Details
Iterative Learning Control with Passive Incomplete Information
Algorithms Design and Convergence Analysis|
139,09 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 16.04.2018 |
| ISBN/EAN: | 9789811082672 |
| Sprache: | englisch |
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Beschreibungen
<p>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.</p> <p>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.</p> 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.<br/>
<p>Introduction,-Random Sequence Model for Linear Systems,- Random Sequence Model for Nonlinear Systems,-Random Sequence Model for Nonlinear Systems with Unknown Control Direction,- Bernoulli Variable Model for Linear Systems,- Bernoulli Variable Model for Nonlinear Systems,- Markov Chain Model for Linear Systems,- Two-Side Data Dropout for Linear Deterministic Systems,- Two-Side Data Dropout for Linear Stochastic Systems,- Two-Side Data Dropout for Nonlinear Systems,- Multiple Communication Conditions and Finite Memory,- Random Iteration-Varying Lengths for Linear Systems,- Random Iteration-Varying Lengths for Nonlinear Systems,- Iterative Learning Control for Large-Scale Systems,- Appendix,- Index</p>
Dr. Dong SHEN received the B.S. degree in mathematics from Shandong University, Jinan, China, in 2005. He received the Ph.D. degree in mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010.<div><br/></div><div>From 2010 to 2012, Dr. Shen was a Post-Doctoral Fellow with the Institute of Automation, CAS. Since 2012, he has been an Associate Professor with College of Information Science and Technology, Beijing University of Chemical Technology (BUCT), Beijing, China. He was a visiting scholar at National University of Singapore from 2016 to 2017.</div><div><br/></div><div>Dr. Shen's current research interests include iterative learning control, stochastic control and optimization. He has published more than 50 refereed journal and conference papers. He is the author of Stochastic Iterative Learning Control (Science Press, 2016, in Chinese), co-author of Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017), and co-editor of Service Science, Management and Engineering: Theory and Applications (Academic Press and Zhejiang University Press, 2012). Dr. Shen received the IEEE CSS Beijing Chapter Young Author Prize in 2014 and the Wentsun Wu Artificial Intelligence Science and Technology Progress Award in 2012.</div><div><br/></div>
<p>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.</p> <p>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.</p> 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.<br/>
Presents a comprehensive discussion of iterative learning control (ILC) in various data dropout environments Proposes several systematic procedures for the design and analysis of ILC for stochastic systems with passive incomplete information Details the current research on stochastic ILC and the major research techniques used
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