Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Distributed Optimization Methods for Estimation and Control over Networks

Back
Abstract:
In this presentation we present several distributed optimization methods for solving estimation and control problems arising in large-scale networks. We show that centralized control problems for networks consisting of subsystems with interacting dynamics can be recast as a separable convex optimization problem but with coupling constraints. Furthermore, the state estimation problem for a system, using a network of sensors can also be posed as a separable convex optimization problem with specific structure on the coupling constraints. We present several distributed optimization algorithms for solving this type of optimization problems and we provide results on convergence and efficiency estimates for these algorithms. The new distributed optimization methods are suitable for application to control and estimation in networks since they are highly parallelizable, each subsystem/sensor uses only local information and the coordination between the local controllers/sensors is performed via the Lagrange multipliers corresponding to the coupled dynamics/the consensus weights.

Type of Seminar:
IfA Seminar
Speaker:
Prof. Ion Necoara
University of Bucharest
Date/Time:
Oct 12, 2010   10:15
Location:

K25
Contact Person:

Colin Jones
File Download:

Request a copy of this publication.
Biographical Sketch: