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State estimation in multi-agent decision and control systems

We consider the problem of estimating the state in multi-agent decision and control systems. A novel approach to state estimation is developed that uses partial order theory in order to overcome some of the severe computational complexity issues arising in multi-agent systems. State estimation algorithms are developed that enjoy provable convergence properties and are scalable with the number of agents. Application examples are considered, which include state estimation in competitive multi-robot systems. A final application example shows how to extend the proposed state estimation approach to the context of monitoring distributed environments.

Type of Seminar:
Symposium on Control and Computation
Dr. Domitilla Del Vecchio
Caltech, USA
Jun 22, 2005   8:15

ETH-Zentrum, Gloriastrasse 35, Zurich, Room ETZ E8
Contact Person:

Prof. M.Morari
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Biographical Sketch:
Domitilla Del Vecchio received her Laurea Degree cum laude in Electronic Engineering in 1999 from the University of Rome at Tor Vergata, where she worked as research assistant in year 2000. She received the Ph.D. in Control and Dynamical Systems in March 2005 from the California Institute of Technology, where she is currently a post-doctoral researcher. Her research interests are in the modeling, estimation, and control of hybrid and embedded systems, with emphasis on computation and theory. Current application areas in which she is interested include autonomous and multi-agent systems, and mobile sensor networks in human environments.