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Kota Umezu

Publications and source records attributed to Kota Umezu.

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Infinite-horizon controllability scores for linear time-invariant systems

We formulate infinite-horizon controllability scores for linear time-invariant systems whose conventional controllability Gramians may diverge. A mode-dependent scaling yields finite-horizon volumetric controllability score (VCS) and average energy controllability score (AECS) problems that are exactly equivalent to the original ones, while the scaled Gramians converge as the horizon tends to infinity. Using epi-convergence, we establish convergence of the finite-horizon optimal allocations to the limiting solution set and obtain convergence to a unique limiting score under explicit rank conditions. The limiting VCS retains the stable, center, and unstable components and preserves full-system controllability, whereas the limiting AECS depends only on the stable component and may neglect an input required for a center or unstable mode. The VCS theory allows arbitrary spectral structures, whereas the AECS limit requires a nonempty stable component. The additional restriction on the center spectrum is imposed only for the real-Schur implementation. A directed-Laplacian example verifies the convergence and uniqueness conditions and illustrates the controllability distinction between the two limiting scores.

math.OC

Controllability scores of linear time-varying network systems

For large-scale network systems, network centrality based on control theory plays a crucial role in understanding their properties and controlling them efficiently. The controllability score is such a centrality index and can give a physically meaningful measure. It is originally proposed for linear time-invariant (LTI) systems, and we extend it to linear time-varying (LTV) systems in this paper. Since the controllability score is defined as an optimal solution to some optimization problem, it is not necessarily uniquely determined. Its uniqueness must be guaranteed for reproducibility and interpretability. In this paper, we show its uniqueness in almost all cases, which guarantees its use as a network centrality measure. We also prove its continuity with respect to the time parameters. In addition, we propose a data-driven method to compute it. Finally, we verify the effectiveness of the extension and examine the performance of the data-driven method through numerical experiments.

math.OC