This literature review extends and contributes to research on the development of data-driven
optimal control. Previous reviews have documented the development of model-based and …

This paper is a brief survey on the existing problems and challenges inherent in model-
based control (MBC) theory, and some important issues in the analysis and design of data …

This book categorizes the wide area of data-driven model-free controllers, reveals the exact
benefits of such controllers, gives the in-depth theory and mathematical proofs behind them …

A data-driven approach to control design has been develo**, since the early 1990's, upon
the concepts and the methods of classical control theory; to this approach we refer as data …

The soaring potential of edge computing leads to the emergence of cloud–edge
collaboration. This hierarchy enables the deployment of artificial intelligence models in the …

The frequency-domain data of a multivariable system in different operating points is used to
design a robust controller with respect to the measurement noise and multimodel …

In many industrial applications, finding a model from physical laws that is both simple and
reliable for control design is a hard and time‐consuming undertaking. When a set of …

This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

In this study, a data-driven technique is proposed to deal with multivariable fixed-order
controller design. The method is based on the virtual reference feedback tuning (VRFT) …

In this tutorial paper, we give a basic concept of FRIT (fictitious reference iterative tuning) as
one of the data-driven controller tuning methods. We explain how FRIT can achieve a …