Multi-Agent Systems
Animal swarms, electric grids, autonomous vehicle formations, interconnected neurons, sensor networks… all these systems share a crucial feature: their overall behavior results from a local interaction among many agents. Here, “local” means that each agent typically interacts with its neighbors only. This course aims at providing the basics to both analyze and control of such large- scale networks. In particular, it provides methodological tools to study interconnection between multiple dynamical agents. It addresses two fundamental features of such networks: consensus, in which the states of all agents converge to a common value, and synchronization, in which agents share a coherent behavior (possibly non-static). These notions will be illustrated via a selection of case studies.
References
- W. Ren, Y. Cao. Distributed Coordination of Multi-agent Networks: Emergent Problems, Models, and Issues. Communications and Control Engineering, Springer-Verlag, London, 2011
- W. Ren, R.W. Beard, and E. M. Atkins. Information Consensus in Multivehicle Cooperative Control, IEEE Control Systems Magazine, Vol. 27, No. 2, April, 2007, pp. 71-82
- Henk, N. and Alejandro, R. A.. Synchronization of mechanical systems (Vol. 46), World Scientific, 2003
- S.S Kia, B.VanScoy, J.Cortes, R.A. Freeman, K.M. Lynch, and S.Martinez. Tutorial on Dynamic Average Consensus The problem, its applications, and the algorithms, arXiv 2018
