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

Model estimation and structure detection with short data records are two issues that receive
increasing interests in System Identification. In this paper, a multiple kernel-based …

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 …

This paper derives a differential contraction condition for the existence of an orbitally-stable
limit cycle in an autonomous system. This transverse contraction condition can be …

This paper concerns maximum likelihood identification of linear time invariant state space
models, subject to model stability constraints. We combine Expectation Maximization (EM) …

Estimation of nonlinear dynamic models from data poses many challenges, including model
instability and nonconvexity of long-term simulation fidelity. Recently Lagrangian relaxation …

Model instability and poor prediction of long-term behavior are common problems when
modeling dynamical systems using nonlinear “black-box” techniques. Direct optimization of …

We consider the problem of designing an excitation input for a system idenfication
experiment. The optimization problem considered is to maximize a reduced Fisher …

In this paper, we introduce a novel class of neural differential equation, which are
intrinsically Lyapunov stable, exponentially stable or passive. We take a recently proposed …