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BL
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| Record Number
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889269
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| Main Entry
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Shen, Dong,1982-
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| Title & Author
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Iterative learning control with passive incomplete information : : algorithms design and convergence analysis /\ Dong Shen.
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| Publication Statement
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Singapore :: Springer,, 2018.
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| Page. NO
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1 online resource
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| ISBN
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9789811082672
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: 9789811082689
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: 9789811341052
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: 9811082677
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: 9811082685
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: 9811341052
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9789811082665
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9811082669
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; Contents; 1 Introduction; 1.1 Iterative Learning Control-Why and How; 1.2 Basic Formulation of ILC; 1.2.1 Discrete-Time Case; 1.2.2 Continuous-Time Case; 1.3 ILC with Random Data Dropouts; 1.3.1 Data Dropout Models; 1.3.2 Data Dropout Positions; 1.3.3 Convergence Meanings; 1.4 ILC with Other Incomplete Information; 1.4.1 Communication Delay and Asynchronism; 1.4.2 Iteration-Varying Lengths; 1.5 Structure of This Monograph; 1.6 Summary; References; Part I One-Side Data Dropout; 2 Random Sequence Model for Linear Systems; 2.1 Problem Formulation.
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2.2 Intermittent Update Scheme and Its Almost Sure Convergence2.3 Extension to Arbitrary Relative Degree Case with Mean Square Convergence; 2.3.1 Noise-Free System Case; 2.3.2 Stochastic System Case; 2.4 Illustrative Simulations; 2.5 Summary; References; 3 Random Sequence Model for Nonlinear Systems; 3.1 Problem Formulation; 3.2 Intermittent Update Scheme and Its Convergence; 3.3 Successive Update Scheme and Its Convergence; 3.4 Illustrative Simulations; 3.5 Summary; References; 4 Random Sequence Model for Nonlinear Systems with Unknown Control Direction; 4.1 Problem Formulation.
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4.2 Intermittent Update Scheme and Its Almost Sure Convergence4.3 Proofs of Lemmas; 4.4 Illustrative Simulations; 4.5 Summary; References; 5 Bernoulli Variable Model for Linear Systems; 5.1 Problem Formulation; 5.2 Intermittent Update Scheme and Its Almost Sure Convergence; 5.3 Successive Update Scheme and Its Almost Sure Convergence; 5.4 Mean Square Convergence of Intermittent Update Scheme; 5.4.1 Noise-Free System Case; 5.4.2 Stochastic System Case; 5.5 Illustrative Simulations; 5.5.1 System Description; 5.5.2 Tracking Performance of both Schemes.
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5.5.3 Comparison of Different Data Dropout Rates5.5.4 Comparison of Different Learning Gains; 5.5.5 Comparison with Conventional P-Type Algorithm; 5.6 Summary; References; 6 Bernoulli Variable Model for Nonlinear Systems; 6.1 Problem Formulation; 6.2 Intermittent Update Scheme and Its Almost Sure Convergence; 6.3 Successive Update Scheme and Its Almost Sure Convergence; 6.4 Illustrative Simulations; 6.5 Summary; References; 7 Markov Chain Model for Linear Systems; 7.1 Problem Formulation; 7.2 ILC Algorithms; 7.3 ILC for Classical Markov Chain Model Case.
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7.4 ILC for General Markov Data Dropout Model Case7.5 Illustrative Simulations; 7.6 Summary; References; Part II Two-Side Data Dropout; 8 Two-Side Data Dropout for Linear Deterministic Systems; 8.1 Problem Formulation; 8.2 ILC Algorithms; 8.3 Markov Chain Model of Input Evolution; 8.4 Convergence Analysis; 8.5 Illustrative Simulations; 8.6 Summary; References; 9 Two-Side Data Dropout for Linear Stochastic Systems; 9.1 Problem Formulation; 9.2 Markov Chain of Input Evolution; 9.3 Convergence Analysis; 9.4 Discussions on Convergence Speed; 9.5 Illustrative Simulations; 9.6 Summary; References.
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| Abstract
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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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| Subject
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Intelligent control systems.
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Iterative methods (Mathematics)
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Intelligent control systems.
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Iterative methods (Mathematics)
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.8/312
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| LC Classification
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TJ217.5
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