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
Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots.
TL;DR: This work proposes using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs) and shows an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.
A novel multi-source sensing data fusion driven method for detecting rolling mill health states under imbalanced and limited datasets
Journal ArticleDOI
Auxiliary Predictive Compensation-Based ILC for Variable Pass Lengths
Na Lin,Ronghu Chi,Biao Huang +2 more
TL;DR: An auxiliary predictive compensation-based ILC (APC-ILC) method is presented by defining an expanded output variable in which the predictive output is incorporated to compensate the unavailable output data due to the shorter operation length.
Journal ArticleDOI
Data-Based Predictive Hybrid Driven Control for a Class of Imperfect Networked Systems
TL;DR: A data-based predictive hybrid driven control (DPHDC) approach is presented for a class of networked systems compromising both computation and communication delays, packet dropouts, and disturbances.
Journal ArticleDOI
Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control
TL;DR: The proposed M-DDAILC method for nonlinear multiple-input-multiple-output (MIMO) systems by virtue of multi-lagged-input iterative dynamic linearization (IDL) is proved to be iteratively convergent with rigorous analysis.
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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