论文标题
矩阵标准共识
Matrix-Scaled Consensus
论文作者
论文摘要
本文提出了矩阵尺度的共识算法，该算法概括了\ cite {roy2015scaled}中的缩放共识算法。在（标量）缩放的共识算法中，代理状态不会收敛到共同值，而是沿状态空间中的直线沿不同点，这取决于缩放因子和试剂的初始状态。在矩阵尺寸的共有算法中，将正/负定基质重量分配给每个代理。每个代理都根据相对矩阵缩放状态和矩阵重量的符号的乘积来更新其状态。在所提出的算法下，每个代理渐近地收敛到最终点，与共同共识点不同，其自身缩放矩阵的倒数。因此，代理的最终状态不仅限于直线，而是扩展到状态空间的开放子空间。详细研究了单个和双积体剂的基质尺度共有共识的收敛分析。给出模拟结果以支持分析。
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.
