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=[[NCS: State Estimation and Sensor Fusion|State Estimation]]}}
{{eeci-sp08 header|next=[[EECI: Information Flow and Consensus|Graph Theory]]|prev=[[EECI: State Estimation and Sensor Fusion|State Estimation]]}}


Revision as of 00:57, 29 March 2008

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).


  1. Kalman filtering with intermittent observations
    • Problem motivation and setup
    • Mathematical preliminaries (Jensen's inequality)
    • Main results: upper and lower bounds
  2. Probabalistic state estimation with packet drops
    • Probabalistic bounds versus expected value (example)
    • Performance versus data loss tradeoff
  3. Packet-based control
    • TCP-like networks
    • UDP-like networks

Lecture Materials

Additional Information

Further Reading