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
Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems:
Lei Cao,Shouli Gao,Dongya Zhao +2 more
TL;DR: A data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems that can be transformed into a dynamic linear data system by a novel dynamic linearization method.
Proceedings ArticleDOI
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
TL;DR: In this article , a Gaussian Process Port-Hamiltonian system (GPPHS) is proposed to generate passive systems with respect to designated inputs and outputs, preserving the compositional nature of Port- Hamiltonian systems.
Journal ArticleDOI
Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay
TL;DR: Numerical simulations of MAS with unknown time-varying delays and disturbance verify that, while providing comparable performance to the model-based control for many different system configurations, the DL-based controls with explicit knowledge of the control signal structure are preferred to that with implicit knowledge ofThe control signal or no knowledge, which shows the promising potential of DL- based control with supervised learning.
Journal ArticleDOI
Data-Driven Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Parameter Design Using Virtual Reference Feedback Tuning
Satoshi Tsuruhara,Kazuhisa Ito +1 more
TL;DR: In this article , a controller design that requires no precise mathematical model and less design parameter tuning with trial and error was developed by combining conventional MFAC and virtual reference feedback tuning, which is a data-driven control method.
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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