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Iterative Learning Control with Passive Incomplete Information: Algorithms Design and Convergence Analysis book cover by Dong Shen
Hardcover

Iterative Learning Control with Passive Incomplete Information: Algorithms Design and Convergence Analysis

by Dong Shen

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Total $3,519.75

Hardcover · 294 pages Springer Nature Singapore · 2018

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About the book

Iterative Learning Control with Passive Incomplete Information

This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC.

It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints.

With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.


Details

Publisher
Springer Nature Singapore
Author
Dong Shen
Format
Hardcover
Published
Apr 26, 2018
ISBN-13
9789811082665
Pages
294
Printing
Printed on demand
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