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.
Bibtex entry:
@article{YueBal:23-010,