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State Estimation in MultiAgent Decision and Control Systems 
Abstract 
This thesis addresses the problem of estim … This thesis addresses the problem of estimating the state in multiagent decision and
control systems. In particular, 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 multiagent systems. Within this approach, state estimation algorithms are
developed, which enjoy proved convergence properties and are scalable with the number
of agents.
<p>
The dynamic evolution of the systems under study are characterized by the interplay of
continuous and discrete variables. Continuous variables usually represent physical quan
tities such as position, velocity, voltage, and current, while the discrete variables usually
represent quantities internal to the decision protocol that is used for coordination, com
munication, and control. Within the proposed state estimation approach, the estimation of
continuous and discrete variables is developed in the same mathematical framework, as a
joint continuousdiscrete space is considered for the estimator. This way, the dichotomy
between the continuous and discrete world is overcome for the purpose of state estimation.
<p>
Application examples are considered, which include the state estimation in competi
tive multirobot systems and in multiagent discrete event systems, and the monitoring of
distributed environments. e monitoring of
distributed environments. +


Authors  Domitilla Del Vecchio + 
ID  2005 + 
Source  PhD Dissertation, Control and Dynamical Systems + 
Tag  ddv05phd + 
Title  State Estimation in MultiAgent Decision and Control Systems + 
Type  PhD Dissertation + 
Categories  Papers 
Modification date This property is a special property in this wiki.

15 May 2016 06:18:05 + 
URL This property is a special property in this wiki.

http://www.cds.caltech.edu/~murray/preprints/ddv05phd.pdf + 
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