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arXiv · 1810.07220

Minimizing Inputs for Strong Structural Controllability

Kumar Yashashwi·Shana Moothedath·Prasanna Chaporkar
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Abstract

The notion of strong structural controllability (s-controllability) allows for determining controllability properties of large linear time-invariant systems even when numerical values of the system parameters are not known a priori. The s-controllability guarantees controllability for all numerical realizations of the system parameters. We address the optimization problem of minimal cardinality input selection for s-controllability. Previous work shows that not only the optimization problem is NP-hard, but finding an approximate solution is also hard. We propose a randomized algorithm using the notion of zero forcing sets to obtain an optimal solution with high probability. We compare the performance of the proposed algorithm with a known heuristic [1] for synthetic random systems and five real-world networks, viz. IEEE 39-bus system, re-tweet network, protein-protein interaction network, US airport network, and a network of physicians. It is found that our algorithm performs much better than the heuristic in each of these cases.

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Kumar Yashashwi, Shana Moothedath, Prasanna Chaporkar. 2018-10-16. Minimizing Inputs for Strong Structural Controllability. https://arxiv.org/abs/1810.07220

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