Difference between revisions of "EECI08: Packet-Based Estimation and Control"
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{{eeci-sp08 header|next=[[EECI: Information Flow and Consensus|Graph Theory]]|prev=[[ | {{eeci-sp08 header|next=[[EECI: Information Flow and Consensus|Graph Theory]]|prev=[[EECI: State Estimation and Sensor Fusion|State Estimation]]}} | ||
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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 (TCP-like) or not acknowledged (UDP-like). | 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 (TCP-like) or not acknowledged (UDP-like). | ||
== Lecture Materials == | |||
|- | * Lecture notes: {{eeci-sp08 pdf|L7_pbc-scan.pdf|Packet-Based Estimation and Control}} (scan of handwritten notes) | ||
| | * Lecture slides: {{eeci-sp08 pdf|L7_pbc.pdf|Packet-Based Estimation and Control}} (contains a few examples + graphs) | ||
* | |||
== | == Reading == | ||
* <p>[http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=29437 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.</p> | * <p>[http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=29437 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.</p> | ||
* <p>[ftp://ftp.cds.caltech.edu/murray/preprints/estm07-automatica_s.pdf 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.</p> | * <p>[ftp://ftp.cds.caltech.edu/murray/preprints/estm07-automatica_s.pdf 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.</p> | ||
Latest revision as of 20:16, 1 March 2009
| Prev: State Estimation | Course home | Next: Graph Theory |
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 (TCP-like) or not acknowledged (UDP-like).
Lecture Materials
- Lecture notes: Packet-Based Estimation and Control (scan of handwritten notes)
- Lecture slides: Packet-Based Estimation and Control (contains a few examples + graphs)
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.
