LEADER 06497nam 22005533 450 001 9910853989003321 005 20240912160740.0 010 $a9789819709267 010 $a9819709261 035 $a(MiAaPQ)EBC31230615 035 $a(Au-PeEL)EBL31230615 035 $a(CKB)31120641000041 035 $a(MiAaPQ)EBC31254305 035 $a(Exl-AI)31230615 035 $a(EXLCZ)9931120641000041 100 $a20240401d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIterative Learning Control for Network Systems under Constrained Information Communication 205 $a1st ed. 210 $cSpringer Nature$d2024 210 1$aSingapore :$cSpringer Singapore Pte. Limited,$d2024. 210 4$dİ2024. 215 $a1 online resource (229 pages) 225 1 $aIntelligent Control and Learning Systems Series ;$vv.12 311 08$a9789819709250 311 08$a9819709253 327 $aIntro -- Preface -- Contents -- Symbols -- 1 Introduction -- 1.1 Iterative Learning Control -- 1.1.1 Current Status of ILC Research -- 1.2 Network Systems -- 1.2.1 Constrained Information Communication -- 1.2.2 Basic Knowledge -- 1.2.3 Notations -- 1.3 Structure of This Monograph -- 1.4 Summary -- References -- 2 Consensus Under Event-Triggered Transmission and Quantization -- 2.1 Introduction -- 2.2 Problem Formulation -- 2.3 ILC Scheme -- 2.3.1 Event-Triggered Transmission and Quantization -- 2.3.2 ILC Scheme Design -- 2.4 Convergence Analysis -- 2.4.1 The Consensus Under Event-Triggered Strategy Without Quantization -- 2.4.2 The Consensus Under Event-Triggered Strategy with Quantization -- 2.5 Simulation Example -- 2.6 Summary -- References -- 3 Consensus Under Limited Information Communication -- 3.1 Introduction -- 3.2 Problem Formulation -- 3.2.1 Quantizer Design -- 3.2.2 ILC Scheme Design -- 3.3 Main Results -- 3.4 Simulation Example -- 3.5 Summary -- References -- 4 Consensus Under Switching Topology and Observer Information -- 4.1 Introduction -- 4.2 Problem Formulation -- 4.3 ILC Scheme -- 4.3.1 Controller Design -- 4.3.2 Initial State Learning Scheme -- 4.4 Convergence Analysis -- 4.5 Simulation Example -- 4.6 Summary -- References -- 5 Tracking Under Measurable and Unmeasurable State Information -- 5.1 Introduction -- 5.2 Problem Formulation -- 5.3 Sampling Protocols and ILC Schemes -- 5.3.1 Two Types of Sampling Protocols -- 5.3.2 Two Types of ILC Design -- 5.4 Convergence Analysis -- 5.4.1 Tracking with Measurability of the States -- 5.4.2 Tracking with Immeasurability of the States -- 5.5 Simulation Example -- 5.6 Summary -- References -- 6 Tracking Under Saturated Finite Interval and HNN-Structural Output -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.2.1 ILC Design -- 6.2.2 Definitions and Lemmas -- 6.3 Convergence Analysis. 327 $a6.3.1 Convergence Under Interval ILC Scheme -- 6.3.2 Convergence Under Saturated Interval ILC Scheme -- 6.4 Simulation Examples -- 6.5 Summary -- References -- 7 Tracking Based on Discontinuous Learning Control Strategy -- 7.1 Introduction -- 7.2 Problem Formulation -- 7.3 Finite-Time Tracking with Impulsive ILC -- 7.3.1 Control Strategy Design -- 7.3.2 Convergence Analysis -- 7.4 Finite-Time Tracking with ZOH Sampled-Data ILC -- 7.4.1 Control Strategy Design -- 7.4.2 Convergence Analysis -- 7.5 Simulation Example -- 7.6 Summary -- References -- 8 Finite-Iteration Learning Tracking with Packet Losses -- 8.1 Introduction -- 8.2 Problem Formulation -- 8.3 ILC Scheme -- 8.4 Main Results -- 8.4.1 The Finite-Iteration Tracking Without the Packet Dropout -- 8.4.2 The Finite-Iteration Tracking with the Packet Dropout -- 8.5 Simulation Example -- 8.6 Summary -- References -- 9 Finite-Iteration Learning Tracking with FlexRay Communication Protocol -- 9.1 Introduction -- 9.2 Problem Formulation -- 9.3 ILC Scheme Under FlexRay Protocol -- 9.4 Main Results -- 9.5 Simulation Example -- 9.6 Summary -- References -- 10 Multi-layered Sampled-Data Tracking Under Cooperative-Antagonistic Interactions -- 10.1 Introduction -- 10.2 Problem Formulation -- 10.2.1 Model Description -- 10.2.2 ILC Scheme Design -- 10.3 Main Results -- 10.3.1 Convergence Analysis with Cooperative-Antagonistic ILC Scheme -- 10.3.2 Convergence Analysis with Cooperative-Antagonistic Sampled-Data ILC Scheme -- 10.4 Simulation Example -- 10.5 Summary -- References -- 11 Stability of Multi-layer Supply Chain Networks with Constraints -- 11.1 Introduction -- 11.2 Problem Formulation -- 11.2.1 Model Description -- 11.2.2 Constraints of States and Objectives -- 11.3 Control Strategy with Limitations -- 11.4 Main Results -- 11.4.1 Learning Control Scheme Analysis -- 11.4.2 Convergence Analysis. 327 $a11.5 Simulation Example -- 11.6 Summary -- References -- 12 Security of Network Systems Under Cyber-Attack -- 12.1 Introduction -- 12.2 Problem Formulation -- 12.3 ILC Scheme Design -- 12.4 Main Results -- 12.4.1 Robust Convergence Analysis -- 12.4.2 Boundedness of All System Trajectories -- 12.5 Simulation Example -- 12.6 Summary -- References. 330 $aThis book focuses on the application of iterative learning control (ILC) techniques to networked systems with communication constraints. It addresses the challenges posed by complex network structures, such as data dropout and quantization, and explores solutions using ILC methods. The book is structured into 12 chapters, covering topics like consensus problems under limited information and tracking issues under various constraints like packet losses and switching topology. It is aimed at students, academics, and engineers in fields such as networked systems and control engineering, offering insights into both theoretical foundations and practical applications.$7Generated by AI. 410 0$aIntelligent Control and Learning Systems Series 606 $aCommand and control systems$7Generated by AI 606 $aCommunication in engineering$7Generated by AI 615 0$aCommand and control systems 615 0$aCommunication in engineering 700 $aXiong$b Wenjun$01737184 701 $aLuo$b Zijian$01737185 701 $aHo$b Daniel W. C$01737186 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910853989003321 996 $aIterative Learning Control for Network Systems under Constrained Information Communication$94158389 997 $aUNINA