scispace - formally typeset
Journal ArticleDOI

From model-based control to data-driven control: Survey, classification and perspective

TLDR
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-driven control (DDC) methods are here reviewed and addressed.
About
This article is published in Information Sciences.The article was published on 2013-06-01. It has received 828 citations till now.

read more

Citations
More filters
Journal ArticleDOI

Big Data for Modern Industry: Challenges and Trends [Point of View]

TL;DR: In this article, the authors explore ways in business and industry is working to manage data and reports on applications that support these initiatives, and report on how they can be used in the context of big data.
Journal ArticleDOI

State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review

TL;DR: This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches and delivers potential recommendations for the development of SOC estimation method of lithium-ion battery in EV applications.
Journal ArticleDOI

On Model-Free Adaptive Control and Its Stability Analysis

TL;DR: The theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle.
Journal ArticleDOI

An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications

TL;DR: This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers.
Posted Content

Formulas for Data-driven Control: Stabilization, Optimality and Robustness

TL;DR: A parametrization of linear feedback systems is derived that paves the way to solve important control problems using data-dependent linear matrix inequalities only and is remarkable in that no explicit system's matrices identification is required.
References
More filters
Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book ChapterDOI

A New Approach to Linear Filtering and Prediction Problems

TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Journal ArticleDOI

Machine learning

TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI

Technical Note : \cal Q -Learning

TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Related Papers (5)