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arXiv 1401.4209math.OC
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鲁棒最小能控性问题

The Robust Minimal Controllability Problem

Sergio Pequito, Guilherme Ramos, Soummya Kar, A. Pedro Aguiar, Jaime Ramos

摘要:

在本文中,我们解决两个最小可控性问题,其中的目标是确定一个最小的状态变量子集的线性定常系统被驱动,以确保可控性下的附加约束。首先,我们研究了当自治系统的矩阵是简单的时,保证能控性的稀疏输入矩阵的特征化问题。其次,我们建立在这些结果来描述解决方案的鲁棒最小可控性问题,其目标是确定稀疏输入矩阵,确保可控性时,指定数量的输入失败。这两个问题都是NP困难的,但在动力学矩阵简单的假设下,我们证明了将这两个问题归结为集合多重覆盖问题是可能的。因此,这些问题共享相同的计算复杂性,即,它们是NP-完全的,但是可以利用多项式算法来近似集合多覆盖问题的解,以获得最小可控性问题中的任一个的接近最优解。

英文摘要:

In this paper, we address two minimal controllability problems, where the goal is to determine a minimal subset of state variables in a linear time-invariant system to be actuated to ensure controllability under additional constraints. First, we study the problem of characterizing the sparsest input matrices that assure controllability when the autonomous dynamics' matrix is simple. Secondly, we build upon these results to describe the solutions to the robust minimal controllability problem, where the goal is to determine the sparsest input matrix ensuring controllability when specified number of inputs fail. Both problems are NP-hard, but under the assumption that the dynamics' matrix is simple, we show that it is possible to reduce these two problems to set multi-covering problems. Consequently, these problems share the same computational complexity, i.e., they are NP-complete, but polynomial algorithms to approximate the solutions of a set multi-covering problem can be leveraged to obtain close-to-optimal solutions to either of the minimal controllability problems.

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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