Complexity management in the state estimation of multi-agent systems

This work 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 quantities such as position, velocity, voltage, and current, while the discrete variables usually represent quantities internal to the decision protocol that is used for coordination, communication, 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. An application example is considered, involving the state estimation in competitive multi-robot systems. Cooperative Control of Distributed Multiagent Systems. Edited by J.S. Shamma c © 2001 John Wiley & Sons, Ltd This is a Book Title Name of the Author/Editor c © XXXX John Wiley & Sons, Ltd 2COMPLEXITY MANAGEMENT IN THE STATE ESTIMATION OF MULTI-AGENT SYSTEMS 1.