This paper investigates the distributed event-triggered fixed-time bipartite average tracking problem for multi-agent systems(MASs) with nonlinear dynamics. Different from the finite-time algorithm, fixed-time consensus strategy is independent of the initial conditions of the system. Firstly, based on our proposed fixed-time bipartite consensus controller, the event-triggered mechanism is introduced to reduce the update frequency of the controller. Secondly, a controller with saturation function is designed to mitigate the chatting phenomenon of the control input. Drawing on the fixed-time control theorem and Lyapunov theory, the sufficient conditions for achieving fixed-time bipartite consensus under the event-triggered strategy are derived, and a strictly positive lower bound of the inter-event is provided to demonstrate that the Zeno behavior is avoided. Moreover, the fixed-time bipartite average tracking problem under denial-of-service (DoS) attacks is investigated, where the attack model is characterized by the average non-attack rate. Finally, to verify the validity of the theoretical results, numerical examples are presented for illustration.

Load Frequency Control (LFC) is imperative in ensuring the stability of power systems in terms of frequency, and its efficacy is frequently impacted by device nonlinearities and cyber attacks. This paper investigates the security control of multi-area LFC power systems under energy-limited denial-of-service (DoS) attacks. Firstly, a unified Takagi-Sugeno (T-S) fuzzy model for multi-area LFC power systems is developed by analysing the nonlinear components in the dynamic characteristics of the turbine and governor. Secondly, a DoS-dependent novel event-triggering mechanism (NETM) is proposed, and the impact of DoS attacks is incorporated into the design of the triggering mechanism. Sufficient conditions for the uniform ultimate boundedness stability of the power system are provided, and an algorithm is devised to determine the time required for a power system state trajectory to enter the stable region. The efficacy of the proposed control method is subsequently validated through the utilization of simulation examples.

This study investigates the resilient event-triggered load frequency control (LFC) issue of islanded microgrids (MGs) subject to denial of service attacks (DoSAs) and unknown false data injection attacks (FDIAs) simultaneously. An augmented extended observer is established to estimate both unavailable system states and unknown FDIA signals. A resilient adaptive event-triggered (AET) scheme, incorporating DoSA detection, FDIA estimation, and dynamic threshold adjustment, is proposed to reduce network resource consumption while countering cyberattacks. Some sufficient criteria are derived to ensure the asymptotic stability of the augmented estimation error system withH∞performance. Additionally, the uniformly ultimate boundedness of the system is also guaranteed even under hybrid cyberattacks. Finally, simulation studies are used to illustrate the effectiveness of the proposed method.

This paper presents an observer-based fuzzy event-triggered load frequency control (LFC) strategy and a neural network (NN) approximation approach for wind power systems (WPSs) facing network resource constraints and multiple cyberattacks. First, a Takagi-Sugeno (T-S) fuzzy model is developed to capture the nonlinear dynamics of the wind turbine. A novel multiple cyberattack model is then proposed, relaxing stringent assumptions about denial-of-service (DoS) and false data injection (FDI) attacks prevalent in prior studies. An observer-based dynamic event-triggered controller is subsequently designed to reduce network burden in WPSs while mitigating DoS effects. This controller incorporates an NN-based online weight adjustment algorithm to approximate and counteract the false data injected by FDI attacks. Lyapunov analysis demonstrates that the augmented error system achieves asymptotic stability with H∞performance in the absence of DoS attacks and maintains uniformly ultimate boundedness under multiple cyberattacks. Finally, a numerical example validates the effectiveness of the proposed control strategy and NN approximation approach.

This paper addresses the secure control problem for the Takagi-Sugeno (T-S) fuzzy wind turbine system (WTS) subject to hybrid cyberattacks. To reduce system performance loss and redundant data transmission under such attacks, a novel adaptive memory event-triggered mechanism (ETM) is proposed, offering two key advantages. First, unlike traditional ETMs that rely solely on current system information, the adaptive memory ETM utilizes historical release data to adjust communication frequency and enhance control effectiveness. Second, an attack-related triggering condition and adaptive law are introduced to reduce the invalid data transmission induced by denial of service (DoS) attacks and extend the lifespan of the sensor node. Sufficient conditions are derived to ensure the mean-square$\mathcal {H}_{\infty }$asymptotic stability of the resulting T-S fuzzy WTS, while also guaranteeing uniformly ultimate boundedness in the presence of DoS attacks. Finally, an illustrative example is used to show the effectiveness of the proposed method.

The fast-growing development of floating wind turbines demands control systems capable of both reducing output power fluctuations and fault-tolerant function. In this paper, we propose an adaptive switched sliding mode controller to enhance the performance of floating wind turbine systems in the presence of environmental uncertainties and actuator faults. A control-oriented switched linear model for floating wind turbines is introduced, considering the average dwell time. Based on the proposed controller, a full-order state observer and an adaptive law compensate for the debilitated control outputs resulting from detecting errors, disturbances, and faults. The control parameters are derived by solving stability theorems, which are proved by the Lyapunov stability theory, linear matrix inequality technique, and average dwell time technique. The proposed model and controller are validated on the NREL 5MW wind turbine and spar-buoy platform using the high-fidelity fatigue, aerodynamics, structures, and turbulence (FAST) code. The performance of the proposed controller is compared with an optimal gain-scheduling proportional-integral controller under different wind-wave combined conditions. The results show that the proposed controller improves the power quality and attenuates mechanical loads of floating wind turbine under healthy and faulty conditions.Note to Practitioners—Floating wind turbines have drawn significant interest in renewable energy. When operating in the ocean far from shore, it is of great importance for floating wind turbines to have fault-tolerance capabilities for achieving stable power quality when faults occur in one or more components. A challenging problem is how to mitigate the fault effects on the wind turbine system. This paper proposes an adaptive fault-tolerant control strategy in the case of actuator fault occurrence in floating wind turbines.

For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.

This paper focuses on the observer-based event-triggered(ET) scheme issue for interval type-2(IT-2) fuzzy systems under the multiple cyber attacks including false data injection (FDI) attacks and denial-of-service (DoS) attacks. Considering both intermittency of DoS attacks and unmeasurable of partial systems, a switched observer-based nonlinear systems in the form of interval type-2 (IT-2) fuzzy models is established. An adaptive event-triggered (AET) scheme that can modulate the threshold value is proposed for better saving the communication resources. Considering the impact of stochastic FDI attacks, a switched AET controller is designed. Then, Lyapunoval analysis method is developed such that the augmented error IT-2 fuzzy systems is mean square exponentially stable with$H_\infty$performance. Finally, a practical example is employed to reflect the validity of the approach.

The synchronization for a class of switched uncertain neural networks (NNs) with frequent asynchronism based on event-triggered control is researched in this article. Compared with existing works that require one switching during an inter-event interval, frequent switching is allowed in this article. By employing controller-mode-dependent Lyapunov-Krasovskii functionals (LKFs), we devise the control strategy to guarantee that the switched NNs can be synchronized. The proposed LKFs can make full use of system information. Using an improved integral inequality, some sufficient stability conditions formed by linear matrix inequalities (LMIs) are derived for the synchronization of switched uncertain NNs. Average dwell time (ADT) is obtained in the form of inequality that includes the maximum inter-event interval. In addition, the existence of lower bound of inter-event interval is discussed to avoid Zeno behavior. At last, the feasibility of the proposed method is proven by a numerical example.

This article addresses the synchronization issue for inertial neural networks (INNs) with heterogeneous time-varying delays and unbounded distributed delays, in which the state quantization is considered. First, by fully considering the delay and sampling time point information, a modified looped-functional is proposed for the synchronization error system. Compared with the existing Lyapunov–Krasovskii functional (LKF), the proposed functional contains the sawtooth structure term$\mathcal{V}_8(t)$and the time-varying terms$e_\mathit{x}(t-\beta\hbar(t))$and$e_\mathit{y}({t-\beta\hbar(t))}$. Then, the obtained constraints may be further relaxed. Based on the functional and integral inequality, less conservative synchronization criteria are derived as the basis of controller design. In addition, the required quantized sampled-data controller is proposed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to show the effectiveness and superiority of the proposed scheme in this article.

This article focuses on a new outlier-resistant nonfragile control issue for a class of Takagi–Sugeno fuzzy delayed neural networks with reaction–diffusion terms. Compared with the existing delayed neural networks, fuzzy control rules and reaction–diffusion phenomenon are considered simultaneously, which makes the proposed models more practical. Furthermore, when subjected to abnormal interference, measurement outputs result in measurement outliers. In order to mitigate the negative effects on the estimation error, a state estimator scheme is presented by introducing a saturation function. By using an appropriate Lyapunov–Krasovskii functional and with the help of a free-weighting matrix, sufficient conditions can be deduced to guarantee the asymptotical stability and prescribed $\mathcal {H}_{\infty }$ performance index of the disturbance attenuation of the estimation error. Next, a design strategy of an outlier-resistant nonfragile state estimator is put forward by employing some decoupling techniques. An illustrative example is exploited to illustrate the validity and feasibility of the proposed state estimator. Finally, the obtained theoretical results are applied to image encryption. The experimental analysis demonstrates that the presented encryption scheme is feasible and effective.

To reduce the computational burden and resist the denial-of-service (DoS) attacks, a resilient distributed sampled-data control scheme is proposed for multiagent systems. The agent states are sampled periodically by the sensors. DoS attacks disrupt the data communication from transmitters to controllers randomly or periodically with a limited duration time. Information on DoS attacks can be obtained by introducing novel logic processors embedded in corresponding controllers. Next, the problem of resilient control can be converted into one concerned with the upper and lower bound of the sampling interval of an aperiodic sampled-data control system. Some sufficient criteria for developing resilient distributed controllers are derived using the novel looped Lyapunov functional approach and the free-matrix-based inequality method. Finally, two illustrative examples, unmanned aerial vehicles and the two-mass-spring systems, are provided to demonstrate the efficiency of the proposed resilient distributed sampled-data control protocols against the DoS attacks.

This article focuses on the event-triggered synchronization of delayed discrete-time chaotic neural networks with quantized effect and stochastic deception attack. First, for alleviating the network communication and communication burden, an event-triggered mechanism and a logarithmic quantizer are employed, separately. Second, for integrating the impact of event-triggered scheme, quantization, and cyberattack in a unified framework, a synchronization error model is introduced. Third, based on the Lyapunov–Krasvovskii functional (LKF), some sufficient conditions are established to guarantee the synchronization of drive system and response system. Furthermore, the co-design controller and homologous event-triggered parameters are also derived according to the presented asymptotic stability condition. Finally, the availability of the proposed method is verified by some numerical examples.

In this study, the problem of fault-tolerant sampled-data H∞ control for a networked control system with random time delays and actuator faults is investigated. Stochastic variables conforming with the Bernoulli distribution are considered to depict random time delays. A state feedback sampled-data controller is designed to ensure the asymptotical stability and H∞ performance of the resulting closed-loop system. By applying the Lyapunov–Krasovskii stability theory and the reciprocally convex combination lemma, a stability criterion for a random time-varying delay system is developed that guarantees the designed controller can satisfy the requirements of stability and maneuverability. The desired controller gain is then found based on the linear matrix inequalities. Finally, as a real application, a quarter-vehicle suspension system model is provided to demonstrate the benefits and validity of the proposed control law.

The issue of finite-time stabilization (FTS) for the memristor-based inertial neural networks (MINNs) with mixed time-varying delays (MTVDs) is researched by virtue of a new analytical method in this brief. First, an appropriate reliable control strategy is proposed for MINNs, which takes the influence of actuator failures into account. Second, by combining Lyapunov functional theory with new analysis techniques, novel theoretical results to guarantee the FTS for the concerned MINNs are acquired, and the desired reliable controller gains are obtained simultaneously. In additions, compared with the previous research works, the FTS results obtained in this paper are established directly from the MINNs themselves without using variable transformation method. In the end, two simulations are exploited to show the correctness and practicability of the acquired theoretical results.

This article is concerned with a neural adaptive tracking control scheme for a class of multiinput and multioutput (MIMO) nonaffine nonlinear systems with event-triggered mechanisms, which include the fixed thresholds, triggering control inputs, and decreasing functions of tracking errors. Unlike the existing results of nonaffine nonlinear controller decoupling, a novel nonlinear multiple control inputs separated design method is proposed based on the mean-value theorem and the Taylor expansion technique. By this way, a weaker condition of nonlinear decoupling is provided to instead of the previous ones. Then, introducing a prescribed performance barrier Lyapunov function (PPBLF) and using neural networks (NNs), the presented event-triggered controller can maintain better tracking performance and effectively alleviate the computation burden of the communication procedure. Furthermore, it is proved that all the closed-loop signals are bounded and the system output tracking errors are confined within the prescribed bounds. Finally, the simulation results are given to demonstrate the validity of the developed control scheme.

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

This paper focuses on the problem of dissipative filtering for nonlinear interconnected systems with interval time-varying delays. The considered nonlinear interconnected system is modeled by Takagi-Sugeno fuzzy rules. By constructing the delay dependent Lyapunov-Krasovskii functional and using new integral inequality, the delay-dependent condition is established to ensure that the derived closed-loop system is asymptotically stable with strict (Q, S, R) - α- dissipativity. In addition, a suitable filter is designed by solving a set of linear matrix inequalities. The presented method can provide better performance than the existing ones for the case of H∞ filtering. A simulation example is given to demonstrate the validity of the developed filter design technique.

This paper focuses on the synchronization problem of Takagi-Sugeno (T-S) fuzzy complex networks with partial and discrete-time couplings via event-triggered sampling control. Different from traditional control methods, a more general and practical event-triggered communication scheme with nonuniform sampling is newly designed for T-S fuzzy complex networks. Then, a Lyapunov-Krasovskii functional (LKF) with a novel input-delay-product-type (IDPT) term is presented. The IDPT term can fully capture the information of the nonlinear functions and the actual sampling pattern. Based on the new IDPT LKF, less conservative synchronization criteria are derived. Meanwhile, by solving a set of linear matrix inequalities, the desired pinning control gains are precisely obtained. Simulation examples are provided to illustrate the effectiveness and superiorities of the proposed results.

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

This paper investigates the problem of event-triggered H∞load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed H∞performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

This paper focuses on the problem of exponential stability for a class of neural networks with time-varying delays. A more general inequality is established which extends the auxiliary function-based integral inequality. Based on the inequality and parameter-dependent matrix inequality, an improved delaydependent stability criterion is obtained by constructing an augmented Lyapunov functional. Three numerical examples are given to illustrate the efficiency of the method.

In this paper, event-triggered reliable dissipative filtering is investigated for a class of Takagi-Sugeno (T-S) fuzzy systems. First, a reliable event-triggered communication scheme is introduced to release sampled measurement outputs only if the variation of the sampled vector exceeds a prescribed threshold condition. Second, an asynchronous premise reconstruct method for T-S fuzzy systems is presented, which relaxes the assumption of the prior work that the premises of the plant and the filter are synchronous. Third, the resulting filtering error system is modeled under consideration of event-triggered communication, sensor failure, and asynchronous premise in a unified framework. By adopting the Lyapunov functional method and integral inequality approach, a delay-dependent criterion is developed to guarantee asymptotic stability for the filtering error systems and achieve strict (Q, S, R) - α dissipativity. Consequently, suitable filters and the event parameters can be derived by solving a set of linear matrix inequalities. Finally, an example is given to show the effectiveness of the proposed method.

This note investigates sampled-data control for chaotic systems. A memory sampled-data control scheme that involves a constant signal transmission delay is employed for the first time to tackle the stabilization problem for Takagi-Sugeno fuzzy systems. The advantage of the constructed Lyapunov functional lies in the fact that it is neither necessarily positive on sampling intervals nor necessarily continuous at sampling instants. By introducing a modified Lyapunov functional that involves the state of a constant signal transmission delay, a delay-dependent stability criterion is derived so that the closed-loop system is asymptotically stable. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalities. Compared with the existing results, a larger sampling period is obtained by this new approach. A simulation example is presented to illustrate the effectiveness and conservatism reduction of the proposed scheme.

This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.

In this paper, the problem of nonfragile sampled-data synchronization of delayed complex dynamical networks with randomly occurring controller gain fluctuations (ROCGFs) is studied. First, more applicable nonfragile memory sampled-data controllers are designed, which involve the signal transmission delay and ROCGFs. The controller gain fluctuations appear in a random way, which obey certain Bernoulli distributed white noise sequences. Second, a modified piecewise Lyapunov-Krasovskii functional (LKF), which involves cubic sawtooth structure term, is constructed for the first time. Third, based on the LKF, less conservative synchronization criteria are established. In comparison with the existing results, the constraint condition of the positive definition of the LKF is less restrictive, since it does not need to be positive definite for all time, but is only required to be positive definite at sampling times. Finally, the effectiveness and advantages of the obtained results are illustrated by two numerical examples.

In this paper, we investigate the problem of stability and stabilization for sampled-data fuzzy systems with state quantization. By using an input delay approach, the sampled-data fuzzy systems with state quantization are transformed into a continuous-time system with a delay in the state. The transformed system contains nondifferentiable time-varying state delay. Based on some integral techniques, some new stability and stabilization criteria are first proposed by a modified Lyapunov functional. Furthermore, in the case of no quantization, some new stability and stabilization criteria are also obtained. It is shown that the new stability and stabilization criteria can provide a larger upper bound of the sampling interval than some existing ones in the literature. Two simulation examples are given to show the effectiveness of the proposed design method.