Fault-Tolerant Consensus Tracking Control for Linear Multiagent Systems Under Switching Directed Network

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays

This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally based on the observed states, the observer-based resilient controller is proposed. An augmented system is presented by considering both the error system and the DTMBNNs with the controller. Secondly, a novel sufficient extended exponential dissipativity condition is obtained for the augmented system with unbounded time-varying delays by proposing a system solutions-based estimation approach. This method is based on system solutions and without constructing any Lyapunov-Krasovskii functionals (LKF), thereby reducing the complexity of theoretical derivation and computational workload. In addition, an algorithm is proposed to solve the nonlinear inequalities in the sufficient condition. Thirdly, the sufficient extended exponential dissipativity condition for the augmented system with bounded time-varying delays is also obtained. Finally, the effectiveness of the theoretical results is illustrated through two simulation examples.

Semi-Global Bipartite Fault-Tolerant Containment Control for Heterogeneous Multiagent Systems With Antagonistic Communication Networks and Input Saturation

Semi-global bipartite fault-tolerant containment control framework on antagonistic communication networks is proposed in this article for heterogeneous multiagent systems (MASs) under the influence of input saturation and actuator faults. An observer is constructed to estimate the leaders' states on signed digraph, where the communication networks are antagonistic. A fully distributed virtual control approach is developed to acquire the containment trajectory. Based on the observer, a semi-global containment control method is developed to compensate for the detrimental impacts of both input saturation and actuator faults. Besides, the dynamics and state-space dimensions of the agents can be different. The proposed framework overcomes two drawbacks of the conventional containment control: 1) the containment trajectory is obtained under general antagonistic communication networks, which is more general in engineering applications and 2) both actuator faults and input saturation are solved for heterogeneous agents, which relaxes the limitation of homogeneous dynamics. Finally, a simulation example is conducted to test and verify the feasibility of the proposed method framework.

Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov-Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.

Data-Based Output Synchronization of Multi-Agent Systems With Actuator Faults

In this brief, the output synchronization of multi-agent systems (MAS) with actuator faults is studied. To detect the faults, a backward input-driven fault detection mechanism (BIFDM) is presented for MAS. Different from previous works, the system operation can be monitored without system model by the proposed BIFDM. Then to tolerate the faults, a novel fault-tolerant controller (FTC) based on reinforcement learning (RL) and backward information (BI) is proposed. Particularly, by the combination of BI, the design of additional parameters for faults is avoided. Furthermore, the proposed FTC overcomes the shortcoming that the previous FTCs cannot be applied to heterogeneous MAS. Finally, two simulation examples are given to verify the effectiveness of the proposed methods.

Distributed Adaptive Fault-Tolerant Control for Multiagent Systems via Virtual-Actuator-Based Reconfiguration

This article aims to develop a virtual-actuator-based control scheme for the consensus tracking problem of multiagent systems (MASs) against actuator faults and mismatched disturbances. The proposed scheme has a double-layer structure. In the cyber layer, the nominal controller is designed with neighboring information for the fault-free case. While in the physical layer, the fault compensator, working as the virtual actuator, is applied to reconfigure faulty plants adaptively. This design enjoys the advantages that the nominal controller needs no adjustment and all its properties can be preserved after failure. Moreover, the proposed control scheme is distinguished by the following features: 1) the commonly imposed rank condition on outage faults is removed; 2) the norm bound of the leader's input is allowed to be unknown even though the topologies are switching and directed; and 3) there is no need to use the estimates of faults in the virtual actuator design, which means the negative impacts caused by the inaccurate fault estimation can be avoided. Finally, a numerical example is given to validate the effectiveness of the theoretical results.

Asymptotical Neuro-Adaptive Consensus of Multi-Agent Systems With a High Dimensional Leader and Directed Switching Topology

We study the asymptotical consensus problem for multi-agent systems (MASs) consisting of a high-dimensional leader and multiple followers with unknown nonlinear dynamics under directed switching topology by using a neural network (NN) adaptive control approach. First, we design an observer for each follower to reconstruct the states of the leader. Second, by using the idea of discontinuous control, we design a discontinuous consensus controller together with an NN adaptive law. Finally, by using the average dwell time (ADT) method and the Barbǎlat's lemma, we show that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the ADT is larger than a positive threshold. Moreover, we study the asymptotical neuroadaptive consensus problem for MASs with intermittent topology. Finally, we perform two simulation examples to validate the obtained theoretical results. In contrast to the existing works, the asymptotical neuroadaptive consensus problem for MASs is firstly solved under directed switching topology.

Consensus of MASs With Input and Communication Delays by Predictor-Based Protocol

In this article, the consensus problem of multiagent systems (MASs) affected by input and communication delays is investigated. A predictor-based state feedback protocol is used to reach the consensus of linear MASs by delay compensation. In order to analyze the maximum delay under the predictor-based protocol, the overall MASs are equivalent to the feedback interconnection system, including a linear time-invariant system and a time-delay operator, in view of the characteristic of the Laplacian matrix. Then, the maximum delay corresponding to the predictor-based protocol is evaluated by using the small gain theorem (SGT). Finally, two numerical examples are given to verify the effectiveness of the obtained consensus condition.

Reset Event-Triggered Adaptive Fuzzy Consensus for Nonlinear Fractional-Order Multiagent Systems With Actuator Faults

This article studies the problem of event-triggered adaptive fault-tolerant fuzzy output feedback consensus tracking control for nonlinear fractional-order multiagent systems with actuator failures under a directed graph. Considering the fact that the actual system works near the equilibrium point most of the time, a novel dynamic event-triggering strategy with the reset mechanism is proposed, where the dynamic threshold can be actively adjusted according to the preset conditions, so that the resource utilization can be further reduced. Based on an improved event-based consensus error, the state estimator about the derivative of reference trajectory and the adaptive law about the information of graph are constructed, which makes distributed consensus tracking control achieved without obtaining global information. Then, by introducing two adaptive compensating terms to deal with actuator failures and event-triggered measurement errors, it is shown in the sense of fractional-order stability criterion that tracking errors can converge to a compact set even if the fault parameters and modes are completely unknown. Finally, the correctness of the presented method is verified by a simulation example.

Distributed Adaptive Leader-Following Consensus for Nonlinear Multiagent Systems With Actuator Failures Under Directed Switching Graphs

This article studies the distributed adaptive failures compensation output-feedback consensus for a class of nonlinear multiagent systems (MASs) with multiactuator failures allowing unmatched redundancy under directed switching graphs. With estimated information of neighbors, a novel distributed reference generator is designed. To compensate the unmeasured state variables of each agent, a reduced-order dynamic gain filter is constructed. Based on the generator and filter, and using the recursive design method, a distributed adaptive protocol is designed, where the adaptive technique is used to compensate the actuator failures. The proposed scheme can significantly relax conditions on the communication graph, which allows the graph to be disconnected at any time instant. The number of introduced variables in the filter and its dimension is greatly reduced and, thus, reduces the numerical challenge. The output-feedback consensus for nonlinear MASs with actuator failures and possible unmatched actuator redundancy is addressed for the first time. The consensus error can converge to an arbitrarily small set not affected by actuator failures, and the resulting closed-loop system is semiglobally stable. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

A Multilayer Graph for Multiagent Formation and Trajectory Tracking Control Based on MPC Algorithm

This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

Sliding-Mode-Based Admissible Consensus Tracking of Nonlinear Singular Multiagent Systems Under Jointly Connected Topologies

The admissible consensus tracking problem of nonlinear singular multiagent systems (SMASs) with time-varying delay, uncertainties, and external disturbances under jointly connected topologies is investigated in this article. First, the sliding-mode control (SMC) is applied to effectively reduce the adverse effects of uncertainties and nonlinearities of systems. Then, by the combination of admissible analysis, the Cauchy convergence criterion, and SMC, the sufficient conditions for the admissible consensus tracking and disturbance rejection of SMASs under jointly connected topologies are provided. Furthermore, a distributed SMC law is designed such that the sliding-mode dynamics trajectories reach the sliding surface in finite time. Finally, the simulation results are utilized to indicate the effectiveness of the presented methods.

Kullback-Leibler Divergence-Based Optimal Stealthy Sensor Attack Against Networked Linear Quadratic Gaussian Systems

This article concentrates on designing optimal stealthy attack strategies for cyber-physical systems (CPSs) modeled by the linear quadratic Gaussian (LQG) dynamics, where the attacker aims to increase the quadratic cost maximally and keeping a certain level of stealthiness by simultaneously intercepting and modifying the transmitted measurements. In our work, a novel attack model is developed, based on which the attacker can launch strictly stealthy or ϵ -stealthy attacks. To remain strictly stealthy, the attacker only needs to solve an off-line semidefinite program problem. In such a case, the attack performance is optimal but limited. To achieve a higher desired attack effect than that of the strictly stealthy attack, the attacker sometimes needs to sacrifice the stealthy level. Thus, the ϵ -stealthy attack is analyzed, where an upper bound of the optimal attack performance is obtained by solving a convex optimization problem. Then, an optimal ϵ -stealthy attack is designed to achieve the upper bound, which differs from the existing suboptimal ϵ -stealthy attack for the considered LQG systems. Finally, the simulations are provided to verify the developed results.

Quantized Output-Feedback Control for Unmanned Marine Vehicles With Thruster Faults via Sliding-Mode Technique

This article is concerned with the quantized output-feedback control problem for unmanned marine vehicles (UMVs) with thruster faults and ocean environment disturbances via a sliding-mode technique. First, based on output information and compensator states, an augmented sliding surface is constructed and sliding-mode stability through linear matrix inequalities can be guaranteed. An improved quantization parameter dynamic adjustment scheme, with a larger quantization parameter adjustment range, is then given to compensate for quantization errors effectively. Combining the quantization parameter adjustment strategy and adaptive mechanism, a novel robust sliding-mode controller is designed to guarantee the asymptotic stability of a closed-loop UMV system. As a result, a smaller lower bound of the thruster fault factor than that of the existing result can be tolerated, which brings more practical applications. Finally, the comparison simulation results have illustrated the effectiveness of the proposed method.

Modeling multi-opinion propagation in complex systems with heterogeneous relationships via Potts model on signed networks

This paper investigates how the heterogenous relationships around us affect the spread of diverse opinions in the population. We apply the Potts model, derived from condensed matter physics on signed networks, to multi-opinion propagation in complex systems with logically contradictory interactions. Signed networks have received increasing attention due to their ability to portray both positive and negative associations simultaneously, while the Potts model depicts the coevolution of multiple states affected by interactions. Analyses and experiments on both synthetic and real signed networks reveal the impact of the topology structure on the emergence of consensus and the evolution of balance in a system. We find that, regardless of the initial opinion distribution, the proportion and location of negative edges in the signed network determine whether a consensus can be formed. The effect of topology on the critical ratio of negative edges reflects two distinct phenomena: consensus and the multiparty situation. Surprisingly, adding a small number of negative edges leads to a sharp breakdown in consensus under certain circumstances. The community structure contributes to the common view within camps and the confrontation (or alliance) between camps. The importance of inter- or intra-community negative relationships varies depending on the diversity of opinions. The results also show that the dynamic process causes an increase in network structural balance and the emergence of dominant high-order structures. Our findings demonstrate the strong effects of logically contradictory interactions on collective behaviors, and could help control multi-opinion propagation and enhance the system balance.

Completely Event-Triggered Consensus for Multiagent Systems With Directed Switching Topologies

This article studies the leader-following consensus of multiagent systems (MASs) with completely event-triggered mechanisms (CETMs) and directed switching topologies. When most event-triggered schemes in MASs only reduce each agent's data transmissions to neighbor agents, CETMs further reduce data transmissions to actuators with one triggered function in each agent. To guarantee the switching links utilized by CETMs contain a directed spanning tree at any time, triggered decisions are improved to include the instants at which each agent's output links or leader link change. Furthermore, a less conservative method based on matrix inequalities is combined to minimize the allowable average dwell time (ADT) of switching topologies under the design conditions of CETMs. Based on multiple Lyapunov functions (MLFs), the sufficient conditions for controller gains that guarantee the leader-following consensus of MASs are proposed when the ADT of switching topologies is larger than the allowable value. Finally, an example of autonomous underwater vehicles (AUVs) is given to illustrate the effectiveness of CETMs.

Adaptive NN-Based Consensus for a Class of Nonlinear Multiagent Systems With Actuator Faults and Faulty Networks

This article addresses the problem of fault-tolerant consensus control of a general nonlinear multiagent system subject to actuator faults and disturbed and faulty networks. By using neural network (NN) and adaptive control techniques, estimations of unknown state-dependent boundaries of nonlinear dynamics and actuator faults, which can reflect the worst impacts on the system, are first developed. A novel NN-based adaptive observer is designed for the observation of faulty transformation signals in networks. On the basis of the NN-based observer and adaptive control strategies, fault-tolerant consensus control schemes are designed to guarantee the bounded consensus of the closed-loop multiagent system with disturbed and faulty networks and actuator faults. The validity of the proposed adaptively distributed consensus control schemes is demonstrated by a multiagent system composed of five nonlinear forced pendulums.

Cooperative Tracking Control of Heterogeneous Mixed-Order Multiagent Systems With Higher-Order Nonlinear Dynamics

This article investigates a class of finite-time cooperative tracking problems of heterogeneous mixed-order multiagent systems (MASs) with higher-order dynamics. Different from the previous works of heterogeneous MASs, the agents in this study are considered to have different first-, second-, or even higher-order nonlinear dynamics. It means that, according to different tasks and situations, the following agents can have nonidentical orders or different numbers of states to be synchronized, which is more general for the practical cooperative applications. The leader is a higher-order nonautonomous system and contains full state information to be synchronized for all agents with mixed-order dynamics. Accordingly, the spanning tree is defined based on the specific state rather than on the agent to guarantee that each following agent can receive adequate state information. Distributed control protocols are designed for all agents to achieve the ultimate state synchronization to the leader in finite time. The Lyapunov approach is used for the stability analysis and a practical example of mixed-order mechanical MASs verifies the effectiveness and performance of the proposed distributed control protocols.

Robust Cooperative Optimal Sliding-Mode Control for High-Order Nonlinear Systems: Directed Topologies

This article is concerned with the robust cooperative optimal control of nonlinear multiagent systems (MASs) with external disturbances and modeling uncertainties. Using the super-twisting algorithm, a continuous sliding-mode control protocol is presented for high-order nonlinear MASs with multiple inputs. The sliding-mode dynamics is modeled by the Takagi-Sugeno fuzzy approach, and the nominal control protocol that guarantees the robust optimization of the cost function is designed. Directed topologies are allowed using the presented protocol, and many assumptions about topologies are removed. Finally, three numerical examples are reported to demonstrate the effectiveness and improved performance of the presented protocol.

Adaptive Fuzzy Event-Triggered Control for High-Order Nonlinear Systems With Prescribed Performance

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

Zonotopic Fault Detection for 2-D Systems Under Event-Triggered Mechanism

This article studies the problem of event-triggered fault detection (FD) for 2-D systems subjected to amplitude-bounded exogenous disturbance and measurement noise via a zonotopic residual evaluation mechanism. An event-triggered mechanism is introduced into the FD framework to save limited communication resources. A finite-frequency (FF) mixed l_∞/h_∞ index is derived to ensure the residual signal is sensitive to a fault signal while robust to disturbance and noise, based on which an optimal mixed l_∞/h_∞ FD filter design criterion is provided. Instead of constant thresholds, novel zonotope-based dynamic thresholds are utilized for residual evaluation. Finally, simulation results are presented to illustrate the effectiveness of the developed mechanism.

Distributed Fault-Tolerant Containment Control for Linear Heterogeneous Multiagent Systems: A Hierarchical Design Approach

In this article, the problem of distributed hierarchical fault-tolerant containment control for heterogeneous linear multiagent systems (MASs) is investigated. In most of the existing distributed methods for MASs with system failures, each agent broadcasts its state, or output, or the estimation of state to neighbors. Once an agent is subjected system failures, faults affect the dynamics of other agents over the network, that is, the influence of faults on the agent will propagate with the network. In order to overcome this drawback, a fault-tolerant hierarchical containment control protocol is developed, which includes two layers: 1) the upper layer and 2) the lower layer. The upper layer consists of a virtual system and a cooperative controller to achieve a virtual containment objective. The lower layer consists of an actual system and a fault-tolerant controller to track the upper layer virtual system. Compared with the existing results, the phenomenon of fault propagation can be avoided by introducing the hierarchical design approach, that is, the fault of agent i only affects the dynamics of itself, and does not affect the dynamics of other agents through the network. It is shown that each follower converges asymptotically to a convex hull spanned by leaders with external input. Finally, the developed method is demonstrated by simulation results.

Cooperative Output-Feedback Secure Control of Distributed Linear Cyber-Physical Systems Resist Intermittent DoS Attacks

This article studies a cooperative output-feedback secure control problem for distributed cyber-physical systems over an unreliable communication interaction, which is to achieve coordination tracking in the presence of intermittent denial-of-service (DoS) attacks. Under the switching communication network environment, first, a distributed secure control method for each subsystem is proposed via neighborhood information, which includes the local state estimator and cooperative resilient controller. Second, based on the topology-dependent Lyapunov function approach, the design conditions of secure control protocol are derived such that cooperative tracking errors are uniformly ultimately bounded. Interestingly, by exploiting the topology-allocation-dependent average dwell-time (TADADT) technique, the stability analysis of closed-loop error dynamics is presented, and the proposed coordination design conditions can relax time constraints on interaction topology switching. Finally, two numerical examples are presented to demonstrate the effectiveness of the theoretical results.

Adaptive Fuzzy Fault-Tolerant Control against Time-Varying Faults via a New Sliding Mode Observer Method

In this study, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults, and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

Cooperative control for cyber-physical multi-agent networked control systems with unknown false data-injection and replay cyber-attacks

The paper discusses the cooperative tracking problem of partially known cyber-physical multi-agent networked systems. In this system, there exist two cascaded chances for cyber-attacks. The local agent is of networked system type that is subjected to unknown false data-injection and replay cyber-attacks that are dissimilar in the sensor-controller and the controller-actuator network parts. The communication between any two agents, if they are connected, is accomplished via a communication network that is subjected to false data-injection cyber-attacks. The problem of the existing two cascaded chances for cyber-attacks is solved in three steps. First, with partially known system parameters and unknown false data-injection and replay cyber-attacks, the state estimates of all the local followers are evaluated by designing local adaptive observers. Second, a new technique is designed to compensate for the unmatched terms that result from the use of local adaptive observers. After that, distributed adaptive leader-follower security controllers are proposed based on the local estimated information in addition to the infected arrived information from the neighbors. Meanwhile, it is verified that the suggested security control method guarantees that all states of the followers under the considered cyber-attacks follow the given leader asymptotically. The efficacy of the developed adaptive leader-follower security controllers is verified via an illustrative example.

Cooperative Fault-Tolerant Output Regulation for Multiagent Systems by Distributed Learning Control Approach

In this article, a new distributed learning control approach is proposed to address the cooperative fault-tolerant output regulation problem for linear multiagent systems with actuator faults. First, a distributed estimation algorithm with an online learning mechanism is presented to identify the system matrix of the exosystem and to estimate the state of the exosystem. In particular, an auxiliary variable is introduced in the distributed estimation algorithm to construct a data matrix, which is used to learn the system matrix of the exosystem for each subsystem. In addition, by resetting the state of the estimator and by using the identified matrix to update the estimator, all subsystems can reconstruct the state of the exosystem at an initial period of time, which is used for the neighbor subsystem to learn the system matrix of the exosystem. Based on the designed estimator, a novel distributed fault-tolerant controller is developed. Compared with the existing cooperative output regulation results, the system matrix of the exosystem considered in this article is unknown for all subsystems. Finally, a simulation example is provided to show the effectiveness of the obtained new design techniques.

Fully Distributed Synchronization of Dynamic Networked Systems With Adaptive Nonlinear Couplings

In this article, we consider the distributed synchronization problem of dynamic networked systems with adaptive nonlinear couplings. Based on how the information is collected, the interactions between subsystems are characterized by nonlinear relative state couplings and nonlinear absolute state couplings. In both cases, we show that the considered nonlinear interactions can be used to simulate the couplings with disturbed relative or absolute states. In order to implement the nonlinear couplings in a fully distributed fashion, adaptive control laws are proposed for the adjustment of coupling strengths between connected subsystems. It is shown that the connected network topology is sufficient to ensure the synchronization of dynamic networked systems with the proposed adaptive nonlinear coupling methods. Different from many existing works, the σ -modification technique is used to suppress the increase of the coupling strengths with an additional benefit of preventing the coupling strengths from increasing. Simulation examples are given to assess the performance of the proposed adaptive nonlinear couplings.

Synchronization in Kuramoto Oscillator Networks With Sampled-Data Updating Law

In this article, we are concerned with the synchronization problem of Kuramoto oscillators under the sampled-data updating law. This article is motivated by the needs of synchronization of Kuramoto oscillators in the presence of periodic and asynchronous coupling updates. Based on the periodical sampled-data method, a sufficient condition ensuring synchronization under periodic updates is derived. In order to relax the requirement of having all data updated simultaneously, an event-triggered law is designed to implement asynchronous coupling updates. Our synchronization analysis does not rely on any linearization technique around equilibrium points. Instead, we employ the Lyapunov stability theory and nonsmooth analysis technique to deduce the synchronization conditions and estimate the region of attraction. The effectiveness of the proposed sampled-data coupling is illustrated by numerical simulations.