View source for State Estimation in Multi-Agent Decision and Control Systems

{{HTDB paper
| authors = Domitilla Del Vecchio
| title = State Estimation in Multi-Agent Decision and Control Systems
| source = PhD Dissertation, Control and Dynamical Systems
| year = 
| type = PhD Dissertation
| funding = NSF/CNSE, NSF/ITR, DARPA/SEC, AFOSR/coop
| url = http://www.cds.caltech.edu/~murray/preprints/ddv05-phd.pdf
| abstract = 
This thesis addresses the problem of estimating the state in multi-agent 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 multi-agent 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 continuous-discrete 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 multi-robot systems and in multi-agent discrete event systems, and the monitoring of 
distributed environments.
| flags = 
| tag = ddv05-phd
| id = 2005
}}