Low-Rank and Low-Order Decompositions for Local System Identification

Nikolai Matni, Anders Rantzer

As distributed systems increase in size, the need for scalable algorithms becomes more and more important. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. We show that in what we term the "full interconnection measurement" setting, this task is easily solved using existing system identification methods. We also propose a promising heuristic for the "hidden interconnection measurement" case, in which contributions to local measurements from both local and global dynamics need to be separated. Inspired by the machine learning literature, and in particular by convex approaches to rank minimization and matrix decomposition, we exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization.

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