Performance analysis and synthesis for discrete-time stochastic systems with network-enhanced complexities

Description

The book addresses the system performance with a focus on the network-enhanced complexities and developing the engineering-oriented design framework of controllers and filters with potential applications in system sciences, control engineering and signal processing areas. Therefore, it provides 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. Such a result will be of great importance in the development of novel control and filtering theories including industrial impact. Key Features: Provides original methodologies and emerging concepts to deal with latest issues in the control and filtering with an emphasis on a variety of network-enhanced complexities; Gives results of stochastic control and filtering distributed control and filtering, and security control of complex networked systems; Captures the essence of performance analysis and synthesis for stochastic control and filtering; Concepts and performance indexes proposed reflect the requirements of engineering practice; Methodologies developed in this book include backward recursive Riccati difference equation approach and the discrete-time version of input-to-state stability in probability.  Read more... Abstract: 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--  Read more... Content: 1 Introduction 1.1 Discrete-Time Stochastic Systems 1.2 Network-Enhanced Complexities1.3 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 Analysis2.3 H Controller Design 2.4 Simulation Examples 2.5 Summary 3 Finite-Horizon H Consensus Control for Multi-Agent Systems with Missing Measurements3.1 Modeling and Problem Formulation3.2 Consensus Performance Analysis 3.3 H Controller Design3.4 Simulation Examples3.5 Summary 4 Finite-Horizon Distributed H State Estimation with Stochastic Parameters through Sensor Networks4.1 Modeling and Problem Formulation 4.2 H Performance Analysis 4.3 Distributed Filter Design4.4 Simulation Examples4.5 Summary 5 Finite-Horizon Dissipative Control for State-Saturated Discrete Time-Varying Systems with Missing Measurements 5.1 Modeling and Problem Formulation5.2 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 Dropouts6.1 Modeling and Problem Formulation6.2 H Filtering for Full State Saturation Case6.3 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 Analysis7.3 Envelope Constraint Analysis7.4 Envelope-Constrained H Filter Design 7.5 Simulation Examples 7.6 Summary 8 Distributed Filtering under Uniform Quantizations and Deception Attacks through Sensor Networks8.1 Modeling and Problem Formulation 8.2 Distributed Filter Design 8.3 Boundedness Analysis 8.4 Simulation Examples8.5 Summary 9 Event-Triggered Distributed H State Estimation with Packet Dropouts through Sensor Networks 9.1 Modeling and Problem Formulation 9.2 H Performance Analysis9.3 H Estimator Design 9.4 Simulation Examples9.5 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 Formulation10.2 Analysis of Input-to-State Stability in Probability10.3 Event-triggered Consensus Control for Multi-agent Systems 10.4 Simulation Examples10.5 Summary 11 Event-Triggered Security Control for Discrete-Time Stochastic Systems subject to Cyber-Attacks11.1 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 Formulation12.2 Consensus Analysis 12.3 Consensus Controller Design 12.4 Simulation Examples 12.5 Summary Bibliography