossy networks between the sensors and the estimator. We first prove that the Kalman filter is still optimal in this new scenario.
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In this lecture, we (to be filled)
We then provide asymptotic results on the performance of the filter. In particular, we show that a transition from boundedness to instability arises if the arrival probability is lower that a critical value, that depends on the unstable eigenvalues of the system.
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== Lecture Materials ==
 
== Lecture Materials ==

Revision as of 00:34, 29 April 2006

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In this lecture, we (to be filled)

Lecture Materials

Reading

  • Kalman Filtering with Intermittent Observations, B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan and S. Sastry. This is the paper where all the proofs reside. Below I posted Chapter 3 of my thesis, which is essentially the same, but the notation is more consistent with the next two lectures.

  • Optimal Estimation in Lossy Networks This is chapter 3 of my thesis. Content is almost the same as the paper above, but notation is slightly modified to be consistent with the control part.


Additional Resources

  • The Kalman Filter, G. Welch and G. Bishop. A webpage with many links on Kalman filter.

  • Optimal Filtering, B.D.O Anderson and J.B. Moore. Dover Books on Engineering, 2005. A reissue of a book from 1979. It contains a detailed mathematical presentation of filtering problems and the Kalman filter. A very good book.