In this paper, we address the state estimation problem for multi-agent systems interacting in large scale networks. This research is motivated by the observation that in large-scale networks for many practical applications and domains, each agent only requires information concerning agents spatially close to its location, let's say topologically k-hop away. We propose a scalable framework where each agent is able to estimate in finite-time the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.
Gasparri, A., Marino, A. (2018). A k-hop graph-based observer for large-scale networked systems. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp.4747-4752). Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2017.8264361].
A k-hop graph-based observer for large-scale networked systems
Gasparri A.;
2018-01-01
Abstract
In this paper, we address the state estimation problem for multi-agent systems interacting in large scale networks. This research is motivated by the observation that in large-scale networks for many practical applications and domains, each agent only requires information concerning agents spatially close to its location, let's say topologically k-hop away. We propose a scalable framework where each agent is able to estimate in finite-time the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
