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
More filters
DissertationDOI
Application of Set Membership Identification to Controller Design
TL;DR: In this paper, a deterministic uncertainty model for set membership identification is proposed for constrained linear systems with multiple inputs and a computationally tractable algorithm for experiment design that is based on convex optimization is proposed.
Journal ArticleDOI
Nonlinear leader-following MASs control: a data-driven adaptive sliding mode approach with prescribed performance
Yanan Zhang,Jiacheng Song +1 more
Posted Content
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda,Jean de Becdelièvre,Jayesh K. Gupta,Ilan Kroo,Mykel J. Kochenderfer,Zachary Manchester +5 more
TL;DR: In this article, the authors formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation for offline identification of partially observed nonlinear systems.
Posted Content
Iterative data-driven inference of nonlinearity measures via successive graph approximation.
Tim Martin,Frank Allgöwer +1 more
TL;DR: An iterative data-driven approach to derive guaranteed bounds on non linearity measures of unknown nonlinear systems by given input-output samples based on a data-based non-parametric set-membership representation of the ground-truth system and local inferences of nonlinearity measures.
Data-Driven Control of Stochastic Systems: An Innovation Estimation Approach
TL;DR: Numerical simulations show that by actively learning innovation from input-output data, remarkable improvement can be made over present formulations, thereby offering a promising framework for data-driven control of stochastic systems.
References
More filters
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.
Related Papers (5)
A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems
Zhongsheng Hou,Shangtai Jin +1 more
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems
Zhongsheng Hou,Shangtai Jin +1 more