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
Proceedings ArticleDOI
Non-Parametric Modeling of Motion Control Systems Using an Hybrid MODE-NARX Algorithm
TL;DR: The proposed hybrid technique outperformed the common well-known PEM-ARMA model with up to 80% better accuracy, and better generalization performance across varying datasets.
Proceedings Article
Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control
TL;DR: It is shown that TS achieves order-optimal regret in adaptive control of multidimensional stabilizable LQRs by carefully prescribing an early exploration strategy and a policy update rule, thereby solving the open problem posed in Abeille and Lazaric (2018.
Posted Content
Safe learning-based trajectory tracking for underactuated vehicles with partially unknown dynamics.
TL;DR: A safe tracking control law for underactuated vehicles is presented using a learning-based oracle for the prediction of the unknown dynamics and guarantees the boundedness of the tracking error with high probability where the bound is explicitly given.
Proceedings ArticleDOI
Data-based control design for linear discrete-time systems with robust stability guarantees
William D'amico,Marcello Farina +1 more
TL;DR: In this paper , the authors proposed a method based on virtual reference feedback tuning with robust closed-loop stability guarantees in a linear single-input and single-output setting, which is not a fully direct data-driven approach since an uncertainty set for the system is obtained through set membership identification.
From Model-Based to Data-Driven Discrete-Time Iterative Learning Control
TL;DR: The last work presented here finally uses model-free reinforcement learning (RL) to eliminate the need for an a priori model, analogous to direct adaptive control using data to directly produce the gains in the ILC law without use of a model.
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