Description: Discrete-time Adaptive Iterative Learning Control : From Model-based to Data-driven, Paperback by Chi, Ronghu; Lin, Na; Zhang, Huimin; Zhang, Ruikun, ISBN 9811904669, ISBN-13 9789811904660, Brand New, Free shipping in the US 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.
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End Time: 2024-11-19T16:15:53.000Z
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Restocking Fee: No
Return shipping will be paid by: Buyer
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Refund will be given as: Money Back
Book Title: Discrete-Time Adaptive Iterative Learning Control : from Model-Based to Data-Driven
Number of Pages: X, 206 Pages
Language: English
Publisher: Springer
Topic: Automation, Probability & Statistics / General, Computer Science, Electrical, Applied
Publication Year: 2023
Illustrator: Yes
Genre: Mathematics, Computers, Technology & Engineering
Item Weight: 11.9 Oz
Author: Huimin Zhang, Ruikun Zhang, Ronghu Chi
Item Length: 9.3 in
Item Width: 6.1 in
Book Series: Intelligent Control and Learning Systems Ser.
Format: Trade Paperback
