Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

This article introduces recurrent equilibrium networks (RENs), a new class of nonlinear
dynamical models for applications in machine learning, system identification, and control …

This tutorial paper provides an introduction to recently developed tools for machine learning,
especially learning dynamical systems (system identification), with stability and robustness …

Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to
model sequence-to-sequence maps. RNNs have excellent expressive power but lack the …

This survey provides an overview of combining federated learning (FL) and control to
enhance adaptability, scalability, generalization, and privacy in (nonlinear) control …

We present a novel framework for transferring the knowledge from one system (source) to
design a stabilizing controller for a second system (target). Our motivation stems from the …

In this paper we prove new connections between two frameworks for analysis and control of
nonlinear systems: the Koopman operator framework and contraction analysis. Each …

The vector autoregression (VAR) has been widely used in system identification,
econometrics, natural science, and many other areas. However, when the state dimension …

In this paper, we use the maximum likelihood principle and the data filtering technique to
study the identification issue of the multivariate equation-error system whose outputs are …

We propose a principled method for projecting an arbitrary square matrix to the nonconvex
set of asymptotically stable matrices. Leveraging ideas from large deviations theory, we …