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State Estimation in Multi-Agent Decision and Control Systems

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Domitilla Del Vecchio

PhD Dissertation, Control and Dynamical Systems

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.

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.

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.

PhD Dissertation

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