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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.
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This article is published in Information Sciences.The article was published on 2013-06-01. It has received 828 citations till now.

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Journal ArticleDOI

Learning quadrotor dynamics for precise, safe, and agile flight control

TL;DR: In this article , the authors present the key challenges to augment or replace classic physic-based dynamic modeling and control techniques with data-driven approaches that can offer several key benefits in terms of flight precision, safety, adaptation, and agility.
Proceedings ArticleDOI

A DC/DC Buck-Boost Converter-Inverter-DC Motor Control based on model-free PID Controller tuning by Adaptive Safe Experimentation Dynamics Algorithm

TL;DR: In this article , the adaptive safe experimentation dynamics (ASED) method was adopted as the optimization approach with considerable precision as needed towards fine-tuning the PID controller for its ability to minimize both output of control tracking and energy consumption at reduced processing interval by the exclusion of mathematical modeling in assessing input and output of the system.
Proceedings ArticleDOI

Model-Free Control of Data Center Compressor-Based Cooling Systems

TL;DR: A model-free control approach is presented for the efficient management of data center cooling system and various combinations of modes of operation can be set by the control system to maximize the compressor-based cooling system overall operating efficiency.
Proceedings ArticleDOI

Data-driven Model-free Adaptive Control of High Power Converters in Super-large-scale Energy Storage Station

TL;DR: The simulation results show that the model free adaptive algorithm of power converter is effective in super-large-scale energy storage station.
References
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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.
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