State Estimation in Multi-Agent Decision and Control Systems
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.