IEEE/CAA Journal of Automatica Sinica
Citation:  D. D. Yue, S. Baldi, J. D. Cao, Q. Li, and B. De Schutter, “Distributed adaptive resource allocation: An uncertain saddlepoint dynamics viewpoint,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2209–2221, Dec. 2023. doi: 10.1109/JAS.2023.123402 
This paper addresses distributed adaptive optimal resource allocation problems over weightbalanced digraphs. By leveraging stateoftheart adaptive coupling designs for multiagent systems, two adaptive algorithms are proposed, namely a directedspanningtreebased algorithm and a nodebased 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 saddlepoint dynamics, which can be tackled by repeatedly adopting the PeterPaul 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 30bus and 118bus power systems.
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