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arXiv 1407.2884math.OCcs.SYeess.SY
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结构可控性的最小输入选择

Minimum Input Selection for Structural Controllability

University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

Alex Olshevsky

AI总结:

本文研究了在给定线性系统和禁止状态变量的情况下,如何通过最小化输入变量集实现结构可控性,并提出确定性算法在O(n+m√n)时间内解决该问题。

摘要:

给定线性系统$\dot{x} = Ax$,其中$A$是一个$n\times n$矩阵,有$m$个非零元素,我们考虑在给定禁止状态变量$F$的情况下,如何通过最小化影响状态变量的输入集来实现结构可控性。我们进一步假设给定一组

英文摘要:

Given a linear system $\dot{x} = Ax$, where $A$ is an $n \times n$ matrix with $m$ nonzero entries, we consider the problem of finding the smallest set of state variables to affect with an input so that the resulting system is structurally controllable. We further assume we are given a set of "forbidden state variables" $F$ which cannot be affected with an input and which we have to avoid in our selection. Our main result is that this problem can be solved deterministically in $O(n+m \sqrt{n})$ operations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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