TY - GEN
T1 - Inferring the network topology of interconnected nonlinear units with diffusive couplings
AU - Burbano-L, Daniel A.
AU - Freeman, Randy
AU - Lynch, Kevin
N1 - Funding Information:
*This work was supported in part by a grant from the Office of Naval Research.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - In this paper we study the problem of real-time topology identification of networks with diffusive couplings and unknown linear terms. Inspired by adaptive observer theory, we propose two different strategies based on full or partial measurements of each node activity. Sufficient conditions guaranteeing convergence to the precise value of coupling weights are derived using appropriate Lyapunov functions, Barbalat's Lemma, and the persistent excitation condition. Our theoretical results are illustrated via numerical simulations using a network of chaotic oscillators.
AB - In this paper we study the problem of real-time topology identification of networks with diffusive couplings and unknown linear terms. Inspired by adaptive observer theory, we propose two different strategies based on full or partial measurements of each node activity. Sufficient conditions guaranteeing convergence to the precise value of coupling weights are derived using appropriate Lyapunov functions, Barbalat's Lemma, and the persistent excitation condition. Our theoretical results are illustrated via numerical simulations using a network of chaotic oscillators.
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U2 - 10.23919/ACC.2018.8431266
DO - 10.23919/ACC.2018.8431266
M3 - Conference contribution
AN - SCOPUS:85052574352
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 3398
EP - 3403
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -