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
Frequency Data-Based Procedure to Adjust Gain and Phase Margins and Guarantee the Uniqueness of Crossover Frequencies
TL;DR: This paper improves a newly proposed data-driven tuning procedure for arbitrarily setting the values of gain and phase margins and crossover frequencies, in the viewpoint of guaranteeing the uniqueness of crossover frequencies.
Book ChapterDOI
Fault-Diagnosis and Fault-Tolerant-Control in Industrial Processes and Electrical Drives
TL;DR: This chapter presents a discussion of general methods applied in fault-diagnosis and fault-tolerant control systems, using passive and active concepts, from the point of view of electric drives.
Journal ArticleDOI
A Robust Data-Driven Controller Design Methodology With Applications to Particle Accelerator Power Converters
TL;DR: A new data-driven approach using the frequency response function (FRF) of a system is proposed for designing robust-fixed structure digital controllers for particle accelerators’ power converters and the effectiveness of the method is illustrated by considering two case studies that require robust controllers for achieving the desired performance.
DissertationDOI
Evolutionary Algorithm for Adaptive Quantum-Channel Control
TL;DR: A robust search algorithm based on an evolutionary algorithm that is ignorant of the properties of the phase noise but is still able to deliver quantum-enhanced precision is devised, which compares the performances of feedback control policies designed using Bayesian inference to policies generated using this robust evolutionary algorithm on their performance in both noisy and noiseless interferometers.
Journal ArticleDOI
Data-driven approximate value iteration with optimality error bound analysis
TL;DR: A quantitative analysis result is given on the error bound between the optimal cost and the cost under the designed controller of the data-driven AVI algorithm, an approximate solution for the optimal control problem.
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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A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems
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