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 Internal Model Control of Second-Order Discrete Volterra Systems
TL;DR: In this paper, the data-driven approach is extended to a class of nonlinear systems, namely second-order discrete Volterra systems, and two main contributions are made for this class of systems.
Journal ArticleDOI
The Generation Mechanism of Tracking Error During Acceleration or Deceleration Phase in Ultraprecision Motion Systems
TL;DR: In this article, the authors studied the generation mechanism of the tracking error during the acceleration or deceleration phase in ultraprecision motion systems and provided a criterion to judge the accuracy of feedforward coefficients by the shape of residual tracking error.
ReportDOI
Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies
Marina Sofos,Jared Langevin,Michael Deru,Erika Gupta,Kyle Benne,David Blum,Theodore Bohn,Robert L. Fares,Nick Fernandez,Glenn A. Fink,Steven Frank,Jennifer Gerbi,Jessica Granderson,Dale Hoffmeyer,Tianzhen Hong,Amy Jiron,Stephanie Johnson,Srinivas Katipamula,Teja Kuruganti,William Livingood,Ralph T. Muehleisen,Monica Neukomm,Valerie Nubbe,Patrick E. Phelan,M. A. Piette,Janet Reyna,Amir Roth,Aven Satre-Meloy,Michael Specian,Draguna Vrabie,Michael Wetter,Steve Widergren +31 more
Journal ArticleDOI
Data-driven iterative tuning based active disturbance rejection control for piezoelectric nano-positioners
Chuan Tian,Peng Yan +1 more
TL;DR: A data-driven Iterative Feedback Tuning (IFT) based Active Disturbance Rejection Control (ADRC) approach is developed to optimize the control performance by conducting controller parameter tuning iteratively from experimental test data.
Journal ArticleDOI
Observer-Based Sampled-Data Model-Free Adaptive Control for Continuous-Time Nonlinear Nonaffine Systems With Input Rate Constraints
TL;DR: A sampled-data model-free adaptive control (SMFAC) strategy is proposed for continuous-time nonlinear nonaffine systems with input rate constraints, including an observer-based SMFAC (ObSMFac) scheme, including a sampled- data parameter estimator to estimate the unknown partial derivatives and a sampling-data observer to estimateThe residual nonlinear uncertainty, respectively.
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.
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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.
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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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