2022/04/22 by Minh Hoang Trinh, Trinh, Minh Hoang, Dung Van Vu +5 · 1 citation
Computer Science · Physics and Astronomy · #Distributed Control Multi-Agent Systems #FOS: Electrical engineering #FOS: Mathematics #Nonlinear Dynamics and Pattern Formation #Opinion Dynamics and Social Influence #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.10723
openalex publication_date 2022/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes matrix-scaled consensus algorithm, which generalizes the scaled consensus algorithm in \citeRoy2015scaled. 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.