1 research outputs found
Minimizing Inputs for Strong Structural Controllability
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