Iterative Learning Control with Passive Incomplete Information

Algorithms Design and Convergence Analysis

Szerző: Dong Shen

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer Verlag, Singapore

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

53 484 Ft

This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplet...

Információk a könyvről

Szerző

Dong Shen

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2018

oldal

294

EAN

9789811082665

ISBN

9811082669

Enbook ID

18803756

Kiadó

Springer Verlag, Singapore

Súly

629

Méretek

155 x 235 x 24

Teljes leírás

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.

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Iterative Learning Control with Passive Incomplete Information

Algorithms Design and Convergence Analysis

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