This paper investigates an event-triggered security control strategy for networked autonomous ground vehicles against actuator attacks. Firstly, a novel bandwidth-aware average-event-triggered protocol is proposed to improve the usage efficiency of limited network resources. There are two main merits of this protocol, the one is that the average of historical data is used to mitigate the over-triggering resulted from random data jitter, the other is that a bandwidth-aware dynamic triggering threshold is constructed to increase the adaption ability of protocol to the time-varying bandwidth status. Secondly, in order to implement state feedback control, a dynamic observer based on a dynamic variable related to the observation error is presented to enhance the state estimation accuracy. Thirdly, a new attack estimator relying on hyper basis function neural network is devised to estimate the unknown malicious attacks injected into the actuator. By combining the observed state and estimated attack, an attack-compensation-based security control strategy is developed to mitigate the negative influence caused by actuator attacks and further increase control performance. Then, by using Lyapunov theory and linear matrix inequality techniques, several sufficient criteria to calculate the controller and observer matrices are derived. Finally, the validity of the addressed strategy is verified via some simulation outcomes.

This paper studies the reinforcement learning-based distributed secondary frequency control and active power allocation of islanded microgrids under event-triggered mechanism. First, a novel bandwidth-conscious memory-event-triggered mechanism is proposed to reduce communication and computation burdens, which introduces the mean of memory signals to smooth the measured outputs perturbed by disturbances and noises. Meanwhile, a dynamic triggering threshold depending on real-time bandwidth status is constructed to adaptively adjust the data transmission rate according to the changes in bandwidth status and system responses. Second, a new reinforcement learning-based distributed secondary controller using Q-learning algorithm is presented to dynamically regulate the frequency controller gains in response to complex system environments, which helps in achieving better frequency restoration performance. Third, the system stability is analyzed by some linear matrix inequality conditions. Then, simulation outcomes based on load variation and plug-and-play test illustrate that our strategy achieves satisfactory performance in frequency restoration and active power sharing. In addition, some comparison results show the merits of the constructed event-triggered scheme and Q-learning-based distributed secondary frequency controller.

This paper focuses on the T-S fuzzy output path tracking control issue for variable speed autonomous ground vehicles (VSAGVs) by using state observer and event-triggered mechanism. The model of VSAGVs with nonlinear dynamics and parameter uncertainty is represented by the uncertain T-S fuzzy system. To enhance the estimation performance of the unknown system state, a fuzzy proportional- integral observer containing an extra integral term of estimation error is designed. To reduce the network communication burden, a novel deep-Q-learning-based proportional- integral event-triggered mechanism by adding the integration of the normal triggering rule and introducing an optimization strategy of the triggering threshold. Compared with the classical normal and dynamic event-triggered mechanisms, a larger inter-event time can be generated by the proposed proportional- integral event-triggered mechanism. In addition, the deep-Q-learning-based optimization strategy is able to dynamically regulate the triggering rate according to the historical triggering performance and control performance. Some sufficient conditions are derived to solve the fuzzy observer and controller gains and the triggering matrix such that the VSAGV is asymptotically stable with given$H_{\infty }$level. Finally, the superiorities of the addressed approach are confirmed through some simulation results.

This paper investigates the neural network-based event-triggered attack-compensation control problem of T-S fuzzy systems in the presence of actuator attacks. In order to enhance the triggering performance, a novel improved dynamic memory-event-triggered mechanism is developed. Three significant features of this mechanism are: 1) the average of some memory outputs is used rather than the current output to decrease the triggering events raised from frequent data jitter; 2) a dynamic variable related to the memory-based triggering condition is introduced to enlarge the triggering interval; 3) Jensen inequality is applied to relax the conventional triggering condition expressed as the quadratic form of the average of memory signals to further improve the triggering efficiency. To execute the state- feedback control and mitigate the impact of unknown malicious attacks injected in the actuator, a T-S fuzzy observer and a radial basis function neural network-based estimator are designed to estimate the system state and actuator attack, respectively. Based on the combination of them, an attack-compensation controller is constructed. Then, with the aid of Simpson's second rule to tackle the variable limit integral functions resulting from the memory signal and communication delay, several sufficient criteria are derived for assuring that the system is uniformly ultimately bounded with$H_{\infty }$performance. Lastly, the benefits of the proposed dynamic memory-event-triggered mechanism and attack-compensation controller are confirmed through some simulation results.

Event-driven fault detection of networked photo-voltaic microgrids usually requires accurate measured data. In real engineering environment, stochastic fluctuating measurements caused by system disturbances, measurement noises and outliers are inevitable. However, existing event-driven mechanisms using single measured data are sensitive to such stochastic fluctuations, which could lead to redundant events induced by disturbances and noises, and false events and fault alarms resulted from measurement outliers, respectively. In order to solve this problem, a novel bandwidth-aware and anti-outlier fusion-event-driven mechanism is proposed for the first time. First, to cut down the redundant events, the fusion of historical measurements is utilized to reduce the stochastic fluctuations and smooth the measurements. Second, an extra upper threshold constraint is introduced to exclude the false events and fault alarms. In addition, a dynamic triggering threshold relying on the bandwidth status and the fused measurements is constructed to improve the mechanism’s flexibility to complex and time-varying network situation. Then, the closed-loop event-triggeredH∞fault detection system with transmission delays is modeled as a time-varying delay system. With the help of Simpson rule, some sufficient conditions formulated by linear matrix inequalities are derived for co-designing the residual filter gain of fault detector and the triggering matrix. Finally, some simulation outcomes are presented for demonstration of the theoretical claims.

This paper studies the outlier-removal memory-event-triggered proportional-integral state estimation for variable-speed wind turbine systems subject to multimodal deception attacks. An innovative outlier-removal memory-event-triggered mechanism using historic data and comparison error upper bound is proposed to enhance the utilization of the limited network resource. The first merit of this mechanism is that the average value of some historical data is employed to smooth the measured outputs with stochastic fluctuations, which is able to reduce the unnecessary triggering events caused by the fluctuations. The second one is that the triggering error term’s upper bound is introduced to avoid the false transmissions induced by measurement outliers. A multimodal deception attack model with different occurrence probabilities for different modes is considered, which is more general and practical than the existing single-modal model. In order to estimate the system state, a fuzzy proportional-integral estimator is presented. With the introduction of an extra integral term, the presented estimator has the potential to improve the estimation performance compared to the traditional proportional estimator without the integral information. Then, the closed-loop estimating error system for variable-speed wind turbine is established as a T-S fuzzy system. Resorting to the Lyapunov theory and linear matrix inequality technique, a set of sufficient conditions for co-solving the estimator gains and triggering matrices are produced. Ultimately, the effectiveness of the developed method is verified by some simulation results.

This article proposes a novel even-triggered mechanism (ETM) to improve load frequency control (LFC) in power systems with electric vehicle (EV) integration, particularly when faced with bandwidth-constrained network communication. To mitigate the transmission of redundant packets that are typically found in conventional ETMs, a variable probabilistic release (VPR) scheme is introduced. The foundation of this VPR-based ETM rests on two crucial steps: 1) Construction of an Event Generator With Variable Probability: This generator facilitates the selection of actual released packets (ARPs) by using the VPR scheme from a group of triggered packets. Leveraging an algorithm, the probability of transmitting each triggered packet in the subsequent group is recomputed, enabling a more adaptive response to system dynamics. 2) Setting a Buffer With Delay Effect: A buffer is utilized to delay the release of ARPs until the final triggered instant in a group. The design not only simplifies timing division but also enhances system stability within fixed time intervals. Furthermore, this work formulates sufficient conditions that ensure the mean-square asymptotic stability (MSAS) of power systems. An illustrative example is presented to confirm the superiority of the proposed VPR-based ETM through comparative analysis with traditional ETMs.

This note proposes a novel weighted memory$H_\infty$controller for time-varying delayed Takagi–Sugeno fuzzy systems via a distributed delay method. In contrast to the traditional memory controller based on instantaneous historical data, the continuous historical information over a given period is utilized to construct the fuzzy-based memory controller, in which a weighting function is employed for the first time to describe the importance of historical information. Then, the weighted historical information is expressed as a distributed delay and the weights are represented as delay kernel. By using a less conservative integral inequality with respect to the weighted historical information, original sufficient conditions are deduced to solve the fuzzy weighted memory$H_\infty$controller gains. Finally, simulations are carried out to reveal the merits of the presented controller.

This paper investigates the fusion-based event-triggered$H_\infty$state estimation of autonomous surface vehicles (ASVs) against measurement outliers and cyber-attacks. To release communication burden, a novel fusion-based event-triggered mechanism (FETM) dependent on the fusion of historical system outputs is proposed. There exist two advantages of this mechanism: 1) the use of fusion signal is able to avoid the information loss between two sampling instants and reduce the redundant triggering events resulted from system disturbances and noises; 2) by requiring the error signal in the triggering condition not only lager than a lower threshold but also less than an upper threshold, the false triggering events incurred by measurement outliers also can be discarded. Then, a time-varying delay system is established to represent the event-triggered$H_\infty$state estimation error system with network-induced delays. Then, sufficient conditions are deduced for solving$H_{\infty}$estimator gain and triggering matrix of FETM. Lastly, some simulation results are given to illustrate the merits of the theoretical method.

In this study, the event-triggered problem of networked control systems (NCSs) is investigated, and a novel information transmission scheme is established. Under this scheme, the segment-weighted information (SWI) in a sliding historical window (SHW) is calculated and then sampled. Compared with the traditional direct sampling method, in this approach, the control input includes historical information in the SHW, thereby leading to less information loss due to sampling. This study also emphasizes on designing an SWI-based event-triggered mechanism (ETM) for scheduling network transmission. Different from most of the existing ETMs, the proposed SWI-based ETM leverages historical information to determine which data are necessary for the whole control system. Our approach can greatly reduce the number of unexpected triggering events of a control system with stochastic disturbances owing to the introduction of the SWI in the ETM. Moreover, Zeno phenomena are prevented thanks to periodic sampling. Sufficient conditions are derived based on the Lyapunov functional approach, and a numerical simulation example is provided to demonstrate the effectiveness of the proposed method.

This study is devoted to event-triggered fuzzy load frequency control (LFC) for wind power systems (WPSs) with measurement outliers and transmission delays. Due to the integration of wind turbine (WT) with nonlinearity, the T–S fuzzy model of WPS is established for stability analysis and controller design. To mitigate the network burden, a new sampled memory-event-triggered mechanism (SMETM) related to historical system information is presented. It has the following two merits: 1) the utilization of continuous memory outputs over a given interval is useful to reduce the information loss in the period of samples and the redundant triggering events induced by disturbances and noises and 2) an extra upper constraint is added in the triggering condition to generate a new event only when the error signal belongs to a bounded range, thus, the false events caused by measurement outliers can be differentiated out and then be dropped. By representing the memory signal with transmission delay as a time-varying distributed delay term, a T–S fuzzy time-varying distributed delay system is built up to model the$H_{\infty}$LFC WPS. With the help of the Lyapunov method and the integral inequality relying on distributed delay, some criteria are derived to solve the triggering matrix and fuzzy controllers. Finally, the merits of the proposed SMETM are tested by simulation results.

In this article, we study the design of fuzzy synchronization controller of Takagi–Sugeno (T–S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay. Compared with the existing model of neural networks with distributed time-varying delay without kernel, a more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. In this model, the delay probability density function is treated as the distributed delay kernel. By developing a new Lyapunov–Krasovskii functional related to the distributed delay kernels and using an integral inequality, new sufficient conditions for the existence of a synchronization controller are presented. Finally, a numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.

In this article, a distributed-delay-dependent method is proposed to deal with the stabilization for interval type-2 Takagi–Sugeno fuzzy systems subject to stochastic network delay and actuator saturation. To deal with the stochastic feature of network-induced delays, their probability density function is utilized to establish the distributed delay model, which is more practical than the traditional time-varying network delay model. Meanwhile, in order to enlarge the estimation of the domain of attraction, a polytopic strategy composed of a state vector and a distributed-delay-dependent vector is proposed to treat the saturation nonlinearity. By constructing a distributed-delay-dependent Lyapunov–Krasovskii functional, new and less conservative conditions are achieved to guarantee the stability of the established closed-loop system. Additionally, two simulation examples are carried out to illustrate the advantages of the proposed strategy.

This article studies the weighted memory-event-triggered$H_{\infty}$static output control issue of Takagi–Sugeno fuzzy wind turbine systems with uncertainty. To decrease the frequency of data communication, a novel adaptive memory-event-triggered mechanism is presented to choose the “necessary” control signals, which has the following two benefits. First, a weighted average signal over a historic period is utilized as the input of event-triggered scheme, instead of the current system information in the conventional one. This could reduce the control signal updating rate and avoid the false triggering events incurred by stochastic environment noises and disturbances. Second, a dynamic triggering threshold is adopted to adaptively regulate the control signal updating frequency along with the average signal. By applying the distributed delay system method to describe the weighted historic signal, a new uncertain T–S fuzzy wind turbine system with distributed delay is established. With the aid of the measured outputs and the integral inequality based on the weighting function of the average signal, the memory-event-triggered static output controller design conditions are obtained to ensure the system exponential stability and the$H_{\infty}$performance. Lastly, an experiment platform integrating Zigbee modules as the wireless network is set up to illustrate the advantages of the proposed strategy.

This article investigates the problem of memory-event-triggered H∞ output feedback control for neural networks with mixed delays (discrete and distributed delays). The probability density of the communication delay among neurons is modeled as the kernel of the distributed delay. To reduce network communication burden, a novel memory-event-triggered scheme (METS) using the historical system output is introduced to choose which data should be sent to the controller. Based on a constructed Lyapunov-Krasovskii functional (LKF) with the distributed delay kernel and a generalized integral inequality, new sufficient conditions are formed by linear matrix inequalities (LMIs) for designing an event-triggered H∞ controller. Finally, experiments based on a computer and a real wireless network are executed to confirm the validity of the developed method.

This article investigates the problem of resilient control for the Takagi–Sugeno (T–S) fuzzy systems against bounded cyberattack. A novel memory-based event triggering mechanism (ETM) is developed, by which the past information of the physical process through the window function is utilized. Using such an ETM cannot only lead to a lower data-releasing rate but also reduce the occurrence of wrong triggering event. Furthermore, the frequency of event generation is relatively smoother than existing ETMs. From the current releasing instant to the next, two periods are designed. The ETM works only when the first period ends, thereby avoiding the Zeno behavior that commonly exists in continuous ETM designs. The control system is then formulated as a switched fuzzy control system with two modes in each releasing period. Based on an assumption of secure control, and the proposed ETM, sufficient conditions are obtained to guarantee the exponential stability of networked T–S fuzzy systems in the presence of deception attacks in secure sense. Finally, a single-link rigid robot is taken as an example to illustrate the advantages of theoretical results.

This article considers an event-triggered H∞synchronization of neural networks (NNs) with mixed (discrete and distributed) delays. To release the communication burden, a novel weighted integral event-triggered scheme (IETS) is proposed based on the past information of the system dynamics. In this scheme, for the first time, a weighting function is proposed to weight the system state over a given period, which can be viewed as a forgetting factor. Moreover, a waiting time interval is added in the proposed IETS to exclude Zeno phenomenon. By constructing a novel Lyapunov-Krasovskii functional with the distributed delay kernel and the weighting function, sufficient linear matrix inequality conditions for the existence of an event-triggered controller that guarantees an exponential synchronization of the delayed NNs with an H∞performance are derived. Finally, an illustrative example and an application to image encryption are used to demonstrate the advantage of the proposed approach.

This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are used. With the proposed IETS, the integral event-triggered synchronization problem becomes a distributed delay problem. Using the Bessel-Legendre inequalities, sufficient conditions for the existence of a controller that ensures asymptotic synchronization are provided in the form of linear matrix inequalities (LMIs). Illustrative examples are used to demonstrate the advantages of the proposed IETS method over other event-triggered scheme (ETS) methods. Moreover, this IETS method is applied to the image encryption and decryption. A novel encryption algorithm is proposed to enhance the quality of the encryption process.

This paper presents the event-triggered control for Takagi-Sugeno (T-S) fuzzy networked systems with transmission delay. An integral-based model is proposed for designing a new event-triggered scheme, which relies on the mean of the system state and the last triggered state. To handle the asynchronous premises of the fuzzy system and fuzzy controller, a novel triggering condition is added into the event-triggered mechanism. Then, the closed-loop T-S fuzzy event-triggered control system is established as a distributed delay system. With the help of the Legendre polynomials and their properties, the co-design conditions of triggering parameters and controller gains are given in linear matrix inequalities to ensure the asymptotic stability of the resulting closed-loop system. Finally, an experiment via a practical wireless network is implemented to illustrate the effectiveness of the proposed approach.

This paper concerns the finite‐time H ∞ filtering of discrete Markov jump system with incomplete transition probabilities which cover the cases of known, uncertain and unknown. To include all possible cases, with the probability viewpoint, a truncated Gaussian distribution is employed to describe them. To ensure the filtering error systems to be finite‐time stochastic stable with a prescribed noise attenuation level, sufficient conditions for the H ∞ filter design are yielded in terms of solvability of a set of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed method.