Journal ArticleDOI
Iterative Learning Control: Brief Survey and Categorization
Hyo-Sung Ahn,YangQuan Chen,Kevin L. Moore +2 more
- Vol. 37, Iss: 6, pp 1099-1121
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TLDR
The iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors.Abstract:
In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.read more
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Journal ArticleDOI
Data-Based Techniques Focused on Modern Industry: An Overview
TL;DR: The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view.
Journal ArticleDOI
From model-based control to data-driven control: Survey, classification and perspective
Zhongsheng Hou,Zhuo Wang +1 more
TL;DR: 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.
Journal ArticleDOI
Survey on iterative learning control, repetitive control, and run-to-run control
TL;DR: Three control methods—iterative learning control, repetitive control (RC), and run-to-run control (R2R)—are studied and compared and some promising fields for learning-type control are revealed.
Book
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton,J. Nathan Kutz +1 more
TL;DR: In this paper, the authors bring together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science, and highlight many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy.
Journal ArticleDOI
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems
Zhongsheng Hou,Shangtai Jin +1 more
TL;DR: A data-driven model-free adaptive control approach based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems.
References
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Journal ArticleDOI
Bettering Operation of Robots by Learning
TL;DR: A betterment process for the operation of a mechanical robot in a sense that it betters the nextoperation of a robot by using the previous operation's data is proposed.
Journal ArticleDOI
A survey of iterative learning control
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Book
Iterative Learning Control for Deterministic Systems
TL;DR: The material presented in this book addresses the analysis and design of learning control systems using a system-theoretic approach, and the application of artificial neural networks to the learning control problem.
Journal ArticleDOI
Iterative learning control and repetitive control for engineering practice
TL;DR: In this article, the authors discuss linear iterative learning and repetitive control, presenting general purpose control laws with only a few parameters to tune The method of tuning them is straightforward, maki