This book provides recent advances in analysis and synthesis of Large-scale network systems (LSNSs) with sampled-data communication and non-identical nodes. In its first chapter of the book presents an introduction to Synchronization of LSNSs and Algebraic Graph Theory as well as an overview of recent developments of LSNSs with sampled data control or output regulation control. The main text of the book is organized into two main parts - Part I: LSNSs with sampled-data communication and Part II: LSNSs with non-identical nodes. This monograph provides up-to-date advances and some recent developments in the analysis and synthesis issues for LSNSs with sampled-data communication and non-identical nodes. It describes the constructions of the adaptive reference generators in the first stage and the robust regulators in the second stage. Examples are presented to show the effectiveness of the proposed design techniques.

Estimation and Control of Large Scale Networked Systems is the first book that systematically summarizes results on large-scale networked systems. In addition, the book also summarizes the most recent results on structure identification of a networked system, attack identification and prevention. Readers will find the necessary mathematical knowledge for studying large-scale networked systems, as well as a systematic description of the current status of this field, the features of these systems, difficulties in dealing with state estimation and controller design, and major achievements. Numerical examples in chapters provide strong application backgrounds and/or are abstracted from actual engineering problems, such as gene regulation networks and electricity power systems. This book is an ideal resource for researchers in the field of systems and control engineering. - Provides necessary mathematical knowledge for studying large scale networked systems - Introduces new features for filter and control design of networked control systems - Summarizes the most recent results on structural identification of a networked system, attack identification and prevention

This book presents a time-delay approach to the analysis and synthesis of networked control systems (NCSs) under communication constraints. Differently from other approaches, the time-delay approach to NCSs allows communication delays to be larger than the sampling intervals in the presence of scheduling protocols. The book starts from a comprehensive introduction to three main approaches to sampled-data and networked control. It then focuses on time-delay approach, and the modelling of the closed-loop systems in the form of time-delay system. It presents discontinuous (in time) Lyapunov functional constructions that are efficient for NCSs in the presence of communications delays. Further, it highlights time-delay approaches developed to model and analyze NCSs under communication constraints, with a particular focus on dynamic quantization, round-robin, try-once-discard and stochastic protocols. The results are first presented for the continuous-time NCSs and then extended to discrete-time NCSs. Discussing recent developments in Lyapunov-based analysis of NCSs under communication constraints, the book is a valuable resource for researchers interested in sampled-data and networked control, and time-delay systems, as well as for graduate students in automatic control and systems theory.

This monograph studies the synchronization control of Markovian complex neural networks with time-varying delays, and the structure of the book is summarized as follows. Chapter 1 introduces the system description and some background knowledges, and also addresses the motivations of this monograph. In Chapter 2, the stochastic synchronization issue of Markovian coupled neural networks with partially unknown transition rates and random coupling strengths is investigated. In Chapter 3, the local synchronization issue of Markovian neutral complex networks with partially information of transition rates is investigated. The new delay-dependent synchronization criteria in terms of LMIs are derived, which depends on the upper and lower bounds of the delays. In Chapter 4, the local synchronization issue of Markovian nonlinear coupled neural networks with uncertain and partially unknown transition rates is investigated. The less conservative local synchronization criteria containing the bounds of delay and delay derivative are obtained based on the novel augmented Lyapunov-Krasovskii functional and a new integral inequality. In Chapter 5, the sampled-data synchronization issue of delayed complex networks with aperiodic sampling interval is investigated based on enhanced input delay approach, which makes full use of the upper bound of the variable sampling interval and the sawtooth structure information of varying input delay. In Chapter 6, the sampled-data synchronization issue of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals is investigated based on an enhanced input delay approach. Furthermore, the mode-dependent sampled-data controllers are proposed based on the delay dependent synchronization criteria. In Chapter 7, the synchronization issue of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. In Chapter 8, we conclude the monograph by briefly summarizing the main theoretical findings.

This monograph represents the outcome of research effort of the authors on scalable synchronization of large-scale multi-agent systems (MAS). Cooperative control of multi-agent systems has been growing in popularity and is highly interdisciplinary in recent years. The application of synchronization of MAS includes automobile systems, aerospace systems, multiple-satellite GPS, high-resolution satellite imagery, aircraft formations, highway traffic platooning, industrial process control with multiple processes, and more. Most of the proposed protocols in the literature for synchronization of MAS require some knowledge of the communication network such as bounds on the spectrum of the associated Laplacian matrix and the number of agents. These protocols suffer from scale fragility wherein stability properties are lost for large-scale networks or when the communication graph changes. In the past few years, the authors of this monograph have worked on developing scale-free protocol design for various cases of MAS problems. The key contribution of the monograph is to offer a scale-free design framework and provide scale-free protocols to achieve synchronization, delayed synchronization, and almost synchronization in the presence of input and communication delays, input saturation and external disturbances. The scale-free design framework solely is based on the knowledge of agent models and does not depend on information about the communication network such as the spectrum of the associated Laplacian matrix or size of the network. Drawing upon their extensive work in this area, the authors provide a thorough treatment of agents with higher-order dynamics, different classes of models for agents, and the underlying networks representing actions of the agents. The high technical level of their presentation and their rigorous mathematical approach make this monograph a timely and valuable resource that will fill a gap in the existing literature.

Far from being separate entities, many social and engineering systems can be considered as complex network systems (CNSs) associated with closely linked interactions with neighbouring entities such as the Internet and power grids. Roughly speaking, a CNS refers to a networking system consisting of lots of interactional individuals, exhibiting fascinating collective behaviour that cannot always be anticipated from the inherent properties of the individuals themselves. As one of the most fundamental examples of cooperative behaviour, consensus within CNSs (or the synchronization of complex networks) has gained considerable attention from various fields of research, including systems science, control theory and electrical engineering. This book mainly studies consensus of CNSs with dynamics topologies - unlike most existing books that have focused on consensus control and analysis for CNSs under a fixed topology. As most practical networks have limited communication ability, switching graphs can be used to characterize real-world communication topologies, leading to a wider range of practical applications. This book provides some novel multiple Lyapunov functions (MLFs), good candidates for analysing the consensus of CNSs with directed switching topologies, while each chapter provides detailed theoretical analyses according to the stability theory of switched systems. Moreover, numerical simulations are provided to validate the theoretical results. Both professional researchers and laypeople will benefit from this book.

This monograph explores the synchronization of large-scale, multi-agent dynamical systems in the presence of disturbances, delays, and time-varying networks. Drawing upon their extensive work in this area, the authors provide a thorough treatment of agents with higher-order dynamics, different classes of models for agents, and the underlying networks representing the agents’ actions. The high technical level of their presentation and their rigorous mathematical approach make this a timely and valuable resource that will fill a gap in the existing literature. Divided into two sections, the first part of the book focuses on state synchronization of homogeneous multi-agent systems. The authors consider state synchronization by determining control strategies for both continuous- and discrete-time systems that achieve state synchronization under both full- and partial-state coupling. The chapters that follow examine multi-agent systems with both linear and nonlinear time-varying agents, input-delays for continuous- and discrete-time systems, and communication delays for continuous-time systems. The second part of the book is dedicated to regulated output synchronization of heterogeneous multi-agent systems with linear and nonlinear agents. Both sections of the book include performance considerations in H2- and H-infinity norms in the presence of external disturbances. Research on synchronization of multi-agent systems has been growing in popularity and is highly interdisciplinary, with applications to automobile systems, aerospace systems, multiple-satellite GPS and high-resolution satellite imagery, aircraft formations, highway traffic platooning, industrial process control with multiple processes, and more. Synchronization of Multi-Agent Systems in the Presence of Disturbances and Delays will therefore be of interest to upper-level graduate students, researchers, and engineers in industry working on interconnected dynamical systems.