
Control over Communication Networks
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
Advanced and systematic examination of the design and analysis of networked control systems and multi-agent systems
Control Over Communication Networks provides a systematic and nearly self-contained description of the analysis and design of networked control systems (NCSs) and multi-agent systems (MASs) over imperfect communication networks, with a primary focus on fading channels and delayed channels. The text characterizes the effect of communication channels on the stability and performance of NCSs, and further studies the joint impact of communication channels and network topology on the consensus of MASs.
By integrating communication and control theory, the four highly-qualified authors present fundamental results concerning the stabilization of NCSs over power-constrained fading channels and Gaussian finite-state Markov channels, linear-quadratic optimal control of NCSs with random input gains, optimal state estimation with intermittent observations, consensus of MASs with communication delay and packet dropouts, and synchronization of delayed Vicsek models.
Simulation results are given in each chapter to demonstrate the developed analysis and synthesis approaches. The references are comprehensive and up-to-date, enabling further study for readers.
Topics covered in Control Over Communication Networks include:
* Basic foundational knowledge, including control theory, communication theory, and graph theory, to enable readers to understand more complex topics
* The stabilization, optimal control, and remote state estimation problems of linear systems over channels with fading, signal-to-noise constraints, or intermittent measurements
* Consensus problems of MASs over fading/delayed channels, with directed and undirected communication graphs
Control Over Communication Networks provides a valuable unified platform for understanding the analysis and design of NCSs and MASs for researchers, control engineers working on control systems over communication networks, and mechanical engineers working on unmanned systems. Preliminary knowledge of linear system theory and matrix analysis is required.
More details
Other editions
Additional editions


Persons
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.
Content
About the Authors xiii
Preface xv
Acknowledgments xvii
Acronyms xix
List of Symbols xxi
1 Introduction 1
1.1 Introduction and Motivation 1
1.1.1 Networked Control Systems 1
1.1.2 Multi-Agent Systems 2
1.2 Literature Review 4
1.2.1 Basics of Communication Theory 4
1.2.2 Stabilization of NCSs 6
1.2.2.1 Control over Noiseless Digital Channels 6
1.2.2.2 Control over Stochastic Digital Channels 7
1.2.2.3 Control over Analog Channels 8
1.2.3 LQ Optimal Control of NCSs over Fading Channels 9
1.2.4 Estimation of NCSs with Intermittent Communication 11
1.2.4.1 Stability of Kalman Filtering with Intermittent Observations 11
1.2.4.2 Remote State Estimation with Sensor Scheduling 12
1.2.5 Distributed Consensus of MASs 13
1.3 Preview of the Book 15
1.4 Preliminaries 18
1.4.1 Graph Theory 18
1.4.2 Hadamard Product and Kronecker Product 19
Bibliography 20
2 Stabilization over Power Constrained Fading Channels 29
2.1 Introduction 29
2.2 Problem Formulation 29
2.3 Fundamental Limitations 31
2.4 Mean-Square Stabilizability 35
2.4.1 Scalar Systems 36
2.4.2 Two-Dimensional Systems 37
2.4.2.1 Communication Structure 38
2.4.2.2 Encoder/Decoder Design 38
2.4.2.3 Scheduler Design 39
2.4.2.4 Scheduler Parameter Selection 40
2.4.2.5 Proof of Theorem 2.3 41
2.4.3 High-Dimensional Systems: TDMA Scheduler 44
2.4.4 High-Dimensional Systems: Adaptive TDMA Scheduler 45
2.4.4.1 Scheduling Algorithm 46
2.4.4.2 Scheduler Parameter Selection 46
2.4.4.3 Proof of Theorem 2.5 46
2.5 Numerical Illustrations 51
2.5.1 Scalar Systems 51
2.5.2 Vector Systems 52
2.6 Conclusions 53
Bibliography 53
3 Stabilization over Gaussian Finite-State Markov Channels 57
3.1 Introduction 57
3.2 Problem Formulation 58
3.2.1 Stability of Markov Jump Linear Systems 59
3.2.2 Sojourn Times for Markov Lossy Process 60
3.3 Fundamental Limitation 61
3.4 Stabilization over Finite-State Markov Channels 64
3.4.1 Communication Structure 65
3.4.2 Observer/Estimator/Controller Design 65
3.4.3 Encoder/Decoder/Scheduler Design 67
3.4.4 Sufficient Stabilizability Conditions 68
3.5 Stabilization over Markov Lossy Channels 71
3.5.1 Two-Dimensional Systems 71
3.5.1.1 Optimal Scheduler Design 72
3.5.1.2 Scheduler Parameter Selection 74
3.5.1.3 Sufficiency Proof of Theorem 3.4 75
3.5.2 High-Dimensional Systems 77
3.5.3 Numerical Illustrations 81
3.6 Conclusions 82
Bibliography 83
4 Linear-Quadratic Optimal Control of NCSs with Random Input Gains 85
4.1 Introduction 85
4.2 Problem Formulation 86
4.3 Finite-Horizon LQ Optimal Control 88
4.4 Solvability of Modified Algebraic Riccati Equation 91
4.4.1 Cone-Invariant Operators 91
4.4.2 Solvability 97
4.5 LQ Optimal Control 108
4.6 Conclusion 114
Bibliography 115
5 Multisensor Kalman Filtering with Intermittent Measurements 117
5.1 Introduction 117
5.2 Problem Formulation 118
5.3 Stability Analysis 120
5.3.1 Transmission Capacity 120
5.3.2 Preliminaries 120
5.3.3 Lower Bound 121
5.3.4 Upper Bound 124
5.3.5 Special Cases 130
5.4 Examples 131
5.5 Conclusions 132
Bibliography 133
6 Remote State Estimation with Stochastic Event-Triggered Sensor Schedule and Packet Drops 135
6.1 Introduction 135
6.2 Problem Formulation 135
6.3 Optimal Estimator 137
6.4 Suboptimal Estimators 143
6.4.1 Fixed Memory Estimator 143
6.4.2 Particle Filter 145
6.5 Simulations 149
6.6 Conclusions 151
Bibliography 152
7 Distributed Consensus over Undirected Fading Networks 153
7.1 Introduction 153
7.2 Problem Formulation 154
7.3 Identical Fading Networks 155
7.4 Nonidentical Fading Networks 163
7.4.1 Definition of Edge Laplacian 163
7.4.2 Sufficient Consensus Conditions 164
7.5 Simulations 168
7.6 Conclusions 170
Bibliography 170
8 Distributed Consensus over Directed Fading Networks 173
8.1 Introduction 173
8.2 Problem Formulation 174
8.3 Identical Fading Networks 174
8.3.1 Consensus Error Dynamics 175
8.3.2 Consensusability Results 177
8.3.3 Balanced Directed Graph Cases 179
8.4 Definitions and Properties of CIIM, CIM, and CEL 181
8.4.1 Definitions of CIIM, CIM, and CEL 181
8.4.2 Properties of CIIM, CIM, and CEL 182
8.5 Nonidentical Fading Networks 185
8.5.1 ¿=µI 189
8.5.1.1 Star Graphs 190
8.5.1.2 Directed Path Graphs 191
8.5.2 ¿ ¿ µI 192
8.6 Simulations 192
8.7 Conclusions 194
Bibliography 195
9 Distributed Consensus over Networks with Communication Delay and Packet Dropouts 197
9.1 Introduction 197
9.2 Problem Formulation 198
9.3 Consensusability with Delay and Identical Packet Dropouts 199
9.3.1 Stability Criterion of NCSs with Delay and Multiplicative Noise 199
9.3.2 Consensusability Conditions 204
9.4 Consensusability with Delay and Nonidentical Packet Dropouts 209
9.5 Illustrative Examples 214
9.6 Conclusions 216
Bibliography 216
10 Distributed Consensus over Markovian Packet Loss Channels 219
10.1 Introduction 219
10.2 Problem Formulation 219
10.3 Identical Markovian Packet Loss 220
10.3.1 Analytic Consensus Conditions 224
10.3.2 Critical Consensus Condition for Scalar Agent Dynamics 226
10.4 Nonidentical Markovian Packet Loss 228
10.5 Numerical Simulations 232
10.6 Conclusions 234
Bibliography 235
11 Synchronization of the Delayed Vicsek Model 237
11.1 Introduction 237
11.2 Directed Graphs 238
11.3 Problem Formulation 239
11.4 Synchronization of Delayed Linear Vicsek Model 240
11.5 Synchronization of Delayed Nonlinear Vicsek Model 246
11.6 Simulations 249
11.7 Conclusions 253
Bibliography 253
Index 255
1
Introduction
1.1 Introduction and Motivation
1.1.1 Networked Control Systems
Due to the flexible architecture and ease of installation and maintenance, communication networks are widely used in control systems, which result in networked control systems (NCSs), where the plants, actuators, sensors, and controllers are spatially distributed and interconnected by communication channels [Schenato et al., 2007, Hespanha et al.]. NCSs are ubiquitous in industry and daily life, such as teleoperation [Arcara and Melchiorri, 2002], power systems [Wang et al., 2012], and transportation systems [Seiler and Sengupta, 2001].
Even though NCSs have the advantages of low cost, easy implementation, and expansion to large-scale applications, they also introduce new challenging problems arising from the limited resources and unreliability of the communication networks used for information transmission (see Figure 1.1). For example, the time delay may occur in digital communication channels due to data processing and transmission [Tse and Viswanath, 2005, Goldsmith, 2005]. Notably, in wireless communication networks, communication channels naturally suffer from interference, fading, and transmission noises [Tse and Viswanath, 2005, Goldsmith, 2005]. There into, fading is the time variation of channel strengths and is usually caused by two factors: one is the shadowing from obstacles; the other one is the multipath propagation [Tse and Viswanath, 2005, Goldsmith, 2005]. Packet drops can also be modeled as a special case of channel fading. Take Figure 1.2 as an illustration. The wireless signal may transmit through the car and undergo several paths before arriving at the receiver. If the phases of the received signals from different paths are the same, the signal strength is enhanced. Otherwise, the signal strength is reduced as a result of the cancellation of radio waves. Besides, the signal strength at the receiver side might be reduced due to the shadowing from the car. Since control is often used in safety- or mission-critical applications, we must take the uncertainties in communication networks into consideration and investigate how they affect the stability and performance of control systems.
The classical control theory mainly deals with the systems with nearly perfect point-to-point connections and focuses on the design of control laws to achieve the given control performance. It can't be applied directly to the NCSs when the uncertainties in the communication network must be considered. A new control paradigm is required to deal with the interplay between control and communication. In this book, one of the main objectives is to study the stabilization, estimation, and optimal control of NCSs over channels with fading, packet drops, or delay.
1.1.2 Multi-Agent Systems
Motivated by the collective behavior in nature, such as schooling fish, flocking birds, and marching locusts, multi-agent systems (MASs) have attracted considerable research interest from the control community [Jadbabaie et al., 2003, Olfati-Saber and Murray, 2004, Olfati-Saber et al., 2007, Bliman and Ferrari-Trecate, 2008, Cao et al., 2008, Ren and Beard, 2008, You and Xie, 2010, Cao et al., 2012, Trentelman et al., 2013, Qi et al., 2016, Qiu et al., 2017, Xu et al., 2018, Zheng et al., 2018]. With the rapid development of wireless communication networks, MASs have been applied in many industrial and military applications. Such systems usually involve large numbers of autonomous agents (e.g. robots, unmanned aerial vehicles, satellites), which share information via local interactions and work together to achieve collective objectives.
For MASs, each agent can have the same or different system dynamics, resulting in different types of MASs, e.g. first- and second-order MASs, linear and nonlinear MASs, homogeneous and heterogeneous MASs. The interactions among the agents form the interaction topology, which can be fixed or time-varying. Then the cooperative control of MASs is based on the system dynamics and the interaction topology to design the control laws, which can be centralized or distributed, to fulfill a task. Typical cooperative control tasks include consensus, formation, swarming/flocking, rendezvous, etc. There into, the consensus problem, which requires all agents to agree on a certain quantity of common interests, builds the foundation of other cooperative tasks.
Existing research on consensus assumes that the communication networks among agents are perfect. However, as mentioned earlier, in practical applications, communication channels naturally suffer from fading, signal-to-noise ratio (SNR) constraints, time delay, etc. Hence, it is of great significance to study how the uncertainties in communication networks influence the consensus of MASs. The other main objective of this book is to analyze the consensus problem of MASs over channels with fading, packet drops, and delay.
Figure 1.1 Networked control systems.
Figure 1.2 Fading phenomenon in wireless communications.
1.2 Literature Review
Control over communication channels/networks has been a hot research topic in the past decades [Matveev and Savkin, 2009, Como et al., 2014, You et al., 2015], motivated by the rapid developments of wireless communication technologies that enable the wide connection of geographically distributed devices and systems. However, the inclusion of wireless communication channels/networks also introduces challenges in the analysis and design of control systems due to constraints and uncertainties in wireless communications. We must take the communication channels/networks into consideration and study their impact on the stability and performance of control systems. This section briefly reviews existing results on the analysis and design of NCSs and MASs over imperfect communication channels.
1.2.1 Basics of Communication Theory
One of the main focuses of this book is to characterize the critical channel requirement such that the NCS can be mean-square stabilized. Since the communication channel is used to transmit information about the system state, as illustrated in Figure 1.1, it is expected that if the channel capacity is large enough, the feedback connected system can be mean-square stable. From this perspective, the communication channel capacity might be critical for the mean-square stabilization of control systems.
The channel capacity problem is fundamental in communication theory since it dictates the maximum data rates that can be transmitted over channels with asymptotically small error probability [Tse and Viswanath, 2005, Goldsmith, 2005]. In this subsection, we briefly review the communication channel capacity definitions and discuss why the communication theoretic channel capacity is not the critical characterization of the capacity required for controls. We only discuss discrete memoryless channels, and most of the definitions are borrowed from Cover and Thomas [2006].
A discrete memoryless channel consists of three parts: an input alphabet , an output alphabet , and a probability transition matrix that describes the probability of observing the output symbol given the input symbol . The channel is memoryless if the probability distribution of the current channel output conditioned on the current channel input is independent of previous channel inputs or outputs. The configuration of the point-to-point communication system is depicted in Figure 1.3. We want to transmit a message reliably through the communication channel with appropriately designed channel encoders and decoders. The code in a communication system is defined as follows.
Figure 1.3 Point-to-point communication system.
The performance of the code is measured by the decoding error.
The communication channel capacity which measures the maximal capacity for reliably transmitting the information is defined below.
The channel capacity in Definition 1.3 is called the Shannon channel capacity since C. E. Shannon proved in the channel coding theorem that this channel capacity equals the mutual information of the channel maximized over all possible input distributions [Shannon, 2001, Cover and Thomas, 2006]:
where the mutual information is defined as
The Shannon capacity of fading channels has been studied under various scenarios in Goldsmith and Varaiya [1997], Biglieri et al. [1998], Sadeghi et al. [2008], Abou-Faycal et al. [2001], and Caire et al. [1999]. For example it is proved in Goldsmith and Varaiya [1997] that if the channel state information is available at the receiver side, the Shannon channel capacity of a fading channel is
where is the probability distribution function of the channel fading...
System requirements
File format: ePUB
Copy protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePub works well for novels and non-fiction books – i.e., „flowing” text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our ebook Help page.