This paper concerns the distributed set-membership fusion estimation (SMFE) problem of complex networks subject to communication constraints and unknown-but-bounded (UBB) noises, where nodes communicate with their neighbours based on a given topology. There are three main contributions: 1) an event-based coding-decoding mechanism (CDM) is designed in each communication channel to code the transmitted data with a finite bit rate, where the purpose is to save communication resources and enhance transmission security; 2) a fusion estimation method, which can fusion a group of local estimation sets, is introduced to estimate the system state with a trace-maximal ellipsoid, where the local estimators make full use of the information from itself as well as its neighbours. It helps to improve the accuracy and precision of the estimation effectively; 3) a genetic algorithm (GA) is firstly adopted to tackle the co-design issue of bit rate allocation protocol and estimator gain. It aims to reduce decoding errors while ensuring good estimation performance. To demonstrate the superiority of the above contributions, a numerical simulation is provided to validate the proposed SMFE method.Note to Practitioners—Complex networks can be applied to various practical problems, such as biological networks, neural networks, and social networks. The set-membership estimation (SME) method limits the state of the system to an ellipsoidal region. This paper studies the SMFE method, which fuses an ellipsoidal set containing the intersection of all local ellipsoidal sets to form a relatively small region containing the system state, thereby improving the estimation performance. Each communication channel equals with event-based CDM, which improves the robustness and security of data transmission. The bit rate allocation mechanism optimized by GA breaks the traditional uniform allocation scheme, which can reduce decoding error and improve estimation accuracy. Therefore, the method proposed in this paper can be used for navigation and positioning devices.

In this paper, event-triggered control synthesis of Interval Type-2 Takagi-Sugeno (IT2 T-S) fuzzy interconnected system is investigated via aperiodic intermittent method. Remarkably, time scale differential equation is introduced to construct a hybrid mode of discrete and continuous interconnections. An aperiodic intermittent control mindset is proposed inspired by the system energy attenuation characteristics. What’s more, a novel event-triggered mechanism embedding exponential decay function is developed to help reduce the sampling amount during the work cycle. By establishing time-scale type Lyapunov functions, sufficient criterion is obtained to guarantee stabilization of IT2 T-S fuzzy interconnected system with no Zeno behavior. Finally, some simulations on different time scales are given to verify the effectiveness of the proposed method.

Cyberattacks significantly compromise the security of sensor networks, which largely hinder the accuracy and privacy of the information interaction process. However, it is tough to identify the attacks with unknown statistical models by a traditional detection scheme. This article is concerned with the resilient set-membership estimation for 2-D systems over sensor networks subject to cyberattacks and unknown-but-bounded noises. First, using the dynamic watermarking (DW) technique, a novel attack detection method is proposed to guarantee the security of information transmission among sensors. Compared to existing results on attack detection, the developed mechanism is capable of adapting to the ellipsoid-dependent set-membership estimation framework that incorporates the set ideas. Second, a group of scalable resilient set-membership estimators are derived for large-scale sensor networks. Each sensor has access to an ellipsoidal estimation set containing the true state of the system, in which the proposed design procedure is independent of global information about the network topology. Third, a set of parameter optimization algorithms is introduced in a recursive manner, which allows the sensor network to obtain a satisfactory estimation performance utilizing the designed estimation gain and detection mechanism. Lastly, a simulation example is shown to evaluate the availability and superiority of the proposed technique.

This paper addresses the problem of event-based distributed set-membership estimation for complex networks with unknown but bounded (UBB) disturbances. To reflect the compromised data transmissions in cyber security, deception attacks are taken into consideration. Meanwhile, a novel estimation model is proposed against UBB disturbances. In order to schedule the signal transmissions between nodes and remote estimators, a novel decentralized dynamic periodic event-triggered mechanism (DPETM) with a time-varying threshold is developed for each node of the complex networks, which reduces the waste of communication resources and the complexity of computation. Thereafter, a series of distributed set-membership estimators are designed, whose parameters are explicitly determined in terms of the resolution of a particular linear matrix inequality (LMI) related to the information of the communication topology. An optimized ellipsoid estimation set is obtained by applying a recursive optimization algorithm. Finally, the simulation results are shown to demonstrate the viability of the proposed method.Note to Practitioners—This paper is motivated by set-membership state estimation problem of complex networks in practical missions, such as military, environment, industry, etc. The set-membership estimation of complex networks provides a reliable confidence region for each system node. Event-triggered control is an effective method for the design of set-membership estimator. But the common results require the systems to monitor the measurements point-to-point, which leads to the huge consumption of calculation and communication resources. For this reason, this paper originally extends the DPETM to the discrete-time version from the field of continuous-time systems. Meanwhile, this paper considers the deception attacks in communication channels, and the generic framework established earlier can tackle simultaneously sector-bounded nonlinearity, UBB disturbances, and deception attacks. The main difficulty of this paper lies in the analysis for the sawtooth constraint of periodic samplings. For this difficulty, we introduce a piecewise auxiliary function, which is similar with the loop-function in the field of continuous-time systems. Together with recursive optimization algorithm, the detailed analysis method is proposed for the reliable confidence regions of each set-membership estimator.

This study investigates the synchronization control of discrete-time complex dynamical networks (CDNs) under discontinuous network communication. A dual-event-driven control scheme is proposed based on periodic detection, which combines a centralized event-driven intermittent mechanism and a decentralized event-driven mechanism. The centralized event-driven intermittent mechanism is utilized to determine the working/rest time of controller, which realizes fine control of the working/rest intervals by responding quickly to system states. Furthermore, the decentralized event-driven mechanism is employed to decide whether the control signal is updated. Compared with existing results, this scheme eliminates the constraints and assumptions related to the interval length, avoids the successive measurements of signal, and reduces the frequency of controller updates. In addition, the piecewise Lyapunov function is constructed for flexible adjustment of the sampling period. Sufficient criteria for realizing discrete-time CDNs synchronization are derived under the proposed control scheme. Finally, several simulation examples are given to verify the validity of the obtained results.

This paper addresses the challenge of distributed adaptive platooning control of connected vehicles with randomly switching topologies. A linearized longitudinal vehicle platoon dynamic model is investigated, which simultaneously considers the bounded external disturbances and leader control input. By representing the switching topologies model in terms of a continuous-time Markov process, a distributed adaptive control scheme is developed such that follower vehicles can synchronize their velocities and accelerations with the leader while preserving the intended distance. The outstanding feature of the proposed distributed adaptive control scheme is that it avoids acquiring global information associated with communication topology. Besides, the design procedure is scalable since the control gain can be derived by only solving a linear matrix inequality offline related to the vehicle dynamics parameters, which means that the method remains applicable when the platoon size changes. Finally, the efficacy of theoretical findings is substantiated through the utilization of simulations.

This article explores the quasi-synchronization of discrete-time-delayed heterogeneous-coupled neural networks (CNNs) via hybrid impulsive control. By introducing an exponential decay function, two non-negative regions are introduced that are named time-triggering and event-triggering regions, respectively. The hybrid impulsive control is modeled by the dynamical location of Lyapunov functional in two regions. When the Lyapunov functional locates in the time-triggering region, the isolated neuron node releases impulses to corresponding nodes in a periodical manner. Whereas, when the trajectory locates in the event-triggering region, the event-triggered mechanism (ETM) is activated, and there are no impulses. Under the proposed hybrid impulsive control algorithm, sufficient conditions are derived for quasi-synchronization with a definite error convergence level. Compared with pure time-triggered impulsive control (TTIC), the proposed hybrid impulsive control method can effectively reduce the times of impulses and save communication resources on the premise of ensuring performance. Finally, an illustrative example is given to verify the validity of the proposed method.

This article investigates the event-triggered synchronization control problem of discrete-time neural networks (DNNs) in the case of periodic sampled-data. A discrete-time periodic event-triggered mechanism is adopted to evaluate the measurements, which avoids formulating the triggering function in a continuous manner and saves energy consumption. Under this framework, an event-triggered dynamic output-feedback controller is designed to achieve the goal of synchronization. A piecewise Lyapunov functional is constructed to analyze the sawtooth-like pattern of sampled-error signals. Thereafter, the synchronization criteria are formulated for the considered DNNs. The co-designed issue is further discussed for the control gains and triggering parameter. Finally, a simulation example is presented to show the effectiveness of the proposed method.

This article reports the synchronization control of discrete-time complex networks using an event-triggered method. The main contributions are twofold: 1) a discrete-time scenario of the dynamic periodic event-triggered mechanism is developed to schedule the transmissions of measurements. The proposed mechanism monitors the synchronization error in a periodic manner, which is beneficial to reduce the calculation resources of sensors. Simultaneously, the proposed mechanism increases the triggering threshold so that it contributes to enlarging the average interevent interval and 2) a new Lyapunov functional is developed to deal with the periodic samplings. On the one hand, the proposed functional involves a delay-dependent term, which is convenient to formulate the synchronization criterion by the delay analysis technique. On the other hand, the functional takes the sawtooth constraint of periodic samplings into consideration by introducing a piecewise functional. Finally, a succinct criterion is derived such that the considered networks are synchronized with a predetermined error level. A simulation example is provided to show our advantages in comparison with the existing approaches.

In this article, we investigate the periodic event-triggered synchronization of discrete-time complex dynamical networks (CDNs). First, a discrete-time version of periodic event-triggered mechanism (ETM) is proposed, under which the sensors sample the signals in a periodic manner. But whether the sampling signals are transmitted to controllers or not is determined by a predefined periodic ETM. Compared with the common ETMs in the field of discrete-time systems, the proposed method avoids monitoring the measurements point-to-point and enlarges the lower bound of the inter-event intervals. As a result, it is beneficial to save both the energy and communication resources. Second, the "discontinuous" Lyapunov functionals are constructed to deal with the sawtooth constraint of sampling signals. The functionals can be viewed as the discrete-time extension for those discontinuous ones in continuous-time fields. Third, sufficient conditions for the ultimately bounded synchronization are derived for the discrete-time CDNs with or without considering communication delays, respectively. A calculation method for simultaneously designing the triggering parameter and control gains is developed such that the estimation of error level is accurate as much as possible. Finally, the simulation examples are presented to show the effectiveness and improvements of the proposed method.

This article reports the synchronization of coupled neural networks (CNNs) by devising an event-dependent intermittent pinning controller. In this article, the Lyapunov–Krasovskii functional (LKF) is taken as an important element of the controller. Different from the preset technique in common intermittent control, the work/rest time is governed by an event-dependent intermittent mechanism which is described by the LKF and three partitions of non-negative real region. More specifically, the pinning controller is imposed on the CNNs only when the trajectory of LKF runs into the presupposed working regions. Under the proposed framework of intermittent control, a simple sufficient condition is formulated to guarantee the synchronization of CNNs. A numerical example is finally provided to demonstrate the validity of the theoretical results.

This article visits the intermittent quasisynchronization control of delayed discrete-time neural networks (DNNs). First, an event-dependent intermittent mechanism is originally designed, which is described by the Lyapunov function and three non-negative real regions. The distinctive feature is that the controller starts to work only when the trajectory of the Lyapunov function goes into the presupposed work region. The proposed method fundamentally changes the principle of the existing intermittent control schemes. Under the proposed framework of the intermittent mechanism, the work/rest time of the controller is aperiodic, unpredictable, and initial value dependent. Second, several succinct sufficient conditions in terms of linear matrix inequalities are developed to achieve the quasisynchronization of the considered DNNs. A simple optimization algorithm is established to compute the control gains and the Lyapunov matrices such that synchronization error is stabilized to the smallest convergence region. Finally, two simulation examples are provided to demonstrate the feasibility of the designed intermittent mechanism.

This paper aims at investigating the master-slave quasi-synchronization of delayed memristive neural networks (MNNs) by proposing a region-partitioning-dependent intermittent control. The proposed method is described by three partitions of non-negative real region and an auxiliary positive definite function. Whether the control input is imposed on the slave system or not is decided by the dynamical relationships among the three subregions and the auxiliary function. From these ingredients, several succinct criteria with the associated co-design procedure are presented such that the synchronization error converges to a predetermined level. The proposed intermittent control scheme is also applied to the event-triggered control, and an intermittent event-triggered mechanism is devised to investigate the quasi-synchronization of MNNs correspondingly. Such mechanism eliminates the events in rest time, and then it reduces the amount of samplings. Finally, two illustrative examples are presented to verify the effectiveness of our theoretical results.

In this paper, the dissipativity problem of discrete-time memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to reflect the memristor-based switched property of connection weights, and the DMNNs are then recast into a tractable model. Based on the tractable model, the robust analysis method and Refined Jensen-based inequalities are applied to establish some sufficient conditions that ensure the of DMNNs. Two numerical examples are presented to illustrate the effectiveness of the obtained results.

This paper investigates the event-triggered stabilization of neural networks (NNs) subject to input saturation. The main core lies in the design of a novel controller with time-varying switching gains and the associated switching event-triggered condition (ETC). The ETC is essentially a switching between the aperiodic sampling and continuous event trigger. The control gains of the designed controller are composed of an exponentially decaying term and two gain matrices. The two gain matrices are required to be switched when the switching between the aperiodic sampling and continuous event trigger is met. By employing the generalized sector condition and switching Lyapunov function, several sufficient conditions that ensure the local exponential stability of the NNs are formulated in terms of linear matrix inequalities (LMIs). Both the exponentially decaying term and switching gains improve the feasible region of LMIs, and then they are helpful to enlarge the set of admissible initial conditions, the threshold in ETC, and the average waiting time. Together with several optimization problems, two numerical examples are employed to validate the effectiveness of our results.

This paper is concerned with the exponential stabilization of memristive neural networks (MNNs) by taking into account the sampled-data control and actuator saturation. On the one hand, the MNNs are converted into a tractable model by defining a class of logical switched functions. Based on this model, the connection weights of MNNs are dealt with by a robust analysis method. On the other hand, a saturating sampled-data controller containing an exponentially decaying term is designed. With the help of generalized sector condition and the Lyapunov stability theory, a novel sufficient condition ensuring the local exponential stability of the closed-loop systems is formulated in terms of linear matrix inequalities. In addition, three optimization problems are given to design the control gain with the aims of enlarging the sampling interval, expanding the estimation of the domain of attraction, and minimizing the size of actuators, while preserving the stability of the closed-loop systems. Two numerical examples are provided to illustrate the effectiveness of the obtained theoretical results.

This brief is concerned with the stability criteria for recurrent neural networks with time-varying delay. First, based on convex combination technique, a delay interval with fixed terminals is changed into the one with flexible terminals, which is called flexible terminal method (FTM). Second, based on the FTM, a novel Lyapunov-Krasovskii functional is constructed, in which the integral interval associated with delayed variables is not fixed. Thus, the FTM can achieve the same effect as that of delay-partitioning method, while their implementary ways are different. Guided by FTM, Wirtinger-based integral inequality and free-weight matrix method are employed to develop several stability criteria, respectively. Finally, the feasibility and the effectiveness of the proposed results are tested by two numerical examples.

This paper is concerned with the exponential stability and stabilization of memristive neural networks (MNNs) with delays. First, we present some generalized double-integral inequalities, which include some existing inequalities as their special cases. Second, combining with quadratic convex combination method, these double-integral inequalities are employed to formulate a delay-dependent stability condition for MNNs with delays. Third, a state-dependent switching control law is obtained for MNNs with delays based on the proposed stability conditions. The desired feedback gain matrices are accomplished by solving a set of linear matrix inequalities. Finally, the feasibility and effectiveness of the proposed results are tested by two numerical examples.

In this paper, we study the global exponential stability in Lagrange sense for memristor-based neural networks (MBNNs) with time-varying delays. Based on the nonsmooth analysis and differential inclusion theory, matrix measure technique is employed to establish some succinct criteria which ensure the Lagrange stability of the considered memristive model. In addition, the new proposed criteria are very easy to verify, and they also enrich and improve the earlier publications. Finally, two example are given to demonstrate the validity of the results.