This article provides an overview of a new approach to designing controllers for nonlinear
systems using data-driven control. Data-driven control is an important area of research in …

This paper introduces the Koopman Control Family (KCF), a mathematical framework for
modeling general discrete-time nonlinear control systems with the aim of providing a solid …

The extended Dynamic Mode Decomposition (eDMD) is a very popular method to obtain
data-driven surrogate models for nonlinear (control) systems governed by ordinary and …

We consider the feedback linearization problem, and contribute with a new method that can
learn the linearizing controller from a library (a dictionary) of candidate functions. When the …

An indirect data-driven control and transfer learning approach based on a data-driven
feedback linearization with neural canonical control structures is proposed. An artificial …

Dieser Beitrag beschäftigt sich mit dem datenbasierten Regler-und Beobachterentwurf
nichtlinearer Systeme auf der Basis neuronaler kanonischer Strukturen. Mit Hilfe von …

This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine
nonlinear systems. First, we estimate the system dynamics from noisy data offline through …

This work presents a control-oriented identification scheme for efficient control design and
stability analysis of nonlinear systems. Neural networks are used to identify a discrete-time …

Analysis and control of network systems largely rely on the availability of the network
topology and the governing dynamics. In some cases, where the network dynamics and …

The traditional approach for the control of dynamical systems relies on the availability of a
model describing the system to be controlled. Typically, a model is derived from first …