Discrete-Time Adaptive Iterative Learning Control: From Model-Based to Data-Driven (1st ed. 2022) (Intelligent Control and Learning Systems #1)

By: Ronghu Chi and Na Lin and Huimin Zhang and Ruikun Zhang

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Synopsis: This book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various applications. This book is intended for academic scholars, engineers and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.

Book Quality:: Publisher Quality
ISBN-13:: 9789811904646
Related ISBNs:: 9789811904639
Publisher:: Springer Singapore, Singapore
Date of Addition:: 04/21/22
Copyrighted By:: The Editor
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Technology, Mathematics and Statistics
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.