This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

Motivated by the fact that many nonlinear plants can be represented through Linear
Parameter Varying (LPV) embedding, and being this framework very popular for control …

Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

In recent years, methods for data-driven Koopman spectral analysis, such as Dynamic Mode
Decomposition (DMD), have become increasingly popular approaches for extracting …

Dynamical systems that evolve continuously over time are ubiquitous throughout science
and engineering. Machine learning (ML) provides data-driven approaches to model and …

As a prerequisite of accurate process analysis, prediction and optimization, the precise
identification of process dynamic characteristics is of great significance. Traditional …

This letter synthesizes a gain-scheduled controller to stabilize all possible Linear Parameter-
Varying (LPV) plants that are consistent with measured input/state data records. Inspired by …

We derive novel methods that allow to synthesize LPV state-feedback controllers directly
from a single sequence of data and guarantee stability and performance of the closed-loop …

Cold atmospheric plasmas (CAPs) are increasingly used for treatment of complex surfaces
in biomedical and biomaterials processing applications. However, the multivariable …

Big data is an expression for massive data sets consisting of both structured and
unstructured data that are particularly difficult to store, analyze and visualize. Big data …