This paper provides an overview of the recent developments in data-based techniques
focused on modern industrial applications. As one of the hottest research topics for …

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 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 …

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 …

During the past half century, modern control theory has developed greatly and many
branches and subfields have emerged, for example, linear system theory, optimal control …

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed
based on a new dynamic linearization technique (DLT) with a novel concept called pseudo …

In this work, a novel data-driven control approach, model-free adaptive control, is presented
based on a new dynamic linearization technique for a class of discrete-time single-input and …

In a recent article, we have shown how to learn controllers for unknown linear systems using
finite-length noisy data by solving linear matrix inequalities. In this article, we extend this …

Combining control engineering with nonparametric modeling techniques from machine
learning allows for the control of systems without analytic description using data-driven …

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual
successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the …