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Distributed Estimation and Control for LTI Systems under Finite-Time Agreement
This paper considers a strongly connected network of agents, each capable of partially observing and controlling a discrete-time linear time-invariant (LTI) system that is jointly observable and controllable.
Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping
The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities.
Distributed k-means algorithm
Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters. The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm.
