Information about the behavior of dynamical systems can often be obtained by analyzing the
eigenvalues and corresponding eigenfunctions of linear operators associated with a …

We develop a data-driven, model-free approach for the optimal control of the dynamical
system. The proposed approach relies on the Deep Neural Network (DNN) based learning …

We present a new framework for optimal and feedback control of PDEs using Koopman
operator-based reduced order models (K-ROMs). The Koopman operator is a linear but …

We introduce the concept of a control contraction metric, extending contraction analysis to
constructive nonlinear control design. We derive sufficient conditions for exponential …

This paper studies the feedback stabilization problem of k-valued logical control networks
(KVLCNs), and proposes a control Lyapunov function (CLF) approach for this problem. First …

Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …

In this paper, we provide a systematic approach for the design of stabilizing feedback
controllers for nonlinear control systems using the Koopman operator framework. The …

Abstract The Koopman and Perron Frobenius transport operators are fundamentally
changing how we approach dynamical systems, providing linear representations for even …

Transient stability control of power systems is based on actions that are taken automatically
following a disturbance to ensure that the system remains in synchronism. Examples of such …

This paper is concerned with the data-driven optimal control of nonlinear systems. We
present a convex formulation of the optimal control problem with a discounted cost function …