Matrix-Scaled Consensus
Abstract
This paper proposes matrix-scaled consensus algorithm, which generalizes the scaled consensus algorithm in \cite{Roy2015scaled}. In (scalar) scaled consensus algorithms, the agents' states do not converge to a common value, but to different points along a straight line in the state space, which depends on the scaling factors and the initial states of the agents. In the matrix-scaled consensus algorithm, a positive/negative definite matrix weight is assigned to each agent. Each agent updates its state based on the product of the sum of relative matrix scaled states and the sign of the matrix weight. Under the proposed algorithm, each agent asymptotically converges to a final point differing with a common consensus point by the inverse of its own scaling matrix. Thus, the final states of the agents are not restricted to a straight line but are extended to an open subspace of the state-space. Convergence analysis of matrix-scaled consensus for single and double-integrator agents are studied in detail. Simulation results are given to support the analysis.
Cite
@article{arxiv.2204.10723,
title = {Matrix-Scaled Consensus},
author = {Minh Hoang Trinh and Dung Van Vu and Quoc Van Tran and Hyo-Sung Ahn},
journal= {arXiv preprint arXiv:2204.10723},
year = {2022}
}
Comments
Accepted to the IEEE Conference on Decision and Control (CDC), 2022