Journal ArticleDOI
From model-based control to data-driven control: Survey, classification and perspective
Zhongsheng Hou,Zhuo Wang +1 more
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
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Proceedings ArticleDOI
Data-Driven Model Predictive Control for Linear Time-Periodic Systems
TL;DR: In this article , the authors considered the problem of data-driven predictive control for an unknown discrete-time linear time-periodic (LTP) system of known period, and proposed a strategy that generalizes both data-enabled predictive control (DeePC) and subspace predictive Control (SPC).
Journal ArticleDOI
Iterative Data-Driven Control for Closed Loop with Two Unknown Controllers
TL;DR: The purpose of this paper derives that, in case of two parametrized controllers, the iterative idea is performed to identify these two unknown parameter vectors, even when parameters converge to their true values.
Journal ArticleDOI
Linear matrix inequality relaxations and its application to data‐driven control design for switched affine systems
Alexandre Seuret,Carolina Albea +1 more
TL;DR: In this article , robust hybrid limit cycles for uncertain switched affine systems, robust model-based and then data-driven control laws are designed based on a Lyapunov approach.
Journal ArticleDOI
A Novel Hybrid Data-Driven Modeling Method for Missiles
Yongxiang He,Hongwu Guo,Yang Han +2 more
TL;DR: The proposed hybrid data-driven modeling method is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles, and can provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios.
References
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Book
System Identification: Theory for the User
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
Chris Watkins,Peter Dayan +1 more
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.
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