Control over Communication Networks

Jianying Zheng is an Associate Professor at the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Liang Xu is a Professor at the Institute of Artificial Intelligence, Shanghai University, Shanghai, China. Qinglei Hu is a Professor at the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Lihua Xie is a Professor at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

• About the Authors xiiiPreface xvAcknowledgments xviiAcronyms xixList of Symbols xxi1 Introduction 11.1 Introduction and Motivation 11.1.1 Networked Control Systems 11.1.2 Multi-Agent Systems 21.2 Literature Review 41.2.1 Basics of Communication Theory 41.2.2 Stabilization of NCSs 61.2.2.1 Control over Noiseless Digital Channels 61.2.2.2 Control over Stochastic Digital Channels 71.2.2.3 Control over Analog Channels 81.2.3 LQ Optimal Control of NCSs over Fading Channels 91.2.4 Estimation of NCSs with Intermittent Communication 111.2.4.1 Stability of Kalman Filtering with Intermittent Observations 111.2.4.2 Remote State Estimation with Sensor Scheduling 121.2.5 Distributed Consensus of MASs 131.3 Preview of the Book 151.4 Preliminaries 181.4.1 Graph Theory 181.4.2 Hadamard Product and Kronecker Product 19Bibliography 202 Stabilization over Power Constrained Fading Channels 292.1 Introduction 292.2 Problem Formulation 292.3 Fundamental Limitations 312.4 Mean-Square Stabilizability 352.4.1 Scalar Systems 362.4.2 Two-Dimensional Systems 372.4.2.1 Communication Structure 382.4.2.2 Encoder/Decoder Design 382.4.2.3 Scheduler Design 392.4.2.4 Scheduler Parameter Selection 402.4.2.5 Proof of Theorem 2.3 412.4.3 High-Dimensional Systems: TDMA Scheduler 442.4.4 High-Dimensional Systems: Adaptive TDMA Scheduler 452.4.4.1 Scheduling Algorithm 462.4.4.2 Scheduler Parameter Selection 462.4.4.3 Proof of Theorem 2.5 462.5 Numerical Illustrations 512.5.1 Scalar Systems 512.5.2 Vector Systems 522.6 Conclusions 53Bibliography 533 Stabilization over Gaussian Finite-State Markov Channels 573.1 Introduction 573.2 Problem Formulation 583.2.1 Stability of Markov Jump Linear Systems 593.2.2 Sojourn Times for Markov Lossy Process 603.3 Fundamental Limitation 613.4 Stabilization over Finite-State Markov Channels 643.4.1 Communication Structure 653.4.2 Observer/Estimator/Controller Design 653.4.3 Encoder/Decoder/Scheduler Design 673.4.4 Sufficient Stabilizability Conditions 683.5 Stabilization over Markov Lossy Channels 713.5.1 Two-Dimensional Systems 713.5.1.1 Optimal Scheduler Design 723.5.1.2 Scheduler Parameter Selection 743.5.1.3 Sufficiency Proof of Theorem 3.4 753.5.2 High-Dimensional Systems 773.5.3 Numerical Illustrations 813.6 Conclusions 82Bibliography 834 Linear-Quadratic Optimal Control of NCSs with Random Input Gains 854.1 Introduction 854.2 Problem Formulation 864.3 Finite-Horizon LQ Optimal Control 884.4 Solvability of Modified Algebraic Riccati Equation 914.4.1 Cone-Invariant Operators 914.4.2 Solvability 974.5 LQ Optimal Control 1084.6 Conclusion 114Bibliography 1155 Multisensor Kalman Filtering with Intermittent Measurements 1175.1 Introduction 1175.2 Problem Formulation 1185.3 Stability Analysis 1205.3.1 Transmission Capacity 1205.3.2 Preliminaries 1205.3.3 Lower Bound 1215.3.4 Upper Bound 1245.3.5 Special Cases 1305.4 Examples 1315.5 Conclusions 132Bibliography 1336 Remote State Estimation with Stochastic Event-Triggered Sensor Schedule and Packet Drops 1356.1 Introduction 1356.2 Problem Formulation 1356.3 Optimal Estimator 1376.4 Suboptimal Estimators 1436.4.1 Fixed Memory Estimator 1436.4.2 Particle Filter 1456.5 Simulations 1496.6 Conclusions 151Bibliography 1527 Distributed Consensus over Undirected Fading Networks 1537.1 Introduction 1537.2 Problem Formulation 1547.3 Identical Fading Networks 1557.4 Nonidentical Fading Networks 1637.4.1 Definition of Edge Laplacian 1637.4.2 Sufficient Consensus Conditions 1647.5 Simulations 1687.6 Conclusions 170Bibliography 1708 Distributed Consensus over Directed Fading Networks 1738.1 Introduction 1738.2 Problem Formulation 1748.3 Identical Fading Networks 1748.3.1 Consensus Error Dynamics 1758.3.2 Consensusability Results 1778.3.3 Balanced Directed Graph Cases 1798.4 Definitions and Properties of CIIM, CIM, and CEL 1818.4.1 Definitions of CIIM, CIM, and CEL 1818.4.2 Properties of CIIM, CIM, and CEL 1828.5 Nonidentical Fading Networks 1858.5.1 Λ=μI 1898.5.1.1 Star Graphs 1908.5.1.2 Directed Path Graphs 1918.5.2 Λ ≠ μI 1928.6 Simulations 1928.7 Conclusions 194Bibliography 1959 Distributed Consensus over Networks with Communication Delay and Packet Dropouts 1979.1 Introduction 1979.2 Problem Formulation 1989.3 Consensusability with Delay and Identical Packet Dropouts 1999.3.1 Stability Criterion of NCSs with Delay and Multiplicative Noise 1999.3.2 Consensusability Conditions 2049.4 Consensusability with Delay and Nonidentical Packet Dropouts 2099.5 Illustrative Examples 2149.6 Conclusions 216Bibliography 21610 Distributed Consensus over Markovian Packet Loss Channels 21910.1 Introduction 21910.2 Problem Formulation 21910.3 Identical Markovian Packet Loss 22010.3.1 Analytic Consensus Conditions 22410.3.2 Critical Consensus Condition for Scalar Agent Dynamics 22610.4 Nonidentical Markovian Packet Loss 22810.5 Numerical Simulations 23210.6 Conclusions 234Bibliography 23511 Synchronization of the Delayed Vicsek Model 23711.1 Introduction 23711.2 Directed Graphs 23811.3 Problem Formulation 23911.4 Synchronization of Delayed Linear Vicsek Model 24011.5 Synchronization of Delayed Nonlinear Vicsek Model 24611.6 Simulations 24911.7 Conclusions 253Bibliography 253Index 255