In this article, we present a method to learn the interaction topology of a network of agents undergoing linear consensus updates in a non invasive manner. Our approach is based on multivariate Wiener filtering, which is known to recover spurious edges apart from the true edges in the topology. The main contribution of this work is to show that in the case of undirected consensus networks, all spurious links obtained using Wiener filtering can be identified using frequency response of the Wiener filters. Thus, the exact interaction topology of the agents is unveiled. The method presented requires time series measurements of the state of the agents and does not require any knowledge of link weights. To the best of our knowledge this is the first approach that provably reconstructs the structure of undirected consensus networks with correlated noise. We illustrate the effectiveness of the method developed through numerical simulations as well as experiments on a five node network of Raspberry Pis.
|Original language||English (US)|
|Title of host publication||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jun 28 2017|
|Event||56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia|
Duration: Dec 12 2017 → Dec 15 2017
|Name||2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017|
|Other||56th IEEE Annual Conference on Decision and Control, CDC 2017|
|Period||12/12/17 → 12/15/17|
Bibliographical noteFunding Information:
The authors S. Talukdar, D. Materassi and M. V. Sala-paka acknowledge the support of ARPA-E for supporting this research through the project titled ‘A Robust Distributed Framework for Flexible Power Grids’ via grant no. DE-AR000071 and Xcel Energy’s Renewable Development Fund. D. Deka acknowledges the support of funding from the U.S. Department of Energy’s Office of Electricity as part of the DOE Grid Modernization Initiative.
© 2017 IEEE.