This paper presents a risk-informed data-driven safe control design approach for a class of
stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled …

Machine learning techniques, including Gaussian processes (GPs), are expected to play a
significant role in meeting speed, accuracy, and functionality requirements in future data …

The Linear Parameter-Varying (LPV) framework provides a modeling and control design
toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite …

In many engineering applications, continuous-time models are preferred to discrete-time
ones, in that they provide good physical insight and can be derived also from non-uniformly …

Today, the need to increase efficiency and performance of dynamical systems leads to
innovative control solutions that rely on accurate representations of the underlying system …

The use of mechatronic integrated equipment, such as servo feed drive systems, has
become increasingly important in high-end manufacturing, aerospace, and semiconductor …

In this work, a two-dimensional (2D) kernel-based Gaussian process regression (GPR)
method for the identification of batch process is proposed. Under the GPR framework, the …

This article develops a robust global strategy for identifying the linear parameter varying
(LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and …

This article presents a data-driven safe reinforcement learning (RL) algorithm for discrete-
time nonlinear systems. A data-driven safety certifier is designed to intervene with the …

Abstract For Linear Time-Invariant (LTI) systems, Frequency Response Functions (FRFs)
facilitate dynamics analysis, controller design, and parametric modeling, while many …