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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Book ChapterDOI
Machine Learning for Process-X: A Taxonomy.
TL;DR: A taxonomy of process-X approaches that sharpens the role of machine learning in these applications and identifies future research directions for appliedMachine learning in cyber-physical systems is derived.
Book ChapterDOI
Overview of Multiagent Systems Cooperation
TL;DR: In this article, the authors proposed a new engineering style to achieve many complex coordination tasks, which is difficult to be implemented based on individual agent, during the rapid and sustained development of industrial and civilian demand, modern control systems are becoming more and more open, complex, heterogeneous and highly distributed.
Proceedings ArticleDOI
Model-free adaptive control based on local dynamic linearization
Ronghu Chi,Shuhua Zhang,Yu Hui +2 more
TL;DR: A new local dynamic linearization method is proposed using differential mean-value theorem, which can be estimated by using the I/O data only, and a new model-free adaptive control is proposed byUsing the principle of optimality, where the controller design and analysis is data-driven without using any model information.
Proceedings ArticleDOI
A model-free adaptive switching control approach for a class of nonlinear systems
TL;DR: A model-free adaptive switching control (MFASC) approach is proposed for a class of discrete-time nonlinear systems with unknown control direction that depends merely on the measured input and output data of the controlled plant without requiring any other modeling information of the plant.
Journal ArticleDOI
Control of a complex multistep process for the production of mesalazine
Ismael Castillo,Jakob Rehrl,Peter Sagmeister,René Lebl,Julia Kruisz,Selma Celikovic,Martin Sipek,Dirk Kirschneck,Martin Horn,Stephan Sacher,David Cantillo,Jason D. Williams,Johannes Khinast,C. Oliver Kappe +13 more
TL;DR: In this paper , the authors presented the application of Data Driven Modelling (DDM) and Non-Linear Model Predictive Control (NMPC) for the control implementation of a continuous reactor for the production of the active pharmaceutical Ingredient (API), Mesalazine.
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.
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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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