Reset-Free Data-Driven Gain Estimation: Power Iteration using Reversed-Circulant Matrices

A direct data-driven iterative algorithm is developed to accurately estimate the $H_{\mathrm{\infty }}$ norm of a linear time-invariant system from continuous operation, i.e., without resetting the system. The main technical step involves a reversed-circulant matrix that can be evaluated in a model-free setting by performing experiments on the real system.