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 explores a T-S fuzzy-model-based approach for load frequency control in network-based power systems under random false data injection attacks, taking into account the integration of electric vehicles and photovoltaic power generation systems. Amidst uncertainties in parameters and nonlinear items of integrated power systems, a T-S fuzzy model is formulated, enabling convenient design and analysis of the rolling horizon optimal control (RHOC) strategy with variable control gains to ensure mean-square asymptotic stability in power systems. Considering the broad dispersion of new energy generation systems across the power grid, the use of network communication has become crucial. To overcome the limitation of network bandwidth while ensuring frequency stability, a novel event-triggered mechanism employing a varying probabilistic release strategy (VPRS) within grouped triggered data-packets is proposed for transmitting power frequency signals. Initially, a conventional event-triggered mechanism is established to generate primary triggering packets, stored in a buffer until designated packet capacity of the group is reached. Then, following the probabilistic updating algorithm in RHOC strategy, the transmission task is executed upon the arrival of the final triggered packet within each group. The efficacy of the proposed method is validated through a numerical example. Note to Practitioners—This paper introduces a T-S fuzzy-model-based RHOC strategy for power systems with renewable energy, addressing challenges posed by limited communication resources and random false data injection attacks. The innovative event-triggered mechanism with a VPRS optimizes network utilization for power systems, while the RHOC strategy with variable control gains guarantees mean-square asymptotic stability despite uncertainties and nonlinearities within the power network. While the efficacy of the approach is demonstrated through numerical examples, practitioners should take into account practical constraints, such as scalability issues and the complexities involved in real-world implementation. Future research will focus on enhancing scalability and addressing implementation challenges to facilitate broader adoption in power systems.

In this article, a reinforcement learning (RL)-based event-triggered guaranteed cost control (GCC) is developed for power systems with control input saturation (CIS). An event-triggered mechanism with a balance factor is developed to optimize both control performance of power systems and computation efficiency. To obtain an online solution to the corresponding modified Hamilton–Jacobi–Bellman equation for the power system, a critic neural network is employed. In contrast to RL-based methods employing dual-network setups, the single neural network not only conserves computational resources but also eliminates the need for an initial admissible control. For accelerating convergence toward optimal solutions, a new adaptive weight tuning law is constructed. Control performance of power systems with CIS and limited computation power is ensured by the proposed RL-based optimal GCC strategy. Simulation results validate the effectiveness of the proposed methodology.

This article studies the event-triggered formation control of multiple unmanned aerial vehicles (UAVs) in the presence of deception attacks. Unlike existing research focusing on deception attacks, the secure upper bound of deception attacks that the formation tracking of UAVs can tolerate is estimated to reduce the conservatism associated with the predefined upper bound of deception attacks. A dynamic event-triggered mechanism is developed by considering triggering and tracking errors to reduce data release rates while maintaining desired formation tracking performance. Leveraging information from neighboring UAVs, tracking control strategies for the multi-UAV system facing deception attacks are designed using the Lyapunov stability theory. Simulation analysis validates the effectiveness of the proposed strategies, demonstrating improved resilience in the presence of deception attacks.

This paper proposes an innovative event-triggered interval type-2 (IT-2) fuzzy consensus control method for agent-based full-vehicle suspension systems (FSSs) using a leader-following approach. By developing a novel IT-2 Takagi-Sugeno (T-S) fuzzy model, the amalgamation of heterogeneous agent-based FSSs into a cohesive homogeneous multi-agent framework becomes feasible, thereby reducing control complexity and effectively dealing with system uncertainty. Within the proposed agent-based architecture, a virtual leader is designed at the core of the agent-based FSS, and the four interconnected quarter-vehicle suspension systems are construed as the following agents. To optimize network bandwidth between the agents, a new event-triggered mechanism (ETM) is established, which is sensitive to significant state changes, particularly when deviations from consensus among the following agents arise abruptly. Sufficient conditions are derived to ensure both the optimal attitude performance and ride comfort of FSSs. Finally, a simulation example of agent-based FSSs is presented to validate the advantages of the proposed approach in optimizing ride comfort and system robustness.

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.

A novel adaptive constraint control approach is proposed for flexible riser systems characterized by uncertain parameters and external disturbances, aimed at achieving the desired transient performance. By combining Hamilton’s principle with partial differential equations (PDEs), the physical model is transformed into a dynamic model. Considering the boundary position constraint, a boundary controller is built using the tangent barrier Lyapunov function (BLF) to mitigate vibrations. In order to ensure the convergence of the boundary position error at a predetermined rate, the control approach incorporates a performance function to attain the necessary transient performance. An auxiliary term is defined to counteract the impact of coupling terms that remain during the decoupling process of PDEs. Finally, the above scheme is further validated through MATLAB simulations.

This article studies an event-based two-step transmission mechanism (TSTM) in the control design for networked T-S fuzzy systems. The transmission task is achieved in two steps. Consecutive triggering packets are relabeled in the first step by applying a traditional event-triggered mechanism (ETM). Then a probabilistic approach is employed to determine which packet is a real release packet (RRP) in the second step. This event-based TSTM is particularly suitable for scenarios in which traditional ETMs are unable to determine which packets are redundant. By discarding most of the unnecessary data packets, especially when the system is tending toward stability, the burden on the network bandwidth is reduced. To establish a control strategy for T-S fuzzy-based nonlinear systems with random uncertainties, a new timing analysis technique is proposed. Additionally, the necessary conditions for a nonlinear system's mean-square asymptotic stability (MSAS) are derived. Finally, two practical applications demonstrate the effectiveness of the suggested transmission mechanism in networked T-S fuzzy systems.

In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.

In this technical correspondence, the resilient fuzzy stabilization is enhanced in the direction of elevating the feasible stabilization region as large as possible while the same alert threshold is chosen as the recent one. To do this, the switching-type fuzzy state-feedback controller is designed with a set of switch modes so that more groups of gain matrices can be introduced to enhance the degree of freedom. What is far more important is that a novel augmented time-variant matrix approach is proposed in order to collect the proprietary features of normalized fuzzy weighting functions with regard to each switch mode. Then, all the obtained augmented time-variant matrices are split into a set of positive/negative matrices, which can be elaborately assigned into different monomials of our designing conditions under the framework of homogeneous polynomials. Therefore, less conservative results of resilient fuzzy stabilization are obtained even if some higher alert thresholds are chosen for probably ensuring the establishment of the involved precondition. Finally, the superiority of our approach is validated by giving some detailed comparisons on the benchmark example.

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 addresses the problem of adaptive memory-event-triggered control for intelligent vehicle transportation systems (IVTSs). Unlike the existing methods for modeling IVTSs, the acceleration information is introduced in the desired distance to achieve better distance control performance of IVTSs. The information between vehicles interacts over a wireless network. In order to further reduce the amount of data transmission, a novel adaptive memory-event-triggered mechanism (METM) is developed, in which historical information is utilized. In addition, the proposed METM threshold is designed to vary with the system state of each autonomous vehicle to adjust the data-releasing rate adaptively. Under the adaptive METM, a distributed tracking controller with historical information is put forward to guarantee uniformly ultimately bounded (UUB) stability using the Lyapunov stability theory and linear matrix inequality (LMI) technique. Finally, simulation results are given to verify the superiority of the proposed method.

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.

This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.

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.

In this article, a networked fault detection (FD) problem is investigated for interval type-2 T–S fuzzy systems. A novel adaptive memory-event-triggered mechanism (METM) is proposed by introducing historical information of the measured output in a prescribed sliding window. The current measured output in the traditional event-triggered mechanism is replaced by a weighting function-based historical information. As a result, the data releasing rate can be effectively reduced and maltriggering events aroused by unknown abrupt disturbance or measurement noise can be avoided as well. Meanwhile, an adaptive threshold depending on the historical information is utilized to further adjust the data releasing rate. The FD filter is designed and derived in terms of linear matrix inequalities to guarantee the$H_{\infty }$performance of fault detected systems. Finally, a hardware-in-loop simulation experiment platform is built to manifest the effectiveness of the proposed METM-based FD 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.

This article mainly focuses on the problem of resilient$H_{\infty }$filtering for cyber–physical systems (CPSs) subject to denial-of-service (DoS) attacks and sensor saturation. A new event-triggered mechanism (ETM) considering both DoS attacks and limited network bandwidth is put forward to guarantee the secure performance of the filter. Under this mechanism, inherent periodical transmission attempts are generated during DoS active periods, by which the latest measurement output of the system can be successfully transmitted to the filter after the end of the DoS attack; while during DoS sleep periods, the ETM degenerates into a traditional one. Furthermore, a valid DoS attack model is proposed to further characterize the following two scenarios occurring between adjacent sampling instants: 1) the end of the DoS attack and 2) both the start of the DoS attack and the end of the DoS attack. Sufficient conditions for designing the secure filters of CPSs against DoS attacks are achieved by using the piecewise Lyapunov–Krasovskii functional approach. Finally, the validity of our designed approach is manifested by an illustration of quarter-vehicle suspension systems (SSs).

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.

filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach is adopted before information from multisensors is transmitted over the network. A novel event-triggered mechanism is proposed, which allows us not only to reduce the data-releasing rate but also to prevent abnormal data being potentially transmitted over the network due to sensor measurement or other practical factors. The problem of denial-of-service (DoS) attacks, which often occurs in a communication network, is also considered, where the DoS attack model is based on an assumption that the periodic attack includes active periods and sleeping periods. By employing the idea of the switching model for filtering error systems to deal with DoS attacks, sufficient conditions are derived to guarantee that the filtering error system is exponentially stable. Simulation results are given to demonstrate the effectiveness of the theoretical analysis and design method.

This article investigates the problem of resilient secure control of cloud-aided 5-DOF active semivehicle suspension systems (SVSSs). A novel joint model considering both the event-triggered mechanism (ETM) and Denial of Service (DoS) attacks is established. Under such a model, during the active period of DoS attacks, periodic transmission attempt is made to ensure the control system can receive the control information at the earliest time; the ETM turns to be a conventional one when the DoS attack is in sleeping period. Meanwhile, the valid attack period is proposed to address the problem of the DoS attack ending within a sampling period, which is a challenging problem in modeling DoS attacks. Takagi–Sugeno (T–S) fuzzy model is used to characterize the uncertainties of sprung and unsprung mass of SVSSs. By converting the cloud-aided active SVSS into a fuzzy-based switched time-delay system, and using the method of piecewise Lyapunov function, sufficient conditions are derived to ensure the performances of active SVSSs subject to DoS attacks. Finally, the effectiveness of the proposed method is validated by a numerical example of active SVSSs subject to DoS attacks.

Resilient fuzzy stabilization is capable of providing much less conservative results than conventional fuzzy stabilization while the alert threshold condition should be always satisfied at each sampling instant. In order to make the alert threshold condition more easily to be guaranteed, the short paper employs the switching-type gain-scheduling control law so that the real-time information, which is specific to the current sampling instant, can be integrated into resilient fuzzy stabilization. More importantly, a new kind of time-variant balanced matrix is introduced for the first time for adjusting positive/negative terms of different monomials in a more flexible way. As a result, the conservatism of resilient fuzzy stabilization can be further reduced even if the alert threshold condition becomes more difficult to be violated. Finally, the advantage of the developed method is tested and validated via related comparisons on a benchmark example.

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 problem of event-triggered secure path tracking control of autonomous ground vehicles (AGVs) under deception attacks. To relieve the burden of the shareable vehicle communication network and to improve the tracking performance in the presence of deception attacks, a learning-based event-triggered mechanism (ETM) is proposed. Different from existing ETMs, the triggering threshold of the proposed mechanism can be dynamically adjusted with conditions of the latest vehicle state. Each vehicle in this study is deemed as an agent, under which a novel control strategy is developed for these autonomous agents with deception attacks. With the assistance of Lyapunov stability theory, sufficient conditions are obtained to guarantee the stability and stabilization of the overall system. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed theoretical results.

This article studies the security of networked interconnected systems (NISs) subject to cyber-attacks based on a new event-triggered mechanism (ETM). NISs with spatially distributed subsystems are vulnerable to cyber-attacks. With a new concept of security control, attention is focused on designing a novel ETM together with a decentralized output feedback control (DOFC) scheme such that the NIS subject to cyber-attacks is stable in secure sense. Under the proposed ETM, the average data-releasing rate over the whole operating period can be extremely decreased, thereby reducing the burden of network bandwidth, computation, and battery-supply. Moreover, during the system with external disturbance or attack on the communication network, more transmission-events can be generated than other periods. As a result, the desired control performance can be achieved. By using stochastic analysis techniques and Lyapunov stability theory, sufficient conditions are derived to obtain both the controller gains and the parameters of the ETM. Numerical simulation of chemical reactor systems is given to illustrate the advantages and effectiveness of the proposed theories and design techniques.

This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of "unnecessary" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.

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.

This study is concerned with the reliable filtering problem for the sampled‐data system subject to a class of probabilistic sensor signals distortion. A new distortion model is developed by introducing a diagonal random matrix whose elements obey the Gaussian distribution. The main purpose in this study is to design a filter such that the error dynamics of the filtering process subject to the probabilistic sensor signal distortion is mean‐square asymptotically stable. Based on the modified delay‐central‐point (DCP) method and the convexity property of the matrix inequality, new criteria are derived for the existence of the desired H ∞ filters, by which it leads to much less conservative analysis results. Simulation results are provided to illustrate the effectiveness of the proposed method.

The system studied in this paper has four main features: 1) It is a networked controlled system (NCS), and therefore, the signal transfer is subject to random delay and/or loss; 2) it is a nonlinear system approximated by a Takegi--Sugeno (T-S) fuzzy model; 3) its multisensors and multiactuators are subject to various possible faults/failures; and 4) there are uncertainties in the plant model parameters. A comprehensive model is first developed in this paper to cover these features for a class of NCS nonlinear systems. This model has removed some limitations of similar models in the published literature. Then, the Lyapunov functional and the linear matrix inequality (LMI) are applied to develop two new stability conditions (Theorems 1 and 2). These conditions and an algorithm are used to design a controller to achieve robust mean square stability of the system. Finally, two examples are used to demonstrate the application of the modeling and the controller design method developed.