| The Importance, Design and Implementation of a Middleware for Networked Control Systems | p. 1 |
| Introduction | p. 1 |
| Networked Control Systems | p. 2 |
| Domain Characteristics | p. 2 |
| Domain Requirements | p. 4 |
| Middleware for Networked Control Systems | p. 5 |
| Middleware Fundamentals | p. 5 |
| Etherware | p. 7 |
| Real-Time Operation of Networked Control Systems | p. 10 |
| Real-Time System Fundamentals | p. 10 |
| Real-Time Support in Etherware | p. 14 |
| Reliability for Networked Control Systems | p. 17 |
| Fundamentals of Reliable System | p. 17 |
| Reliability Support in Etherware | p. 20 |
| Case Study: Networked Inverted Pendulum Control System | p. 22 |
| Inverted Pendulum Control System | p. 22 |
| Periodic Control under Stress | p. 23 |
| Runtime System Management | p. 25 |
| Conclusion | p. 27 |
| References | p. 28 |
| Wireless Networking for Control: Technologies and Models | p. 31 |
| Introduction | p. 31 |
| Understanding the Single Link | p. 32 |
| Wireless Propagation and Outage | p. 32 |
| Markov Models for the Wireless Channel | p. 39 |
| The ISM Band, Co-existence and Interference | p. 40 |
| Means for Increasing Reliability | p. 42 |
| Multiple Links: Medium Access Control | p. 45 |
| Scheduled Medium Access: TDMA and FDMA | p. 47 |
| Contention-Based Medium Access: Aloha, CSMA and Beyond | p. 48 |
| Dynamic Access Scheduling via Polling and Reservation | p. 52 |
| Energy-Efficient Medium Access Control | p. 53 |
| From Single Links to Network: The Upper Networking Layers | p. 54 |
| Topologies and Multi-hop Communications | p. 54 |
| Routing | p. 58 |
| Transport Layer Protocols and Traffic Patterns | p. 61 |
| Standards and Specifications for Industrial Wireless Networking | p. 62 |
| Control Relevant Models of Latency and Loss | p. 66 |
| Conclusions | p. 69 |
| References | p. 70 |
| A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms | p. 75 |
| Introduction | p. 75 |
| Linear Consensus Algorithms: Definitions and Main Results | p. 77 |
| Analysis | p. 78 |
| Design | p. 82 |
| Estimation and Control Problems as Average Consensus | p. 89 |
| Parameter Estimation with Heterogeneous Sensors | p. 89 |
| Node Counting in a Network | p. 90 |
| Generalized Averages | p. 90 |
| Vehicle Rendezvous | p. 91 |
| Least Squares Data Regression | p. 91 |
| Sensor Calibration | p. 92 |
| Kalman Filtering | p. 93 |
| Control-Based Performance Metrics for Consensus Algorithms | p. 95 |
| Performance Indices | p. 95 |
| Evaluation and Optimization of Performance Indices | p. 100 |
| Conclusion | p. 104 |
| References | p. 104 |
| Distributed Optimization and Games: A Tutorial Overview | p. 109 |
| Introduction | p. 109 |
| Convex Optimization Using First-Order Methods | p. 110 |
| Gradient Methods for Smooth Problems | p. 111 |
| Subgradient Methods for Non-smooth Problems | p. 114 |
| Incremental Subgradient Methods | p. 115 |
| Decomposition Techniques | p. 117 |
| Dual Decomposition | p. 118 |
| Augmented Lagrangian and Proximal Point Methods | p. 122 |
| Primal Decomposition | p. 124 |
| Networked Optimization | p. 126 |
| Networked Optimization via Dual Decomposition | p. 127 |
| Consensus-Subgradient Schemes | p. 129 |
| Networked Incremental Subgradient Methods | p. 132 |
| Game Theory in Distributed Optimization | p. 133 |
| Basics of Game Theory | p. 133 |
| Properties of Nash Equilibria | p. 134 |
| Dynamics of Gradient Algorithms | p. 139 |
| Connection between Lyapunov Functions and Objective Functions | p. 140 |
| Krasovskii's Method | p. 142 |
| Non-strictly Convex Problem | p. 143 |
| Conclusions | p. 144 |
| References | p. 145 |
| Decentralized Model Predictive Control | p. 149 |
| Introduction | p. 149 |
| Model Predictive Control | p. 152 |
| Existing Approaches to DMPC | p. 153 |
| DMPC Approach of Alessio, Barcelli, and Bemporad | p. 154 |
| DMPC Approach of Jia and Krogh | p. 161 |
| DMPC Approach of Venkat, Rawlings, and Wright | p. 162 |
| DMPC Approach of Dunbar and Murray | p. 163 |
| DMPC Approach of Keviczy, Borrelli, and Balas | p. 164 |
| DMPC Approach of Mercangöz and Doyle | p. 165 |
| DMPC Approach of Magni and Scattolini | p. 166 |
| Example of Decentralized Temperature Control in a Railcar | p. 166 |
| Example Description | p. 166 |
| Simulation Results | p. 168 |
| Hierarchical MPC | p. 172 |
| Problem Description | p. 172 |
| Illustrative Example | p. 173 |
| Conclusions | p. 175 |
| References | p. 176 |
| Decentralized Control | p. 179 |
| Motivating Examples | p. 179 |
| Vehicle Spacing | p. 180 |
| Witsenhausen's Counterexample | p. 181 |
| Static Problems | p. 182 |
| Solution of the Multi-vehicle Problem | p. 184 |
| Nonlinear Policies | p. 185 |
| Dynamic Problems | p. 188 |
| Quadratic Invariance | p. 190 |
| Skyline Information Structures | p. 191 |
| Control of Networks | p. 193 |
| Non-convex Systems | p. 195 |
| Unstable Plants | p. 196 |
| Solving the Optimization Problem | p. 196 |
| Spectral Factorization | p. 197 |
| Solution of the Two-Player Problem | p. 198 |
| Summary | p. 199 |
| References | p. 200 |
| Stability and Stabilization of Networked Control Systems | p. 203 |
| Introduction | p. 203 |
| Overview of Existing Approaches | p. 205 |
| The Types of Network-Induced Phenomena | p. 205 |
| Different Approaches in Modeling/Analysis of NCS | p. 206 |
| NCS with Delays, Varying Sampling Intervals and Packet Loss | p. 209 |
| Description of the NCS | p. 209 |
| Discrete-Time Modeling Approaches | p. 211 |
| Sampled-Data Modeling Approaches | p. 223 |
| NCS Including Communication Constraints | p. 228 |
| Continuous-Time (Emulation) Approaches | p. 228 |
| Discrete-Time Approach | p. 238 |
| Comparison of Discrete-Time and Continuous-Time Approaches | p. 245 |
| Conclusions | p. 246 |
| References | p. 248 |
| Feedback Control over Limited Capacity Channels | p. 255 |
| Introduction | p. 255 |
| Control under Capacity Constraints: System Setup and Background | p. 258 |
| The Minimum Data Rate for Stabilization | p. 261 |
| Problem Formulation and Initial Results | p. 261 |
| Dynamic Quantizers | p. 263 |
| The Solution to the Minimum Data Rate Problem | p. 265 |
| The Coarsest Quantization for Stabilization | p. 267 |
| The Coarsest Quantizers | p. 268 |
| The Coarsest Quantizer for Stabilization over Lossy Channels | p. 272 |
| Quantized Adaptive Control for Uncertain Systems | p. 276 |
| Information Theoretic Approach to Bode's Integral Formula | p. 282 |
| Bode's Integral Formula for Complementary Sensitivity Functions | p. 283 |
| Entropy and Mutual Information | p. 284 |
| Characterization of Complementary Sensitivity Properties | p. 285 |
| Conclusion | p. 288 |
| References | p. 289 |
| Event-Triggered Feedback in Control, Estimation, and Optimization | p. 293 |
| Introduction | p. 293 |
| Mathematical Preliminaries | p. 297 |
| Event-Triggered Feedback in Embedded Control Systems | p. 302 |
| Event-Triggered Feedback in Networked Control Systems | p. 320 |
| Event-Triggered Estimation | p. 330 |
| Event-Triggered Approaches to Optimization | p. 340 |
| Research Issues | p. 350 |
| References | p. 353 |
| Index | p. 359 |
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