论文标题
选择大规模网络系统控制节点的可控性得分
Controllability scores for selecting control nodes of large-scale network systems
论文作者
论文摘要
为了适当选择大规模网络系统的控制节点，我们提出了两个称为体积和平均能量可控性得分的控制中心。分数是使用可控性Gramian制定的凸出优化问题的独特解决方案。对于稳定的案例和包括多代理系统的不稳定案例，唯一性已被证明。我们表明，可以使用基于预测的梯度方法对标准单纯形的建议算法进行有效计算得分。数值实验表明，所提出的算法比现有的内点方法更有效，并且所提出的分数可以正确捕获每个状态节点对可控性的重要性，从而超过现有的控制中心。
To appropriately select control nodes of a large-scale network system, we propose two control centralities called volumetric and average energy controllability scores. The scores are the unique solutions to convex optimization problems formulated using the controllability Gramian. The uniqueness is proven for stable cases and for unstable cases that include multi-agent systems. We show that the scores can be efficiently calculated by using a proposed algorithm based on the projected gradient method onto the standard simplex. Numerical experiments demonstrate that the proposed algorithm is more efficient than an existing interior point method, and the proposed scores can correctly capture the importance of each state node on controllability, outperforming existing control centralities.
