Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Comple

This book aims to provide a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics.

1 Introduction 1.1 Discrete-Time Stochastic Systems 1.2 Network-Enhanced Complexities 1.3 Performance Analysis and Engineering Design Synthesis 1.4 Outline 2 Finite-Horizon H Control with Randomly Occurring Non-linearities and Fading Measurements 2.1 Modeling and Problem Formulation 2.2 H Performance Analysis 2.3 H Controller Design 2.4 Simulation Examples 2.5 Summary 3 Finite-Horizon H Consensus Control for Multi-Agent Systems with Missing Measurements 3.1 Modeling and Problem Formulation 3.2 Consensus Performance Analysis 3.3 H Controller Design 3.4 Simulation Examples 3.5 Summary 4 Finite-Horizon Distributed H State Estimation with Stochastic Parameters through Sensor Networks 4.1 Modeling and Problem Formulation 4.2 H Performance Analysis 4.3 Distributed Filter Design 4.4 Simulation Examples 4.5 Summary 5 Finite-Horizon Dissipative Control for State-Saturated Discrete Time-Varying Systems with Missing Measurements 5.1 Modeling and Problem Formulation 5.2 Dissipative Control for Full State Saturation Case 5.3 Dissipative Control for Partial State Saturation Case 5.4 Simulation Examples 5.5 Summary 6 Finite-Horizon H Filtering for State-Saturated Discrete Time-Varying Systems with Packet Dropouts 6.1 Modeling and Problem Formulation 6.2 H Filtering for Full State Saturation Case 6.3 H Filtering for Partial State Saturation Case 6.4 Simulation Examples 6.5 Summary 7 Finite-Horizon Envelope-Constrained H Filtering with Fading Measurements 7.1 Modeling and Problem Formulation 7.2 H Performance Analysis 7.3 Envelope Constraint Analysis 7.4 Envelope-Constrained H Filter Design 7.5 Simulation Examples 7.6 Summary 8 Distributed Filtering under Uniform Quantizations and Deception Attacks through Sensor Networks 8.1 Modeling and Problem Formulation 8.2 Distributed Filter Design 8.3 Boundedness Analysis 8.4 Simulation Examples 8.5 Summary 9 Event-Triggered Distributed H State Estimation with Packet Dropouts through Sensor Networks 9.1 Modeling and Problem Formulation 9.2 H Performance Analysis 9.3 H Estimator Design 9.4 Simulation Examples 9.5 Summary 10 Event-Triggered Consensus Control for Multi-Agent Systems in the Framework of Input-to-State Stability in Probability 10.1 Modeling and Problem Formulation 10.2 Analysis of Input-to-State Stability in Probability 10.3 Event-triggered Consensus Control for Multi-agent Systems 10.4 Simulation Examples 10.5 Summary 11 Event-Triggered Security Control for Discrete-Time Stochastic Systems subject to Cyber-Attacks 11.1 Problem Formulation 11.2 Security Performance Analysis 11.3 Security Controller Design 11.4 Simulation Examples 11.5 Summary 12 Event-Triggered Consensus Control for Multi-Agent Systems subject to Cyber-Attacks in the Framework of Observers 12.1 Modeling and Problem Formulation 12.2 Consensus Analysis 12.3 Consensus Controller Design 12.4 Simulation Examples 12.5 Summary Bibliography