A brief overview on the model-based control and data-driven control methods is presented.
The data-driven equivalent dynamic linearization, as a foundational analysis tool of data …

Freeway traffic control is a broad research area, not only interesting for its applicative
perspective, but also highly motivating for theoretical investigations. This research topic has …

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 non-model-based approach is proposed for the adaptive optimal
control of a class of connected vehicles that is composed of n human-driven vehicles only …

In this paper, by splitting a traffic flow series into basis series and deviation series, the
concepts of similarity and repeatability of traffic flow patterns are defined using the statistic …

The terminal iterative learning control (TILC) method is introduced for the first time into the
field of train station stop control and three TILC-based algorithms are proposed in this study …

This book explores the development of PDE (partial differential equation) backstepping
controllers for the suppression of stop-and-go instabilities and oscillations in congested …

Abstract This paper studies Deep Convolutional Neural Networks (DCNNs) for the accurate
estimation of space–time traffic speeds given sparse data on freeways. Several aspects are …

ILC has been a major control design methodology for twenty years; numerous algorithms
have been developed to solve real-time control problems, from MEMS to batch reactors …