We develop a new method which extends dynamic mode decomposition (DMD) to
incorporate the effect of control to extract low-order models from high-dimensional, complex …

We develop a new generalization of Koopman operator theory that incorporates the effects
of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of …

This article proposes a data-driven model reduction approach on the basis of noisy data with
a known noise model. Firstl, the concept of data reduction is introduced. In particular, we …

Our contribution in this paper is twofold. In the first part, we study Lyapunov functions when a
plant is interconnected with a dissipative stabilizing controller. In the second, we present …

This article introduces a computational framework to identify nonlinear input–output
operators that fit a set of system trajectories while satisfying incremental integral quadratic …

This paper is concerned with the parameter estimation of a class of time-delay systems in
the state space form. By using the hierarchical identification principle, a gradient based and …

In this article, the classical algebraic regulator problem is studied in a data-driven context.
The endosystem is assumed to be an unknown system that is interconnected to a known …

The field of smart health monitoring, intelligent fault detection and diagnosis is expanding
dramatically in order to maintain successful operation in many engineering applications …

The increasing ubiquity of complex systems that require control is a challenge for existing
methodologies in characterization and controller design when the system is high …

A new method for data-driven interpolatory model reduction for discrete-time systems is
presented in this paper. Using the so-called data informativity perspective, we define a …