Reference
D. Yue, S. Baldi, J. Cao, Q. Li, and B. De Schutter, "Distributed adaptive
resource allocation: An uncertain saddle-point dynamics viewpoint,"
IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 12, pp.
2209-2221, Dec. 2023.
Abstract
This paper addresses distributed adaptive optimal resource allocation problems
over weight-balanced digraphs. By leveraging state-of-the-art adaptive coupling
designs for multiagent systems, two adaptive algorithms are proposed, namely a
directed-spanning-tree-based algorithm and a node-based algorithm. The benefits
of these algorithms are that they require neither sufficiently small or unitary
step sizes, nor global knowledge of Laplacian eigenvalues, which are widely
required in the literature. It is shown that both algorithms belong to a class
of uncertain saddle-point dynamics, which can be tackled by repeatedly adopting
the Peter-Paul inequality in the framework of Lyapunov theory. Thanks to this
new viewpoint, global asymptotic convergence of both algorithms can be proven
in a unified way. The effectiveness of the proposed algorithms is validated
through numerical simulations and case studies in IEEE 30- and 118-bus power
systems.
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BibTeX
@article{YueBal:23-010,
author = {Yue, Dongdong and Baldi, Simone and Cao, Jinde and Li, Qi and De
Schutter, Bart},
title = {Distributed Adaptive Resource Allocation: {An} Uncertain
Saddle-Point Dynamics Viewpoint},
journal = {IEEE/CAA Journal of Automatica Sinica},
volume = {10},
number = {12},
pages = {2209--2221},
month = dec,
year = {2023}
}