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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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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.
Journal ArticleDOI

Data-driven iterative tuning based active disturbance rejection control for piezoelectric nano-positioners

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

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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