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 paper investigates the problem of resilient control for the T-S fuzzy systems against bounded cyber-attack. 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 can not 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 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.

This article presents the design of an event-trigger-based networked robust dissipative tracking control systems. An event-triggered mechanism (ETM) is proposed by introducing the probability density distribution of communication delay. Thus, it can yield less conservative results. The tracking error system is transferred by a distributed communication delay system wherein the delay probability density is viewed as the kernel of the distributed delay. The threshold of the proposed ETM is designed as a dynamic parameter to adapt the transmission of the control signal. By applying the Lyapunov method, sufficient conditions are achieved to ensure the stability of the tracking error system with strictly dissipative performance, which includes $H_{\infty }$ performance and passive performance. Based on the dissipativity analysis condition, a co-design method with both tracking control and the ETM is derived in a united framework. Experiments on networked dc motor tracking system are provided to show the effectiveness of the presented method.

This article studies 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 (LKF) 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.