We use physics-informed neural networks (PINNs) to numerically solve the discrete-time
nonlinear observer-based state estimation problem. Integrated within a single-step exact …

We extend the theory of Kazantzis–Kravaris/Luenberger (KKL) observers. These observers
consist in immersing the system into a linear stable filter of the output with sufficiently large …

This paper presents a first step towards tuning observers for general nonlinear systems.
Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we …

In this paper, we deal with output tracking control problems for input-affine nonlinear
systems. We propose a deep learning-based solution whose foundations lay in control …

Dynamics model learning deals with the task of inferring unknown dynamics from
measurement data and predicting the future behavior of the system. A typical approach to …

In this work, we consider the problem of global exponential synchronization of a network of
identical inputaffine nonlinear time-varying systems. To this end, we tackle the problem with …

Learning causal relationships in high-dimensional data (images, videos) is a hard task, as
they are often defined on low dimensional manifolds and must be extracted from complex …

The identification of a nonlinear dynamic model is an open topic in control theory, especially
from sparse input-output measurements. A fundamental challenge of this problem is that …

Abstract KKL (Kazantzis-Kravaris/Luenberger) observers are based on the idea of
immersing a given nonlinear system into a target system that is a linear stable filter of the …

This paper presents a new model-free methodology to learn Kazantzis-Kravaris-Luenberger
(KKL) observers for nonlinear systems. We address three major difficulties arising in …