Analysis Of The Dynamics In Delayed Networked System And Sampled-Data Control

The analysis and synthesize of dynamics as well as distributed consensus control of delayed networked system,including neural networks,multi-agent systems,complex cyber-physical sys-tems,are becoming hot research topics in recent years.Many researchers from different fields have paid much attention to it's wide applications in communication security,neuroscience,spacecraft formation flying,large-scale sensor networks,urban traffic networks,etc.Investi-gations of dynamics and consensus phenomena of delayed networks will contribute to a deep comprehension of cooperative mechanisms in networked circumstances,and will also benefit the design of more effective protocols in order to realize various control objectives.Mainly based on Lyapunov stability analysis,matrix measure approach,sampled-data control,cyber-security control,this dissertation is devoted to analyze the dynamics,synchronization and distribut?ed control of several delayed networked systems.Specifically,this dissertation is divided into seven chapters and organized as follows:In the first chapter,the general backgrounds and the significance of delayed networked system are presented.Specifically,the developments and hot issues of the delayed neural net-works are elaborated.Furthermore,the significance and developments of cooperative control of multi-agent system and sampled-data control are stated.The main contents and contributions of this dissertation are explained based on the above-mentioned discussions.In the second chapter,the dynamics and synchronization control of two kinds of delayed neural networks are explored.In Section 2.1,the stability and synchronization control of inertial neural networks with time-varying delays are concerned.By using matrix measure and Halanay inequality,several sufficient conditions for global exponential stability of the equilibrium are provided in form of matrix measure.These criteria are simple in form and easy to verify.To realize the synchronization of master-slave neural networks,an error-feedback control strategy is employed and the requirements for the feedback gains are also derived.In Section 2,2,the fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated.The settling time of fixed-time stability can be adjusted to desired values regardless of initial conditions.Novel synchronization control strategy is proposed.By utilizing the concept of Filippov solutions and nonsmooth analysis,some sufficient conditions are provided for selecting the control parameters to ensure the fixed-time synchronization.In the third chapter,sampled-data control of several dynamical networks are discussed.Section 3.1 is concerned with the mean-square node-to-node consensus tracking problem for multi-agent systems with stochastic sampling and directed topologies.By employing the delayed-input method and constructing discontinuous Lyapunov functionals,sufficient con-ditions are obtained to guarantee that the state of each follower can track that of the cor-responding leader asymptotically in the mean-square sense.In Section 3.2,the distributed observer-based stabilization problem of multi-agent systems under directed graphs is investi-gated and distributed control protocol with sampled-data information is proposed.In order to stabilize the states of the whole network,all the nodes can utilize the relative output es-timation error at sampling instants and only a small fraction of nodes can use the absolute output estimation error additionally.By virtue of the properties of input-to-state stability,an algorithm to design the control gain matrix,observer gain matrix,coupling strength as well as the length of allowable sampling intervals are derived.Further discussions about the solvabil-ity of obtained linear matrix inequalities are also given.In Section 3.3,the synchronization problem of master-slave chaotic neural networks with distributed sensors,quantization pro-cess,and communication delays is investigated.At each sampling instant,only one sensor is scheduled to transmit its latest information to the controller's side.Thus,such communi-cation and control strategies are much more energy-saving.Sufficient conditions for output feedback control gain matrix,the length of allowable sampling intervals,and the upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural netw,orks.The distributed robust stabilization control problem of multi-agent systems with linear dynamics is investigated in the fourth chapter.The considered topology is directed and the dynamics of each agent are subjected to unknown uncertainties.Relative state feedback control inputs are implemented for every node while additionally,the control input of the root agent can utilize its own absolute state intermittently.In order to stabilize the whole network,an algorithm to choose control parameters is provided and the intermittent communication ratio is also derived by using directed graph theory and Lyapunov stability analysis.In the fifth chapter,under the framework of cyber-physical systems,distributed tracking problem of complex dynamical networks with Lipschitz-type nonlinear dynamics is investi-gated.Due to limitations in practical circumstances,the states of the agents are usually unavailable for controllers,so distributed observers are firstly designed to reconstruct the s-tates of nodes.The communication channels for controllers and observers are both subjected to cyber attacks,which will destroy the connectivity of the communication topologies.It is assumed that the impacts of attacks on different communication networks are independent.New security control strategies are proposed and analyzed.An algorithm to properly select the feedback gain matrices and coupling strengths is also presented.In the sixth chapter,an adaptive perimeter control problem is studied for urban traffic networks with multiple regions and time-varying delays.Firstly,the system model is formulated as nonlinear ordinary differential equations based on the concept of macroscopic fundamental diagram.This model includes both the time of vehicles traveling to the borders of regions as well as evacuation time of traffic jams,and they are modeled as state and input delays,respectively.The control objective is to stabilize the number of vehicles in each region to desired values.By employing the model reference adaptive control combining with asymptotical sliding mode technique,adaptive laws for control parameters are given by using only the information of the reference system.Finally,the convergence of tracking error is proved.In the seventh chapter,the research work of this dissertation is summarized,and some interesting future researches are also included.