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 controller design via a frequency domain approach with application on structure control
TL;DR: A frequency domain approach for designing a data-driven controller that does not require a parametric model but only needs the input-output open loop test data for the purpose of spectral analysis to give an optimal parameter design for a pre-defined structure controller.
Posted Content
Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems
TL;DR: In this paper, the authors presented a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states.
Journal ArticleDOI
Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics
TL;DR: A learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process, which is used to optimize and apply a feedforward control input on each trial, and which works plug and play.
A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization
TL;DR: A data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance and simple convex feasibility constraints are devised which can be used to stabilize systems with multi-model uncertainty.
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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