Communications for control in cyber physical systems theory, design and applications in smart grids
Communications and Controls in Cyber Physical Systems: Theory, Design and Applications in Smart Grids provides readers with all they need to know about cyber physical systems (CPSs), such as smart grids, which have attracted intensive studies in recent years. Communications and controls are of key i...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Massachusetts :
Morgan Kaufmann
2016.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629898106719 |
Tabla de Contenidos:
- Front Cover
- Communications for Control in Cyber Physical Systems: Theory, Design and Applications in Smart Grids
- Copyright
- Dedication
- Contents
- Biography
- Preface
- Chapter 1: Introduction to cyber physical systems
- 1.1 Introduction
- 1.2 Elements of a CPS
- Dynamics of CPS
- Linear time-invariant systems
- Observation model
- Feedback control
- 1.2.1 Communications for CPSs: Theoretical Studies
- Communication requirements
- Impact of communications on control
- Communication design
- 1.2.2 Communications for CPS: Industrial Systems
- 1.3 What is included and what is missing
- 1.3.1 Content
- 1.3.2 Missing Topics
- Chapter 2: Basics of communications
- 2.1 Introduction
- 2.2 Information Measures
- 2.2.1 Shannon Entropy
- 2.2.2 Mutual Information
- 2.3 Communication channels
- 2.3.1 Typical Channels
- 2.3.2 Mathematical Model of Channels
- 2.3.3 Channel Capacity
- 2.4 Source Coding
- 2.4.1 Lossless Source Coding
- 2.4.2 Lossy Source Coding
- 2.5 Modulation and coding
- 2.5.1 Channel Coding
- Error detection coding
- Error correction coding
- 2.5.2 Modulation
- 2.6 Networking
- 2.6.1 Graph Representation
- 2.6.2 Layered Structure
- 2.6.3 Cross-Layer Design
- 2.6.4 MAC Layer
- Multiple access schemes
- Scheduling
- 2.6.5 Network Layer
- 2.6.6 Transport Layer
- 2.7 Typical communication systems
- 2.7.1 Wired Networks
- 2.7.2 Wireless Networks
- 2.8 Conclusions
- Chapter 3: Basics of control
- 3.1 Introduction
- 3.2 Modeling of controlled dynamical systems
- 3.2.1 Continuous-Time Case
- Dynamics evolution
- Linear time-invariant systems
- Observation model
- Feedback control
- 3.2.2 Discrete-Time Case
- 3.2.3 Discrete or Hybrid Dynamical Systems
- 3.3 Observability and controllability
- 3.3.1 Observability
- 3.3.2 Controllability.
- 3.4 Optimal control
- 3.4.1 LQR Control
- 3.4.2 LQG Control
- 3.5 Conclusions
- Chapter 4: Typical cyber physical systems
- 4.1 Introduction
- 4.2 Power networks
- 4.2.1 Physical Dynamics
- 4.2.2 Protection
- 4.2.3 Smart Metering
- 4.2.4 Communication Systems in Industrial Control and Smart Grids
- 4.3 Robotic networks
- 4.3.1 Physical Dynamics
- Deterministic model
- Probabilistic model
- 4.3.2 Communications and Control
- 4.3.3 Coordination of Robots
- Rendezvous
- Connectivity maintenance
- 4.4 Conclusions
- Chapter 5: Communication capacity requirements
- 5.1 Introduction
- 5.1.1 Methodologies and Contexts
- 5.1.2 Basic Models
- 5.2 Deterministic System: Stability
- 5.2.1 Topological Entropy
- Spanning orbit-based definition
- Separated orbit-based definition
- Cover-based definition
- Equivalence
- 5.2.2 Communication Capacity Requirements
- System model
- Communication requirements for estimation
- Communication requirements for linear system control
- Communication requirements for optimal control
- 5.2.3 Calculation of Topological Entropy
- Linear systems
- Generic systems
- 5.3 Stochastic Systems: Estimation
- 5.3.1 System Model
- 5.3.2 Separation
- 5.3.3 Sequential Estimation
- Gauss-Markov source
- Noiseless digital channel
- 5.4 Stochastic Systems: Stability
- 5.4.1 System Model
- 5.4.2 Inadequacy of Channel Capacity
- 5.4.3 Anytime Capacity
- Necessity
- Sufficiency
- Encoding procedure
- 5.5 Stochastic Systems: Reduction of Shannon Entropy
- 5.5.1 Cybernetics Argument
- Law of requisite variety
- Shannon entropy in discrete-value dynamics
- Shannon entropy in continuous-value dynamics
- Controller design based on entropy
- Criticisms on entropy-based control
- 5.5.2 Does Practical Control Really Reduce Entropy?.
- Analytical results of entropy reduction
- 5.5.3 Discrete-State CPS: Entropy and Communications
- System model
- Entropy reduction in one time slot
- Entropy change in the long term
- 5.5.4 Continuous-State CPS: Entropy and Communications
- System model
- Traditional Bode's law
- Entropy reduction and Bode's law
- Communication requirement
- 5.6 Networked Stochastic Systems
- 5.6.1 System Model of Networked CPS
- Physical dynamics
- Communication network
- Feedback control
- 5.6.2 Entropy Propagation in CPS
- Motivating example
- Entropy propagation with perfect communications
- Interdependency of entropy and communications
- 5.6.3 Joint Evolution of Communication and Entropy
- 5.6.4 Continuous Space Limit
- Consensus dynamics
- Space condensation
- Diffusion of uncertainty
- 5.7 Control Communication Complexity
- 5.7.1 System Model
- 5.7.2 Control Communication Complexity
- Communication complexity
- Control communication complexity
- 5.8 Control and Information in Physics
- 5.8.1 Entropy and Control in Physics
- Second law of thermodynamics
- Maxwell's demon
- Szilard engine
- 5.8.2 Nonequilibrium Thermodynamics of Feedback Control
- System model
- Fluctuation theorem
- Feedback control
- Fluctuation theorem for feedback control
- Nonequilibrium equalities for feedback control
- Nonequilibrium equalities with efficacy
- 5.9 Conclusions
- Chapter 6: Network topology design
- 6.1 Introduction
- 6.2 WDM networks and design constraints
- 6.2.1 WDM Networks
- 6.2.2 Design Constraints
- 6.2.3 Optimization Procedure
- Tabu search
- GDAP algorithm
- 6.3 Optimization based on topology design
- 6.3.1 Objective Function
- Eigenvalue-based objective function
- Physical dynamics cost-based objective function
- Sum cost of control- and communication-based objective function.
- Synchronization-based objective function
- 6.3.2 Optimization of Topology
- Greedy algorithm
- Relaxed optimization
- Decomposition-based optimization
- Structure-based optimization
- 6.4 Team decision theory
- 6.4.1 Team Decision Theory
- System model
- Values of multiple information structures
- Information structures in networks
- 6.4.2 Team Decision Theory in Optimal Control
- System model
- State teams
- Dynamic teams
- 6.5 Conclusions
- Chapter 7: Communication network operation for CPSs
- 7.1 Introduction
- 7.1.1 Main Challenges
- 7.1.2 Main Approaches
- 7.2 Hybrid system modeling for CPSs
- 7.2.1 Hybrid Systems
- Linear switching system
- Control of linear switching systems
- 7.2.2 Hybrid System Model of a CPS
- Communication mode
- Relationship between communication and dynamics modes
- 7.3 Optimization of scheduling policy
- 7.3.1 Fundamental Challenges
- 7.3.2 Mode Provisioning
- Generic procedure
- Illustration by examples
- 7.3.3 Mode Scheduling
- Centralized mode scheduling
- Distributed scheduling
- 7.4 Scheduling: other approaches
- 7.4.1 Optimization-Based Scheduling
- System model
- Communication constraints
- 7.4.2 Effective Information-Based Scheduling
- Delay-tolerant Kalman filtering
- Definition of virtual queues
- Information bits
- Distributed scheduling
- 7.5 Routing
- 7.5.1 Estimation Oriented Routing
- System model
- Encoder and decoder
- Evolution of covariance
- 7.5.2 System Dynamics-Aware Multicast Routing
- System model
- Single mode routing
- Multiple routing modes
- 7.6 Conclusions
- Chapter 8: Physical layer design
- 8.1 Introduction
- 8.1.1 Modulation
- 8.1.2 Coding
- Source coding
- Channel coding
- 8.2 Adaptive modulation
- 8.2.1 System Model
- Physical dynamics
- Communication channel.
- 8.2.2 Impact of Modulation on System Dynamics
- Impact of communication delay
- Impact of delay and packet loss
- Markovian jump process
- 8.2.3 Hybrid System Modeling
- SPAAM
- SSAAM
- 8.3 Source coding in a CPS: point-to-point case
- 8.3.1 System Model
- 8.3.2 Structure of Causal Coders
- Sliding block causal coders
- Block causal coders
- Block stationary coders
- Finite-state causal coders
- Rate distortion
- 8.3.3 Finite-State Transceiver
- Transceiver structure
- Major problem and conclusions
- 8.3.4 Channel Feedback
- 8.3.5 Sequential Quantization
- System model
- Vector quantizer
- Equivalent control problem
- Dynamic programming
- 8.4 Source coding in a CPS: distributed case
- 8.4.1 Distributed Coding in Traditional Communications
- 8.4.2 System Model
- 8.4.3 Distortion and Quantization
- Distortion
- Lattice-based quantization
- 8.4.4 Coding Procedure
- Slepian-Wolf coding
- Coloring-based coding
- 8.4.5 Adaptation to Physical Dynamics
- Prediction
- Range adaptation
- 8.5 Physical dynamics-aware channel decoding
- 8.5.1 Motivation
- 8.5.2 System Model
- 8.5.3 Joint Decoding
- A brief introduction to Pearl's BP
- Iterative decoding
- 8.6 Control-oriented channel coding
- 8.6.1 Trajectory Codes
- System model
- Concept of trajectory codes
- Construction of trajectory codes
- 8.6.2 Anytime Channel Codes
- System model
- Anytime reliability
- Linear tree codes
- Maximum likelihood decoding
- Code construction
- 8.6.3 Convolutional LDPC for Control
- 8.7 Channel coding for interactive communication in computing
- 8.7.1 System Model
- 8.7.2 Tree Codes
- Construction of potent tree codes
- Explicit construction of tree codes
- 8.7.3 Protocol Simulation
- 8.7.4 Performance Analysis
- 8.8 Conclusions
- Bibliography
- Index
- Back Cover.
