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This study presents distributed event-based control strategies for a networked dynamical system consisting of N linear time-invariant interconnected subsystems. Each subsystem broadcasts its state over the network according to certain triggering rules that only depend on local information. The system can converge asymptotically to the equilibrium point under the proposed control design, and the existence of a lower bound for the broadcasting period is guaranteed. A novel model-based approach is derived to reduce the communication between the agents. Simulation results show the effectiveness of the proposed approaches and illustrate the theoretical results.

In a distributed ‘networked control system’ (NCS), multiple physical systems or agents are connected to their corresponding controllers through a shared packet-switched communication network. For such distributed NCS, periodic sampled controller design is unsuitable to handle packet-switched closed-loop control systems and a novel stochastic optimal adaptive event-sampled controller scheme is proposed in the application layer for each physical system or agent expressed as an uncertain linear dynamic system. Lyapunov stability analysis will be utilised to derive the event trigger condition. In addition, a network scheduling protocol is also required for such NCS. Traditional network scheduling protocols are unsuitable for such NCS since the behaviour of the physical systems is disregarded during the protocol design. Therefore in this study, a novel distributed network scheduling protocol via cross-layer approach is developed to improve the performance of distributed NCS by minimising an overall system cost function which consists of the information collected from both the event-triggered controller for each physical system in the application layer and the distributed scheduling protocol from the network layer. It will be demonstrated that the proposed co-design approach will not only allocate the network resources efficiently but also it will improve the performance of the overall distributed NCS. Simulation results are included to demonstrate the effectiveness of the proposed cross-layer co-design.

This paper considers the identification problem of network structures of interconnected dynamical systems using measured output data. In particular, we propose an identification method based on the measured output data of each node in the network whose dynamic is unknown. The proposed identification method consists of three steps: we first consider the outputs of the nodes to be all the states of the dynamics of the nodes, and the unmeasurable hidden states to be dynamical inputs with unknown dynamics. In the second step, we define the dynamical inputs as new variables and identify the dynamics of the network system with measured output data using Koopman operators. Finally, we extract the network structure from the identified dynamics as the information transmitted via the network. We show that the identified coupling functions, which represent the network structures, are actually projections of the dynamical inputs onto the space spanned by some observable functions. Numerical examples illustrate the validity of the obtained results.

This study is concerned with modelling and observer-based H∞ controller design for a continuous-time networked control system with network-induced delays and packet dropouts. A new model for an observer-based networked control system is first established by proposing a linear estimation-based delay compensation method. Then some controller design criteria are obtained by constructing an interval time-varying delay decomposition-based Lyapunov functional. A new bounding inequality is introduced to transfer non-linear matrix inequalities into a solvable optimisation problem. A numerical example is given to illustrate the merits and effectiveness of the obtained results.

The efficient utilization of computation and communication resources became a critical design issue in a wide range of networked systems due to the finite computation and processing capabilities of system components (e.g., sensor, controller) and shared network bandwidth. Event-triggered mechanisms (ETMs) are regarded as a major paradigm shift in resource-constrained applications compared to the classical time-triggered mechanisms, which allows a trade-off to be achieved between desired control/estimation performance and improved resource efficiency. In recent years, dynamic event-triggered mechanisms (DETMs) are emerging as a promising enabler to fulfill more resource-efficient and flexible design requirements. This paper provides a comprehensive review of the latest developments in dynamic event-triggered control and estimation for networked systems. Firstly, a unified event-triggered control and estimation framework is established, which empowers several fundamental issues associated with the construction and implementation of the desired ETM and controller/estimator to be systematically investigated. Secondly, the motivations of DETMs and their main features and benefits are outlined. Then, two typical classes of DETMs based on auxiliary dynamic variables (ADVs) and dynamic threshold parameters (DTPs) are elaborated. In addition, the main techniques of constructing ADVs and DTPs are classified, and their corresponding analysis and design methods are discussed. Furthermore, three application examples are provided to evaluate different ETMs and verify how and under what conditions DETMs are superior to their static and periodic counterparts. Finally, several challenging issues are envisioned to direct the future research.

Management of resources is a constant topic in industrial systems. How to use minimum communication resources is of particular interest for control and estimation of networked systems. It raises the question for researchers in the field: how often should one update control and estimation? One of the most intelligent approaches is to trigger updates by events. In the literature, event-triggered control and estimation have been widely studied in the last decade. On one hand, events should be triggered sufficiently frequent to maintain system performance; on the other hand, the possibility of Zeno behavior caused by infinite frequency should be avoided. This review aims at revisiting some existing triggering techniques in a unified formulation, separated from system dynamics and control and estimation strategies. It brings readers better understanding of triggering mechanisms, the underlying technical challenges, and some promising future research topics.

A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create “pluralistic ignorance”, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals.

In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.

With the rapid advancement of information technology, the networked systems (NSs) have found extensive application across various fields. As the complexity and scale of NSs continue to increase, the fault detection plays an important role in ensuring the reliable operation of the system and has attracted the attention of scholars. In this paper, the research progress of fault detection (FD) for NSs is reviewed, especially the FD methods in communication-constrained situations. First, the research significance of FD for NSs and the current research hotspots are reviewed. Subsequently, the methods of FD subject to time delay, measurement loss, signal quantization and cyber attack are summarized. Next, for the NSs constrained by communication protocols, the corresponding FD methods are recalled. Then some research results on FD for NSs with finite-time index constraint and memory-scheduled strategy are presented. Finally, the limitations of current FD methods are summarized and the future research directions are pointed out.

This paper addresses the time‐varying formation control issue of networked multi‐agent systems with actuator fault and random communication constraints. A time‐varying formation active fault‐tolerant predictive control scheme is proposed. First, a composite state observer is designed to jointly estimate the system state and actuator fault. Then, an active fault‐tolerant predictive control algorithm is developed to generate a control prediction sequence such that actively compensating for actuator fault and random communication constraints. The design principle of control parameters is obtained by deriving the system stability condition. Finally, numerical simulations and practical experiments are carried out to verify the effectiveness and feasibility of the proposed control scheme. This paper tackles the time‐varying formation control of networked multi‐agent systems with actuator fault and random communication constraints. A time‐varying formation active fault‐tolerant predictive control scheme is proposed. A composite state observer estimates system state and actuator fault, and an active fault‐tolerant predictive control algorithm generates a control prediction sequence to compensate for the fault and communication constraints.