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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Journal ArticleDOI
Adaptive Critic Nonlinear Robust Control: A Survey
Ding Wang,Haibo He,Derong Liu +2 more
TL;DR: This survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems and promotes the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.
Book
Subspace methods for system identification
TL;DR: In this article, the authors propose a formalization theory for the realization of deterministic problems in linear algebra and disctrete-time linear systems based on the Kalman filter.
Journal ArticleDOI
Data-driven model predictive control using random forests for building energy optimization and climate control
Francesco Smarra,Francesco Smarra,Achin Jain,Tullio de Rubeis,Dario Ambrosini,Alessandro D'Innocenzo,Rahul Mangharam +6 more
TL;DR: Data-driven model predictive control (DPC) as discussed by the authors leverages machine learning algorithms such as regression trees and random forests to identify a predictive model of a building and derive a controller based only on the data.
Journal ArticleDOI
Data Informativity: A New Perspective on Data-Driven Analysis and Control
TL;DR: This article develops a new framework in order to work with data that are not necessarily persistently exciting, and investigates necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems.
Journal ArticleDOI
A Novel Model-Free Adaptive Control Design for Multivariable Industrial Processes
Dezhi Xu,Bin Jiang,Peng Shi +2 more
TL;DR: The proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded.
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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