Efficiently Computing the Cyclic Output-to-Output Gain

Abstract

The cyclic output-to-output gain is a security metric for control systems. Commonly, it is computed by solving a semi-definite program, which scales badly and inhibits its use for large-scale systems. We propose a method for computing the cyclic output-to-output gain using Hamiltonian matrices, similar to existing methods for the $H_\infty$-norm. In contrast to existing methods for the $H_{\infty}$-norm, the proposed method considers generalized singular values rather than regular singular values. Moreover, to ensure that the Hamiltonian matrices exist, we introduce a regularized version of the cyclic output-to-output gain. Through numerical experiments, we show that the proposed method is more efficient, scalable, and reliable than semi-definite programming approaches.