Difference between revisions of "EECI08: PacketBased Estimation and Control"
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* Mathematical preliminaries (Jensen's inequality)  * Mathematical preliminaries (Jensen's inequality)  
* Main results: upper and lower bounds  * Main results: upper and lower bounds  
<li>  <li>Probabalistic state estimation with packet drops</li>  
*  * Probabalistic bounds versus expected value (example)  
* Performance versus data loss tradeoff  * Performance versus data loss tradeoff  
<li>Packetbased control</li>  <li>Packetbased control</li>  
* TCPlike networks  * TCPlike networks  
* UDPlike networks  * UDPlike networks  
</ol>  </ol>  
 width=50%    width=50%  
Revision as of 00:35, 29 March 2008
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This lecture describes how to extend results in estimation and control to the case where the information between sensing, actuation and computation flows across a network with possible packet loss and time delay. We begin with the estimation problem, summarizing the results on Sinopoli et al on Kalman filtering with intermittent data, which uses average convergence as a stability metric. An alternative formulation is to use almost sure convergence, which gives improved results for lossy networks. Finally, we extend the results on estimation to the control setting, summarizing approaches in the cases where receipt of packets are acknowledge (TCPlike) or not acknowledged (UDPlike).
Outline

Lecture Materials
Additional Information 
Further Reading
Kalman Filtering with Intermittent Observations, B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan and S. Sastry. IEEE T. Automatic Control, 2004. This is the paper that covers the main results of this lecture.
Probabilistic Performance of State Estimation Across a Lossy Network. Michael Epstein, Ling Shi, Abhishek Tiwari and Richard M. Murray. Automatica, 2008 (to appear). This article describes how to computer probabilistic guarantees on estimator performance in the presence of packet loss.