This paper is concerned with the asynchronous sampled-data state estimation problems for a class of continuous-time nonlinear complex networks. A novel asynchronous sampled-data estimator is constructed with matrix exponential gains to estimate the states of network nodes, which allows each node to independently sample and transmit the measured signals at its own designated time instants. It is demonstrated that the utilization of matrix exponential gains is capable of enlarging the maximum-allowable bound of the sampling intervals. Moreover, a modified Halanay-type inequality is derived to facilitate the analysis of estimation errors. Accordingly, by leveraging the Lyapunov stability theory, some sufficient conditions are obtained to guarantee the global exponential stability of the estimation error dynamics. In addition, the maximum-allowable bound of the sampling intervals is explicitly characterized by resorting to an algebraic inequality, and a convex optimization method is adopted with the aim of maximizing such an allowable bound. Finally, some numerical simulations are conducted to validate the feasibility and usefulness of the established theoretical results.

Maintaining a stable formation among nonholonomic unicycles presents a significant challenge, particularly when subjected to real-world disturbances such as wheel slipping and motor noise. To tackle this issue, we propose a conditional disturbance utilization (CDU) framework that selectively uses favorable disturbances rather than simply rejecting all. First, the disturbance observer (DO) is employed to establish the informational foundation for subsequent conditional utilization decisions. And, the CDU mechanism dynamically decides whether the disturbance aligns with the desired control objective. Moreover, incorporating bearing rigidity achieves global stability with fast convergence. Specifically, the proposed potential function, constructed from bearing information, mitigates disturbance driven collision risks and ensures safety. Finally, simulations and experiments validate the proposed CDU-based formation control. The results demonstrate substantial improvements in responsiveness, and efficiency when compared to disturbance suppression method, highlighting the superiority of the proposed framework.

In this article, we propose a continuous-time multidimensional opinion dynamics model over a social network, incorporating heterogeneous logic matrices that capture interdependencies among multiple topics. The social network comprises an influence network with cooperative and antagonistic relationships and a family of logic networks, where the influence network reflects the interpersonal interactions among individuals. In the continuous-time setting, we show that the connectivity of the social network is fully determined by the connectivity of the influence and logic networks. By employing the structural balance theory, we establish a modulus consensus of the opinion dynamics on strongly connected networks. Moreover, if the influence network contains a directed spanning tree or the logic matrices are reducible, we derive sufficient conditions for exponential convergence of the proposed model. Simulation results are provided to demonstrate and validate the analytical conclusions.

In this article, the encoding-decoding-based state estimation problem is investigated for a class of continuous-time nonlinear complex networks (CNs) subject to communication bandwidth constraints. Based on the sampled outputs from a subset of network nodes, a novel dynamic event-driven encoding mechanism is integrated into the design of state estimator, where a time-varying auxiliary parameter is utilized to modulate the triggering condition in a dynamical fashion, enabling the event detector to decide whether the data packet should be released at the periodic sampling instants. Specifically, when the dynamic triggering condition is satisfied, the data are first encoded into a codeword and subsequently transmitted to the estimator through a digital communication channel. The Zeno behavior can be naturally prevented due to the periodic feature of the proposed event detector. By leveraging the Lyapunov theory and the matrix inequality techniques, sufficient conditions are established to ensure the exponential stability of the estimation error system. In addition, a convex optimization approach is employed to design the estimator gain with the goal of maximizing the allowable bound of the sampling intervals. Finally, an illustrative example and a practical example involving a three-area power system are provided to showcase the effectiveness of the proposed state estimation method.

Different from continuous-time or discrete-time (CoD-T) systems, time scale-type systems (TTSs) can operate in a continuous-discrete mode, bridging the gap between continuous and discrete states. As a result, conventional methods such as the average impulsive interval (AII) and the impulsive density function (IDF) are not suitable for characterizing the frequency of impulses (FoI) required for stability analysis of TTSs with impulses. This is due to the possibility that all impulses may fall into the complementary set of time scales. In light of this, the number of impulses satisfying AII or IDF may not be sufficient to ensure stability. In response to this challenge, we introduce a novel definition in this paper, termed time scale-type impulsive density (TTID). The key feature of TTID lies in the consideration of the graininess function, which is determined by time scales, allowing for a more accurate characterization of the number of impulses required for the stability of the TTSs. Employing the proposed TTID, the criteria of asymptotical stability are presented for TTSs with stochastic and Markov-type impulses, respectively. The results presented in this paper encompass and generalize the discoveries of some existing CoD-T impulsive systems, which can be regarded as special cases of our results. We shed new light on the advantages of the TTID and the effectiveness of the theoretical results by an application of voltage control for micro-grids.

This article tackles the sampled-data control issue for a class of time-scale-type systems (TSTSs) subject to denial-of-service (DoS) attacks. A novel sampled-data control protocol, that incorporate the backward-jump-like operator (BJLO), is proposed to ensure compatibility with the discontinuity of time scales. Furthermore, a generalized Halanay-like inequality (GHLI) is proposed to address the effects of time scale discontinuities and DoS attacks on sampling intervals. Compared with the common Halanay inequality (CHI) used in continuous-time sampled-data systems, the GHLI accommodates TSTSs and permits some sampling intervals that exceed the constraints of the CHI. By leveraging the GHLI and the proposed sampled-data control protocol, the exponential stability criterion is derived for TSTSs under DoS attacks. This article culminates with two simulation examples and the micro-grid case study conducted to validate the proposed results.

This article investigates the almost sure consensus (ASC) problem for sampled-data multiagent systems (MASs) operating over semi-Markov switching networks (SMSNs) and facing different types of denial-of-service (DoS) attacks. During real-time information exchange among agents, communication failures between agents occur randomly, which may result in each possible network topology occurring with a certain probability, and its sojourn time is also stochastic. This necessitates the consideration of a more general switching signal to describe the stochastic switching phenomenon of networks. In pursuit of this goal, a semi-Markov chain is introduced to characterize the switching signal of stochastic interaction networks, whose sojourn time distribution allows for arbitrary continuous-time distribution and depends on the current and next state. Additionally, this article delves into the impact of two distinct types of DoS attacks on MASs. The first type involves random DoS attacks, which are also modeled by a semi-Markov chain to capture the stochastic nature of attack durations. The second type is deterministic DoS attacks, characterized by their frequency and duration. The proposed new stochastic analysis method, based on the law of large numbers, is used to analyze the ASC for MASs featuring SMSNs under the DoS attacks. The effectiveness of the proposed approach is demonstrated by evaluating the results obtained from two illustrative numerical examples.

Due to the capability of Heterogeneous Information Network (HIN) in modeling different types of entities and relations, HIN based recommendation is proposed to enhance performance. In existing methods, meta-paths are extensively used to explore the semantic information in HINs. However, these methods mainly consider some nodes in instances of meta-paths, which may result in information loss and reduce recommendation performance. In the paper, we propose a Pre-training encoder based on Meta-Path for Recommendation (PMPRec) to learn rich and meaningful information between users and items. Specifically, in the pre-training stage, we design a Meta-Path based encoder (MP-encoder) to properly capture both structural and semantic information between users and items by considering all nodes and edges in instances of meta-paths including their types and positions, leading to effective embeddings. In the fine-tuning stage, we utilize a Multi-Layer Perceptron (MLP) to further optimize the embeddings according to user-item ratings. Experiments on four real-world datasets show the proposed PMPRec model outperforms the baseline models.

This article addresses the distributed cooperative protocol for nonlinear agents with the aim of attaining the optimal leader-following consensus. The main challenges encountered when deriving the cooperative optimal protocol are caused due to the conservatism of Lipschitz nonlinear dynamics, uncertainties, disturbances, and coupling of agents. Until now, the consensus optimal protocols for Lipschitz nonlinear agents are provided in the existing literature, which is conservative and has limited applications. To tackle these issues, a cooperative protocol is provided for intelligent nonlinear systems that are optimal for the performance index and robust against uncertainties and disturbances. The performance index of the optimal protocol depends on the Lipschitz nonlinearities is converted into linear parameter-varying (LPV) form. The LPV approach reduces the conservatism of the existing methods for Lipschitz nonlinearities that improves the scope of the design approach and is applicable to practical applications of nonlinear multiagents. The optimal solution is obtained by solving the algebraic Riccati equation. The proposed optimal scheme increases the region of the feasibility of the Lipschitz constant by extracting precise information about nonlinearities. Moreover, a robust cooperative optimal protocol is designed that has ensured optimal consensus for the nonlinear agents and eliminated the negative effects of parameter uncertainties and disturbances. Finally, the results are verified through a simulation example of multiple nonlinear robotic manipulators.

In this article, inspired by the Halanay inequality, we study stability of sampled-data systems with packet losses by proposing a nonuniform sampling interval approach. First, a sampled-data controller with an exponential gain is put forward to reduce conservatism. We obtain the sufficient condition for linear sampled-data systems to be exponentially stable by extending the famous Halanay inequality to sampled-data systems. The obtained sufficient conditions indicate that the maximal-allowable bound of sampling intervals is determined by the constant terms in the Halanay inequality, and the decay rate is presented in the form of a Lambert function. Compared with some existing results on the stability of sampled-data systems by using the Gronwall-Bellman Lemma, the conservatism induced by the exponential term via the Gronwall-Bellman Lemma can be reduced to some extent. Considering the phenomenon of packet losses, a new lemma is further proposed to generalize the proposed Halanay-like inequality. The results derived by the new lemma permit that there exist some sampling intervals with the upper bound violating the desired condition of the Halanay-like inequality. This permits us to establish exponential stability in significant cases that do not satisfy the Halanay-like inequality needed in the previous results. Finally, the sampled-data local exponential stability is investigated for nonlinear systems with strong nonlinearity.

In this article, based on the classical Degroot and Friedkin–Johnsen model, two opinion dynamics models with antagonistic relationship and multiple interdependent topics are proposed. The proposed models aim to characterize the evolution of individual opinions when individuals in a signed social network are able to discuss multiple interdependent topics simultaneously. Considering that different individuals may have different perceptions of the same thing, a set of heterogeneous logical matrices is used to represent the logical interdependence between different topics. In Model I with time-varying topologies, both the structurally balanced and unbalanced network topologies are investigated. Some sufficient conditions for the modulus consensus about topics are obtained in our study. About Model II with the stubborn individuals, we rigorously concentrate on its convergence and stability for the structurally balanced and unbalanced social networks. And some conditions on the convergence and stability are obtained. All the obtained conditions depend on the network topology and logical matrices and fully show how the network topology and logical matrices influence the evolution of opinions. Finally, two examples are used to verify our results.

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

Under practical environments, some certain noises may arise from the communication of agents due to electromagnetic interference, sensor error, and other disturbances, which are usually modeled by additive noise in the relative position state between an agent and its neighbors. In this article, a formation control strategy is considered for this kind of relative position state with the additive noise, where the unmeasured velocity information is estimated by an observer-based strategy. On the other hand, the considered problem is transformed into a convergence problem of infinite products of a sequence of general stochastic matrices. The general stochastic matrix means that a matrix has row sum one but its entries do not necessarily require non-negative. This article develops the convex analysis to cope with such infinite products. Then, a sufficient condition is obtained to ensure that the specified formation shape in a noisy environment can be achieved. Subsequently, an example is presented to show the effectiveness of the result.

In this paper, the sampled-based consensus problem is investigated for a class of nonlinear multiagent systems subjected to deception attacks. Due to network fluctuations and limited resources, deception attacks might destroy the sampled-data in communication networks. Additionally, the success of deception attacks greatly depends on some randomly fluctuated factors. This paper takes into account the deception attacks that are randomly launched at each sampling instant. The eigenvector of Laplacian matrix is utilized to construct a novel Lyapunov functional. Then, the decoupled criterion connected with eigenvalues of Laplacian matrix is obtained such that the addressed multiagent systems achieve the mean square consensus. Furthermore, the solutions of a set of matrix inequalities represent the controller gain matrix. Finally, a numerical example is provided to validate the effectiveness of the derived results.

With the rapid development of biomedical technology, discovering causality from genes and human physiological and pathological characteristics has become a hot but challenge spot over the past decades. Due to the increment of the amount of biomedical data, discovering causality from observed data becomes more and more difficult to search this large body of knowledge in a meaningful manner. To address the issues in existing causality discovering models, we introduce a generative Bayesian causal network that combines neural network to explicitly characterize these unique causal-effect relationships as a variable number of nodes and links. Particularly, a basic skeleton is generated for node selection to reduce the network size by minimizing the maximum mean discrepancy among variables. In addition, a causal generative neural network model is presented to construct causal network with cause-effect scores between variables. Empirical evaluations on two publicly available biomedical datasets and four synthetic datasets suggest our approach significantly outperforms the state-of-the-art methods in discovering causal relationships among biomedical variables.

In this paper, the quasi-consensus problem is investigated for a class of heterogeneous-switched nonlinear multiagent systems, in which both cooperation and competition interactions are considered simultaneously. By means of the Lyapunov function method, we show that quasi-consensus can be ensured for switched multiagent systems under the assumption that the activation time of cooperation interactions is sufficiently large. Moreover, a new Lyapunov function is considered to provide the lower and upper bounds of switching intervals explicitly. Thus, these bounds can be used to obtain less conservative stability results of switched systems. Furthermore, the established results are specialized to both the traditional consensus case and the stability of linear-switched systems. Finally, simulations are given to illustrate the theoretical results derived in this paper.

This article solves the event-triggered exponential synchronization problem for a class of complex-valued memristive neural networks with time-varying delays. The drive-response complex-valued memristive neural networks are translated into two real-valued memristive neural networks through the method of separating the complex-valued memristive neural networks into real and imaginary parts. In order to reduce the information exchange frequency between the sensor and the controller, a novel event-triggered mechanism with the event-triggering functions is introduced in wireless communication networks. Some sufficient conditions are established to achieve the event-triggered exponential synchronization for drive-response complex-valued memristive neural networks with time-varying delays. In addition, to guarantee that the Zeno behavior cannot occur, a positive lower bound for the interevent times is explicitly derived. Finally, numerical simulations are provided to illustrate the effectiveness and superiority of the obtained theoretical results.

This paper is concerned with the consensus of multiple Euler-Lagrange systems with time-varying sampled-data control. Different from traditional sampled-data strategies, a time-varying sampled-data strategy is developed to realize the consensus of multiple Euler-lagrange systems, in which a function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler-Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.

H∞consensus is investigated for multi-agent systems with linear dynamics and impulsive effects in this paper. First of all, by considering the effects of distributed impulses, the model of linear multi-agent systems with impulsive effects and external disturbances has been obtained. Then, in view of the average impulsive interval and the Lyapunov stability theory, an algorithm has been given to solve the H∞impulsive consensus for the linear multi-agent system under consideration. The theoretical results are verified by an example in the final.

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.

In this paper, consensus of multi-agent systems containing linear self dynamics is investigated. By considering packet losses, a sampled data protocol with packet losses is considered. A switched systems including stable modes and unstable modes is considered here to describe packet losses. By using the contradiction method and the Lyapunov function method, a sufficient condition on consensus of the multi-agent system under consideration is obtained, where the controller can be solved in the form of convex optimization approaches. In addition, the maximum sampling interval is also given.

This paper is concerned with pth moment exponential stability of stochastic delayed neural networks. By using the Lyapunov function method, some stability criteria of impulsive stochastic delayed systems are obtained, and these results are applied to the study on the stability criteria of stochastic delayed neural networks. It is shown that if the continuous stochastic delayed neural network is stable and the impulsive effects are destabilizing, then the stochastic delayed neural network is exponentially stable with respect to a lower bound of the impulsive interval. Moreover, if the continuous stochastic delayed neural network is not stable, the impulsive effects can successfully stabilize the delayed neural network for a given upper bound of the impulsive interval. One example is presented to demonstrate the usefulness of the proposed results.

In this paper, the distributed exponential consensus of stochastic delayed multi-agent systems with nonlinear dynamics is investigated under asynchronous switching. The asynchronous switching considered here is to account for the time of identifying the active modes of multi-agent systems. After receipt of confirmation of mode's switching, the matched controller can be applied, which means that the switching time of the matched controller in each node usually lags behind that of system switching. In order to handle the coexistence of switched signals and stochastic disturbances, a comparison principle of stochastic switched delayed systems is first proved. By means of this extended comparison principle, several easy to verified conditions for the existence of an asynchronously switched distributed controller are derived such that stochastic delayed multi-agent systems with asynchronous switching and nonlinear dynamics can achieve global exponential consensus. Two examples are given to illustrate the effectiveness of the proposed method.

In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants. Hence, the impulses here can encompass several typical impulses in NNs. The aim of this paper is to derive stability criteria such that stochastic NNs with local impulsive effects are exponentially stable in mean square. By means of the mathematical induction method, several easy-to-check conditions are obtained to ensure the mean square stability of NNs. Three examples are given to show the effectiveness of the proposed stability criterion.

In this paper, the stochastic synchronization problem is studied for a class of delayed dynamical networks under delayed impulsive control. Different from the existing results on the synchronization of dynamical networks under impulsive control, impulsive input delays are considered in our model. By assuming that the impulsive intervals belong to a certain interval and using the mathematical induction method, several conditions are derived to guarantee that complex networks are exponentially synchronized in mean square. The derived conditions reveal that the frequency of impulsive occurrence, impulsive input delays, and stochastic perturbations can heavily affect the synchronization performance. A control algorithm is then presented for synchronizing stochastic dynamical networks with delayed synchronizing impulses. Finally, two examples are given to demonstrate the effectiveness of the proposed approach.

This paper investigates the synchronization problem of coupled switched neural networks (SNNs) with mode-dependent impulsive effects and time delays. The main feature of mode-dependent impulsive effects is that impulsive effects can exist not only at the instants coinciding with mode switching but also at the instants when there is no system switching. The impulses considered here include those that suppress synchronization or enhance synchronization. Based on switching analysis techniques and the comparison principle, the exponential synchronization criteria are derived for coupled delayed SNNs with mode-dependent impulsive effects. Finally, simulations are provided to illustrate the effectiveness of the results.

Previous works on network coding capacity for wireless networks have taken the assumption that the network is stationary. In this paper, the mobility of ad hoc networks is considered as a key factor influencing network capacity, and a new and unified analytical expression of the capacity of a mobile ad hoc network applying network coding is derived under the two main mobility models, including random waypoint and random walk. The simulation results show that under the mobility condition, the network capacity of mobile ad hoc networks applying network coding still exhibits a concentration behavior around the mean value of the minimum cut.