Industrial Internet of Things (IIoT) systems are vulnerable to cyber-attacks due to their interconnected nature. While federated learning (FL) offers a privacy-preserving approach to intrusion detection, it struggles with data heterogeneity, class imbalance, and adversarial attacks in practical IIoT settings. To address these challenges, this paper introduces HGDS, a FL-based framework that integrates a federated diffusion-based sampler for class rebalancing with a dynamic hypernetwork grouping mechanism for model personalization. The proposed framework further incorporates robust defense strategies, including gradient clipping and anomaly-based client isolation, to mitigate adversarial attacks. Comprehensive evaluations are conducted on four real world datasets. The results demonstrate the superiority of HGDS, which achieves a performance gain of up to 15.3% in F1-score over state-of-the-art benchmarks under non-IID data conditions. Furthermore, the method maintains resilience against both model poisoning and label flipping attacks.

In this article, the practical fixed-time (FxT) control of switched neutral Filippov systems (SNFSs) on networks is considered. The perturbation functions that can be discontinuous, and the neutral logics expressed by the difference operators, are addressed by new approaches. Several novel Lyapunov inequalities with indefinite functions are proposed, where detailed estimations of the settling-time (ST) are obtained by discussing the different values of the exponents of the Lyapunov functions, which can include the existing results. Considering that the states generally cannot converge to the origin accurately under finite-time control in real applications, practical FxT stability lemmas with indefinite functions are established for the first time, in which the bounded condition imposed on the indefinite function is more practical than the previously unbounded ones. By designing the adaptive control strategies, the FxT and practical FxT synchronization control are investigated based on the Lyapunov-Krasovskii functionals (LKFs), which show the delay characteristic via the adaptive update law containing delay values. Notably, the theoretical deficiency arising from the Lyapunov function when studying the FxT stability of real systems with delays by using the FxT stability lemmas with indefinite function is solved in a successful way. Finally, the validity of the main results is verified by numerical simulations on an electrical device containing an LC transmission line.

This article addresses the problem of distributed output formation optimal tracking for heterogeneous multiagent systems (MASs). Unlike the main approach in most existing formation tracking studies, which relies on prespecified trajectories, this work aims to enable heterogeneous MASs to achieve desired formation while tracking the optimal reference trajectory generated by a distributed optimization algorithm. First, a distributed time-varying optimization algorithm is proposed as the distributed optimal reference trajectory generator, which accounts for inequality constraints and ensures fixed-time convergence in consensus and asymptotic convergence in optimality. Then, a distributed output formation optimal tracking control protocol is developed for heterogeneous MASs, with the integration of the distributed optimal reference trajectory generator. Subsequently, the convergence of the proposed distributed output formation tracking control algorithm-based on time-varying optimization-is rigorously proven using Lyapunov stability analysis. Finally, simulation examples are provided to validate the theoretical results.

This article focuses on the design and analysis of resilient leaderless consensus for multiagent systems exposed to distributed denial-of-service (DoS) attacks. Unlike most existing works that focus on undirected communication topologies and synchronized attacks affecting all links simultaneously, this work considers a more general scenario with directed communication graphs and distributed DoS attacks, where different communication channels can be disrupted independently. To address this challenge, a dynamic event-triggered communication scheme is incorporated into the consensus protocol, under which asymptotic consensus stability can be preserved despite distributed DoS disruptions. Information exchange is performed only at triggering instants, which effectively suppresses redundant transmissions and enhances communication efficiency. To further reduce computational burden and remove the requirement of continuous monitoring, a self-triggered communication scheme is introduced, in which each agent determines its next triggering instant solely based on the most recently available data. Rigorous analysis verifies that both the event-triggered and self-triggered schemes preclude Zeno behavior. In addition, numerical simulations of a low Earth orbit satellite formation demonstrate the efficacy and advantages of the developed control strategies.

This article focuses on resilient time-varying optimal tracking problems of output formation in open multiagent systems (MASs). Agents can join or exit at any time and may be subject to switching between normal and malicious identities. Normal agents in the open MAS aim to minimize the sum of their local time-varying composite objective functions, each consisting of an output-related term and a state-related nonsmooth term. Simultaneously, agents are required to maintain a given output formation configuration. Based on relative outputs from neighbors, a distributed tracking protocol is proposed, combining the subgradient method with proximal mapping and an adaptive aggregation technique. By analyzing the upper bounds of total dynamic regret and individual dynamic regrets, it is proved that resilient optimal tracking of output formation can be achieved without knowledge of agent identities. Simulations validate these results.

This article investigates the fault detection and isolation (FDI) problem for a class of re-entrant manufacturing systems (RMSs) subject to workstation faults, sensor faults, and measurement disturbances. The system dynamics are first characterized by a hybrid hyperbolic partial differential equation (HHPDE) continuum model. A two-stage observer-based FDI framework is then developed to enable timely and reliable fault diagnosis. In the first stage of this framework, a diagnostic observer equipped with residual evaluation logic is employed, which is capable of detecting the occurrence of faults yet incapable of differentiating between the sensor faults and the workstation faults. Once a fault is detected, the isolation procedure starts as the second stage to distinguish the fault types and localize the fault sources. To be specific, anH-/H∞observer-based isolation scheme is proposed to effectively decouple the sensor faults from the sensor disturbances, by exploiting the dual performance of disturbance attenuation and fault sensitivity; an adaptive observer-based isolation strategy is devised to identify the workstation faults by capturing the associated structural changes in the system dynamics. Finally, the effectiveness and robustness of the proposed methods are validated through comprehensive numerical simulations.

Local feature description is crucial for robotic tasks, yet existing methods struggle with motion blur, a prevalent challenge in high-dynamic and low-light environments. While effective on sharp images, they suffer significant degradation under blur. To address this issue, we propose Motion-Feat, an end-to-end motion blur-aware feature description method. Our approach introduces a Motion Deformable Block (MDB) that adaptively adjusts the receptive field based on pixel-wise motion information at different stages of the network, enhancing multi-scale feature descriptor robustness in blurred conditions. Additionally, we construct synthetic blurred datasets to systematically benchmark feature matching performance across varying blur intensities. Extensive experiments demonstrate that Motion-Feat outperforms state-of-the-art methods on blurred images while maintaining competitive performance on sharp images for relative camera pose estimation and homography estimation tasks. Both code and datasets are available at https://github.com/AndreGao08/Motion-Feat.

In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision– making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision–making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting adaptability in dynamic environments. Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers. This method effectively maps commands to task allocation and actions to path planning, while leveraging cross– training techniques to enhance learning across the hierarchical framework. Furthermore, we introduce a trajectory prediction model that bridges abstract task representations with actionable planning goals. In our experiments, it achieves over 80% in confrontation win rate and under 0.01 seconds in decision time, outperforming existing approaches. Demonstrations through large–scale tests and real–world robot experiments further emphasize the generalization capabilities and practical applicability of our method.

This paper presents a distributed observer-based non-linear control framework to address a prescribed-time output formation tracking problem of heterogeneous nonlinear networks under a nonlinear leader system and a digraph with a spanning tree, wherein dynamics and dimensions of leader-follower systems can be different. To solve this issue, prescribed-time nonlinear distributed observers are firstly developed to estimate the nonlinear leader’s state, and the prescribed-time distributed control law is further presented. Under this framework, the zero-error output formation tracking is obtained within a pre-specified and user-defined time by leveraging the time transformation approach. The proposed algorithm is free of global knowledge, and its convergence time is irrelevant to initial conditions, control parameters or network structures. Simulation and experimental results are given to verify the designs’ effectiveness. The main features of developed designs lie in that: (1) prescribed-time output formation tracking of heterogeneous nonlinear systems can be achieved under directed graphs, wherein both dynamics and dimensions of leader-follower nonlinear systems can be different; (2) the convergence time is pre-specified and thus, irrelevant to any initial conditions, control parameters, or network structures; (3) the proposed algorithm that does not depend on any global information, is distributed under a general communication graph with a directed spanning tree; and (4) the prescribed-time output formation tracking solution can be extended beyond the terminal time.

This article addresses a prescribed-time time-varying output formation tracking (TVOFT) problem for heterogeneous multiagent systems (MASs) under directed topologies. Formation tracking in heterogeneous linear MASs is critical for practical applications, such as cooperative robotics, autonomous transportation, and surveillance. However, many existing related designs often fail to guarantee convergence within a prescribed time. To overcome this limitation, a distributed prescribed-time TVOFT protocol associated with a corresponding design algorithm is presented. In the designed protocol, a distributed output-feedback observer is constructed for each follower to estimate the state of the leader within a prescribed time. Then, a local output-feedback controller is developed by incorporating a local state observer. It is proved that the heterogeneous MASs can achieve the desired TVOFT within the prescribed time. Furthermore, a heterogeneous experimental platform consisting of two autonomous aerial vehicles and three autonomous ground vehicles, is constructed to verify the effectiveness of the proposed prescribed-time TVOFT design. Comparative experiment results highlight the advantages of the proposed design over existing methods from the perspective of accurate prescribed-time convergence and practical feasibility.

Users upload their own WiFi localization request signals to match against the received signal strengths (RSSs) in an offline constructed fingerprint database, thereby obtaining their own geographic location estimates. However, the relationships between the measured signals of users are seldom explored for enhancing the localization accuracy. Furthermore, the tedious site surveys required for building an offline fingerprint database hinder the wider application of this technology. To address the above issues, this article proposes a collaboratively enhanced lightweight WiFi localization algorithm named CBWF+, capable of providing an accurate indoor position with low-overhead fingerprints and collaborative localization. Specifically, we first discretize the area of interest to produce virtual points (VPs), and leverage the concept of circular-boundary-based localization to select a few VPs that share similar signal characteristics with the request signals. Then, important users for collaborative localization are chosen by two new proposed indicators. Next, based on the selection results and the RSS relationships among users, the spatiotemporal characteristics of WiFi signals are used to further filter out VPs that contradict the relationship between RSS and physical location. Finally, the credible VPs are used to obtain a high-accuracy estimate, by a proposed weighting method. The results of the real-world experiments validate the effectiveness of our proposed CBWF+ algorithm, compared to other state-of-the-art approaches (e.g., NN, OCLoc, CBWF, etc.). Specifically, in a 40-m$\times 17$-m real scenario with only 20 reference points (RPs) and 11 access point (APs), our algorithm achieves an average localization accuracy of 2.49 m. Our codes are available at:https://github.com/dadadaray/CBWF2.0.

The atmospheric density error affects the accuracy of the Strap-down inertial navigation system (SINS)/Refraction celestial navigation system (RCNS) integrated navigation seriously by degrading the accuracy of the atmospheric refraction model. At present, the mechanism and extent of the impacts of atmospheric density error on measurements measurement models of SINS/RCNS integrated navigation are unclear. To solve this problem, this study first derives the relationship among atmospheric density error, measurements and measurement models (including refraction apparent height, refraction angle, and star pixel coordinates) of SINS/RCNS integrated navigation. Then, the quantitative relationship is provided based on it. The results show that 3.0% atmospheric density error can cause errors of 190.22 m in refraction apparent height measurement, 4.40" in refraction angle measurement model, and 0.13 pixels in star pixel coordinates measurement model. And these errors increase approximately linearly with the increase of atmospheric density error. Furthermore, simulations are carried out to verified the theoretical derivation and the linear relationship between the impacts of the atmospheric density error, which has a significant impact on position error and a relatively small impact on velocity error and posture error. When the atmospheric density error are 3.0%, 6.0%, and 9.0%, the position errors of the SINS/RCNS integrated navigation are about 1.4 times, 2.1 times, and 2.9 times of those without atmospheric density error. The research provides a theoretical basis for the elimination of the atmospheric density error.

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm calledGeneticSimulated-annealing-basedParticle swarm optimization withAuto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs.

In this paper, the security control problem for a class of multi-tiered re-entrant manufacturing networks (RMNs) under cyber-attacks is investigated. First, a linear hyperbolic partial differential equation (PDE) continuum model is employed to model the dynamics of each single manufacturing line node and the overall RMN system is then characterized by a dual-layer coupling structure consisting of a production line network and a workshop network, both of which adhere to the global mass conservation law. Second, to mitigate the adverse effects of malicious cyber-attacks on RMNs, a node-dependent control approach and an edge-dependent control approach are developed, both of which guarantee the global exponential stability of the multi-tiered RMNs under cyber-attacks. Numerical simulations demonstrate the effectiveness of the proposed network architecture and control schemes.

In this article, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable. Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges. To solve this challenging problem, we propose the following novel results. Firstly, to estimate the relative positions among neighboring robots, a concurrent-learning based estimator is proposed. It relaxes the persistent excitation condition required in the classical ones such as the least-square estimator. Secondly, we introduce a finite-time agreement protocol to determine the shape location. This is achieved by estimating the relative position between each robot and a randomly assigned seed robot. The initial position of the seed one marks the shape location. Thirdly, based on the theoretical results of the relative localization, a novel behavior-based control strategy is devised. This strategy not only enables the adaptive shape formation of large groups of robots but also enhances the observability of inter-robot relative localization. Numerical simulation results are provided to verify the performance of our proposed strategy compared to the state-of-the-art ones. Additionally, outdoor experiments on real robots further demonstrate the practical effectiveness and robustness of our methods. Note to Practitioners—Shape formation has a broad potential for large groups of robots to execute certain tasks, such as object transport, forest firefighting, and entertainment shows. However, most of the existing approaches rely on external localization infrastructures, rendering them impractical in environments where such systems are not available. To address this issue, this article proposes an integrated strategy that can achieve shape formation for large groups of robots by using local distance and displacement measurements. This strategy consists of three main components. Firstly, a relative localization estimator is introduced to estimate the relative positions among neighboring robots. Secondly, a protocol for reaching a consensus on the desired shape’s position is proposed. Thirdly, a behavior-based controller is developed to achieve massive shape formation and enhance the observability of relative localization. More details of the proposed algorithms and swarm robotic systems are provided in this article.

Formation constrained optimal tracking problems for heterogeneous multi-agent systems under Byzantine attacks are studied. The objective of the honest agents, unaffected by Byzantine attacks, is to find optimal trajectories that minimize the cumulative local cost functions of all honest agents, while simultaneously satisfying the cumulative local inequality constraints and maintaining the formation configuration. A distributed resilient formation tracking controller utilizing preview control and primal-dual strategy is proposed without requiring each agent to know which agents are affected by Byzantine attacks. The performance of the proposed algorithm is analyzed in terms of the upper bounds of dynamic regret and constraint violations. Numerical simulations and experiments, involving one unmanned aerial vehicle and four unmanned ground vehicles, are performed to verify the effectiveness of the obtained results.

In swarm robotics, confrontation including the pursuit-evasion game is a key scenario. High uncertainty caused by unknown opponents’ strategies, dynamic obstacles, and insufficient training complicates the action space into a hybrid decision process. Although the deep reinforcement learning method is significant for swarm confrontation since it can handle various sizes, as an end-to–end implementation, it cannot deal with the hybrid process. Here, we propose a novel hierarchical reinforcement learning approach consisting of a target allocation layer, a path planning layer, and the underlying dynamic interaction mechanism between the two layers, which indicates the quantified uncertainty. It decouples the hybrid process into discrete allocation and continuous planning layers, with a probabilistic ensemble model to quantify the uncertainty and regulate the interaction frequency adaptively. Furthermore, to overcome the unstable training process introduced by the two layers, we design an integration training method including pre-training and cross-training, which enhances the training efficiency and stability. Experiment results in both comparison, ablation, and real-robot studies validate the effectiveness and generalization performance of our proposed approach. In our defined experiments with twenty to forty agents, the win rate of the proposed method reaches around ninety percent, outperforming other traditional methods. Note to Practitioners—With artificial intelligence rapidly developing, robots will play a significant role in the future. Especially, the swarm formed by many robots holds promising potential in civil and military applications. Promoting the swarm into games or battles is rather riveting. The reinforcement learning method provides a plausible solution to realize the battle of robotic swarms. There are still some issues that need to be addressed. On one hand, we focus on the uncertainty caused by the battlefield nature and the environment which limits our ability for the implementation of swarms. On the other hand, we solve the problem that the decision process combined with commands and actions is a hybrid system, which cannot be directly reflected in the confrontation of swarms. Overall, our approaches throw light on artificial general intelligence and also reveal a solution to interpretable intelligence.

This paper investigates the secure cooperative control problem of second-order nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks based on dynamic event-triggered schemes. First, a dynamic event-triggered control protocol is designed, which can adaptively adjust the triggering threshold according to the state information of MASs, it can also reduce the number of triggering times and save the communication resources. Then, based on the parameters of control protocol, intensity of DoS attacks, dynamics of MASs, and communication topologies, some sufficient conditions are derived for MASs to guarantee consensus under DoS attacks. In addition, it is proved that under the proposed dynamic event-triggered schemes and control protocol, the MASs can achieve consensus, and the event-triggered schemes can exclude Zeno behavior. Finally, numerical simulation example is presented to illustrate the effectiveness of the main results.

The robust fuzzy control problem for a category of network-type re-entrant manufacturing systems (RMSs) is explored in this paper. A continuum model is first employed to represent the RMSs with external disturbances and time-varying communication delays in terms of a nonlinear hyperbolic partial differential equation (PDE) model. The nonlinear model is then reformulated in the framework of a T-S fuzzy model. A novel Lyapunov-Krasovskii-type stability theorem is proposed for the concerned PDEs with delays, extending the classical Lyapunov-Krasovskii framework to delayed PDE systems. Using the proposed stability theorem, LMI-based conditions are developed for stability analysis and control synthesis of the closed-loop RMSs under external disturbances and time-varying delays, which demonstrates effective alignment of production output with market demand despite environmental perturbations. At last the results of a simulation validate the developed control approach.

This paper investigates the fault-tolerant observer design problem for a class of re-entrant manufacturing systems (RMSs) in the presence of workstation faults during the production process. A hyperbolic hybrid partial differential equation (HHPDE) continuum model is constructed to describe the dynamics of RMSs suffering from unexpected workstation faults, by considering that machinery failures of workstations lead to discarding of defective products. In the case that the faults are known, a fault-tolerant impulsive observer is designed for state estimation of the RMSs. In the case that the fault information is uncertain, a diagnostic observer based residual evaluation logic is developed for fault detection first. Upon detecting the faults, an adaptive impulsive observer is then proposed to simultaneously estimate both the system states and the faults. In addition, by using a piecewise Lyapunov function candidate, sufficient stability conditions that guarantee the exponential input-to-state stability (EISS) of the estimation error are formulated in terms of linear matrix inequalities (LMIs). Finally, the feasibility and effectiveness of the proposed strategy are validated through numerical simulations.

The surge in high-throughput biological data necessitates efficient tools for knowledge discovery. Group Lasso for logistic regression, a powerful model for sample classification and gene selection in correlated data, relies on robust clustering. This paper addresses the instability of traditional $k$-means variants by introducing $k$-shape clustering into the group Lasso for logistic regression framework. Comparative analyses using simulated and real-world datasets demonstrate that group Lasso for logistic regression with $k$-shape (GLKSH) outperforms $k$-means variants in accuracy and robustness. GLKSH also uniquely identifies informative genes and achieves superior sample classification, highlighting the impact of clustering on group Lasso and offering a valuable approach for gene selection.

This article investigates the distributed estimation problem of an unknown high-dimensional sparse state vector for a stochastic dynamic system. The communication topology randomly switches, and the switching law is governed by a time-homogeneous Markovian chain. By means of the compressed sensing (CS) theory and a diffusion strategy, we propose a compressed distributed Kalman filter (CDKF). That is, each sensor first compresses the original high-dimensional regression data. Then, the covariance intersection fusion rule is utilized to obtain a distributed Kalman filter (DKF) estimate in the compressed low-dimensional space. Afterward, the original high-dimensional sparse state vector can be well recovered by a reconstruction technique. In terms of stability analysis, one of the main difficulties lies in analyzing the product of nonindependent and nonstationary random matrices in the context of time-varying communication topologies. Relying on the stochastic stability theory, the Markov chain theory, and the CS theory, we establish the upper bound for the estimation error under the compressed cooperative excitation condition, which is much weaker than the traditional uncompressed collective observability conditions used in the existing literature. Finally, we provide a simulation example to illustrate the performance of the proposed algorithm.

This article investigates the leader-follower formation control of flight vehicles subject to speed and control acceleration constraints. The objective of the flight vehicles is to track a virtual leader in a nominal configuration, while the speeds and control accelerations of the flight vehicles are restricted within certain ranges. A distributed extended state observer (DESO) featuring practical predefined-time convergence is proposed for the followers to estimate the leader's position and velocity. Then, an adaptive finite-time position tracking control law is developed so that the followers form the expected formation by tracking the expected positions related to the estimation of the virtual leader's information and the nominal configuration. The speed constraint is satisfied by leveraging a transformation based on the inverse hyperbolic tangent function, while an adaptive scheme exploiting the integral barrier Lyapunov function (IBLF) is proposed to address the control acceleration constraints. Numerical simulations are conducted to validate the proposed method.

This article introduces a novel approach for spacecraft formation flying utilizing Lorentz-augmented techniques. It demonstrates that the relative motion among spacecraft, driven by the Lorentz force, possesses equilibrium states beneficial for formation maintenance. However, for effective formation reconfiguration, reliance solely on the Lorentz force is insufficient; low thrust is also necessary. To address this, this article proposes an optimal control framework based on reinforcement learning (RL). It derives the nonlinear dynamics of relative motion within the geomagnetic field, considering intersatellite Lorentz force, atmospheric drag, and Earth's gravitational harmonics. The study employs Lagrangian coherent structure analysis to identify relative equilibrium configurations and develops an RL-based optimal control strategy for real-time formation reconfiguration. By leveraging optimal demonstrations, the framework guides the agent's actions to match these demonstrations over time, especially when encountering out-of-distribution states. Numerical simulations confirm the method's optimality, robustness, and real-time performance, highlighting its potential in achieving optimal control and adapting to varying environment in future space missions.

In this article, the security formation control problem is investigated for underactuated quadrotors involving nonlinear coupled dynamics, subject to denial-of-service (DoS) attacks and uncertain communication faults. A security formation control method is proposed, including a distributed resilient observer and a hierarchical data-driven controller. The observer with an adaptive event-triggered mechanism is developed to restrain the influence of DoS and communication faults on interaction information among quadrotors, and Zeno behavior of all observers can be avoided. The optimal control laws are learned iteratively based on observation data and system data by utilizing reinforcement learning without knowledge of system dynamics. The stability of the constructed closed-loop control system is proven, and sufficient conditions are established for the unreliable network. Simulation results demonstrate the advantages of the proposed security control method.

In this article, two types of multiagent systems (MASs) are developed for distributed bilevel constrained optimization. Within the framework of the distributed bilevel optimization modeling, the objective function is in a summation manner of local objective functions. Multiple agents connected via a communication network are harnessed for optimizing the local objective functions cooperatively while adhering to coupled constraints with global information, and each agent is tasked with solving an individual inner problem and it is subject to multiple local constraints. To address challenges posed by the distributed computation requirement of the proposed bilevel optimization models and multiple complex constraints, first and second-order MASs are customized and proven to converge to the optimal solution. Three examples involving two numerical simulations and an economic dispatch problem are elaborated to verify and demonstrate the optimality, enhanced robustness to communication blocking, and fast convergence of the proposed approaches.

Over the past decade, numerous synchronization-based identification methods have been proposed to address the challenge of identifying unknown network topologies. The linear independence condition (LIC) is an essential requirement in these methods, however, there are issues with this condition. In this article, we propose an improved LIC-free synchronization-based identification method to address above issues. Specifically, a drive network consisting of isolated nodes that satisfy specific conditions is constructed, and the network containing an unknown topology is defined as the response network. Through the design of appropriate controllers and update laws, the drive network and the response network achieve synchronization, while the estimation matrix accurately identifies the unknown topology matrix. Our method is proven to be a generalized form of the existing LIC-free identification methods. Furthermore, we introduce a novel proof framework to theoretically demonstrate the effectiveness of our method. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

This article aims to consider the practical fixed-time (FxT) synchronization control of discontinuous Takagi–Sugeno (T–S) fuzzy complex networks (DTSFCNs). By using the comparison principle and the iterative technique, new completely-intermittent-type FxT stability lemmas are established. The new Lyapunov inequality with a unified exponent condition is proposed, which can reduce the input of redundant system-independent parameters. Some existing results on the FxT stability lemmas are improved. Considering that the states cannot converge to the origin accurately, completely-intermittent-type practical FxT stability lemmas are further investigated, which can be regarded as the first one and the problem of constructing the practical FxT stability lemmas under the completely-intermittent mechanism is solved. From the perspective of saving information resources to the greatest extent, by designing the quantized controllers, the intermittent FxT and practical FxT control of the addressed DTSFCNs are studied. Here, the quantized controller only works in intermittent intervals, which can improve the quantized controllers and intermittent controllers. Finally, numerical simulations are given to verify the main results.

This paper studies the practical fixed-time synchronization control of T-S fuzzy complex networks with different dimensions, a topic for which no prior solutions exist, despite extensive research on the synchronization of T-S fuzzy complex networks with the same dimensions. First of all, under the semi-intermittent framework, by adopting a more relaxed average control rate under the new definition, a novel fixed-time stability lemma with a unified exponential condition is established, which can reduce the input of parameters irrelevant to the system. Considering the important practical application significance of the practical fixed-time stability, two new practical fixed-time stability lemmas are further obtained based on two different algorithms, which can greatly improve the existing ones. By using the transformation of the same dimension, some new results on the practical fixed-time synchronization control of the considered networks are given by designing the intermittent event-triggered controllers with exponential gains, which have smaller trigger frequency and higher trigger efficiency than the traditional event-triggered controllers. In parallel, both static and dynamic event-triggered controls have been designed, and the dynamic function has been stringently proven to be positive via simple conditions. This accomplishment addresses a long-standing issue in the extant literature, where the presence of high-order terms in the dynamic function had hitherto precluded or failed to establish its non-negativity. Finally, the viability of the acquired results is demonstrated through numerical simulations.

As industrial control systems become increasingly interconnected with information networks, attackers could exploit vulnerabilities across different system layers to create complex exploit chains to compromise field control elements. As such, security decision-making is of essential importance to maintain the operational security of critical industrial infrastructures. In this paper, we consider the problem of designing cost-effective defense strategies to minimize the risk of successful attack paths. To this end, we propose a non-Markovian security game framework on labeled attack graphs to simulate the attack-defense process in industrial control systems. Compared with existing methods, where the cost of exploiting a vulnerability is considered constant, we consider a more dynamic and realistic case where the exploitation cost is discounted with the number of exploitations. Moreover, a state-decomposition based multi-agent reinforcement learning algorithm is developed to obtain the Nash equilibrium of the proposed non-Markovian security game. A case study on a simulated industrial control system is presented to illustrate the feasibility of the proposed approach. The results demonstrate that the discounting exploitation cost could greatly alter the attack and subsequently the defense strategies. In comparison to traditional static intrusion response approaches, our non-Markovian approach offers a more realistic and adaptive framework to anticipate evolving attack paths and allocate defense resources.

Industrial Internet of Things (IIoT) faces significant security challenges such as data privacy and vulnerabilities. Unsupervised anomaly detection aims to identify abnormal patterns by monitoring multivariate time series data of IIoT without anomaly annotation. Previous deep-learning-based methods have high-computation cost, which hinders their deployment in edge devices. In this article, we propose a dynamic anomaly detection network (DADN), which introduces a dynamic anomaly detection mechanism to enable efficient inference. Specifically, a bilateral early-exit mechanism is designed so that each sample can dynamically exit at a certain layer during the forward process to support the anomaly judgement, and the layer where sample exits is adaptively determined at the inference stage. Experimental results show that DADN significantly reduces computational costs and enhances F1 scores in industrial anomaly-detection benchmarks, as shown by a 58.22% decrease in GFLOPS on the SWAT dataset, outperforming previous representative method (anomaly transformer).

Time-varying formation tracking (TVFT) control problem for multiple Euler–Lagrange (EL) systems with an uncertain leader is investigated in this article. The objective of this problem is for the followers' states to track the uncertain leader's output and achieve the desired formation. One of the major challenges in solving such a problem lies in estimating the information of the leader. In this article, an observer-based TVFT control framework is presented in this article, which does not require all or part of the follower agents to know the system dynamic knowledge of the leader agent directly in advance. First, two fully distributed adaptive observers are presented for estimating the uncertain leader's state, output, and the unknown system matrices under an IE condition, which relaxes the existing restrictive persistently exciting or cooperative finite-time excitation condition. Then, on the basis of the constructed adaptive observers, state and output variables of the leader, a fully distributed TVFT for EL systems with an uncertain leader is developed, which means the proposed formation tracking controller operates independently of the system dynamic knowledge of the leader agent; with the help of the Lyapunov stability theory, the TVFT criterion for the considered system is derived. Finally, in order to demonstrate the theoretical results derived in this article, a practical air–ground formation tracking platform, which consists of an unmanned aerial vehicle and four unmanned ground vehicles, is introduced, and physical experimental results are obtained.

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon. Simulation results from various types of graph data are presented to demonstrate the learning, generalization, and robustness capabilities of the proposed quantum neural network (QNN) model.

In recent years, the synchronization of coupled neural networks (CNNs) has been extensively studied. However, existing results heavily rely on assuming continuous couplings, overlooking the prevalence of intermittent couplings in reality. In this article, we address for the first time the synchronization challenge posed by intermittently CNNs (ICNNs) with coupling delay. To overcome the difficulties arising from intermittent couplings, we put forward a general piecewise delay differential inequality to characterize the dynamics during both coupled intervals and decoupled intervals. Based on the proposed inequality, we establish delay-independent synchronization criteria (DISCs) for ICNNs, enabling them to tackle general coupling delay. Notably, unlike previous studies, the achievement of synchronization in our approach does not rely on external control. Furthermore, for ICNNs that synchronize only under small delays, we formulate non-linear matrix inequality (LMI)-based delay-dependent synchronization criteria (DDSCs) that are computationally efficient and do not require delay differentiability. Finally, we provide illustrative examples to demonstrate our theoretical results.

In this article, robustH∞synchronization problem for a class of networked re-entrant manufacturing systems (RMSs) is investigated by utilizing state feedback control and distributed adaptive state feedback control approaches. Different from the isolated single re-entrant manufacturing line, a networked RMS with three-tiered architecture is presented, which contains the production line, the production workshop and the workshop network. Based on the mass conservation law, the dynamics of the production line and the production workshop are established by a first-order linear hyperbolic PDE and a first-order semi-linear hyperbolic PDE, respectively. On one hand, in view of communication delays and external disturbances that might exist in the system, a delayed state feedback controller is constructed to address robustH∞synchronization of the networked RMSs. On the other hand, considering the uncertainty with coupling gain and the unavailability of global information, a distributed cooperative controller with the edge-dependent adaptive gain is further developed to ensure robustH∞synchronization of the networked RMSs. Numerical simulations validate the effectiveness of both proposed control schemes.

Superdiffusion refers to the faster diffusion process in a multiplex network compared to that in an individual network. In this work, we study how interlayer connectivity affects the diffusion performance of a multiplex network. Based on spectral graph theory, we explore the principles of superdiffusion in multiplex networks. We prove that in a duplex network with identical structures, superdiffusion cannot occur under one-to-one interlayer connections. In addition, we prove that the dissimilarity of the Fiedler vector significantly enhances the network superdiffusion performance, which can lead to superdiffusion when selecting nodes with differential eigenvector components in the Fiedler vector for interlayer connections. We also prove that the upper bound of network diffusion with interlayer crossing-connections is limited by the maximum difference of the eigenvector components in the Fiedler vector. Finally, we verify the effectiveness of the theoretical results by numerical analysis.

Pinning control provides an effective approach to controlling large-scale networks and conserving control resources. This article presents a solution to pinning synchronization in directed networks with a precise index that measures the pinning synchronization capability of directed networks, capturing full topological information about the networks. Building upon this index, the article utilizes matrix analysis tools, such as the non-negative matrix theory and strongly connected decomposition to analyze the impact of network structures and controller parameters on the network synchronizability. Specifically, the study investigates the influence of the in-degree of unpinned nodes, the difference between in-degrees and out-degrees of nodes, strong connectivity components, and the linear feedback control gains on the network synchronizability. Moreover, the article addresses the challenge of optimally selecting pinned nodes by using a graph partitioning algorithm and a greedy node selection algorithm, which can be applied to effectively select pinned nodes in a large-scale network. Extensive simulations on a range of real-world directed networks validate the efficiency of the proposed algorithms and demonstrate their superiority over seven baseline algorithms.

In this article, a momentum-based multiagent optimization approach is developed for distributed nonconvex optimal resource allocation. The proposed resource allocation model is formulated without the convex conditions, and a paradigmatic system based on the gradient descent with momentum method is proposed for handling its functional nonconvexity. Based on the paradigmatic system, a momentum-based multiagent system (MAS) is developed, and its convergence and convergence rate to a local minimizer are proven. Then, a distributed average tracking approach is introduced, based on which a hybrid multiagent optimization approach consisting of multiple MASs and a meta-heuristic rule is designed for seeking global minimizers. Finally, a simulation in a chiller system is elaborated to demonstrate the enhanced stability, fast convergence, and optimality of the developed distributed optimization approaches.

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations.

This paper investigates the robust intelligent control problem of multi-line re-entrant manufacturing plants. The control system is designed with a hierarchical architecture, where a nonlinear stochastic hyperbolic partial differential equation (PDE) is used to describe the system dynamics and a robust controller is designed to exponentially drive the manufacturing plants to a desired operation mode with steady feeding and production rates. The developed robust control scheme is shown to be practically implementable through convex optimization techniques. Numerical experiments are presented to demonstrate the feasibility and advantages of the proposed approach.Note to Practitioners—The motivation of this work originates from the need to develop an intelligent robust control strategy for a class of practical complex re-entrant manufacturing plants, for instance, the semiconductor wafer factory and the chemical production lines with numerous process procedures. Discrete-model-based algorithms have been extensively employed in this field due to their excellent convenience and great accuracy. However, when dealing with coupled multi-line re-entrant manufacturing plants with nonlinearities, traditional discrete-model-based methods lack rigorous theoretical analysis and, more importantly, suffer from the curse of dimensionality in many cases. To equip the re-entrant manufacturing plant with a desired operation mode that enjoys significant robustness against stochastic noises, we propose a continuum-model-based intelligent robust control strategy. The proposed method is practically useful in the sense that it can be conveniently applied to various industrial scenarios with re-entrant characteristics and the control design problem can be well solved via available convex optimization algorithms.

An adaptive neural stochastic contraction metric with Lipschitz constant optimization (aNSCM-Lip) is proposed for It stochastic systems with unmatched parameter uncertainties. The adaptive law is designed via the certainty equivalence principle with incremental stability guarantee, which is specified by the neural stochastic contraction metric (NSCM). Then, we develop a neural network (NN) based on Lipschitz constant estimation and optimization. Lipschitz optimization and weights training are formulated as optimization problems utilizing the alternating direction method of multipliers (ADMM), which ensures the Lipschitz continuity of the network metric and its derivative. The learning-based controller with a Lipschitz constant optimized network provides stability certificates for the closed-loop system. DC-DC buck vector and single-joint manipulator examples are given to demonstrate the effectiveness and superiority of the proposed control strategy.

This paper considers Stackelberg equilibrium (SE) and Nash equilibrium (NE) computation in a class of non-convex network aggregative games with one leader and multiple followers. The cost function of each follower is influenced by its strategy, the leader’s strategy, and its neighbors’ aggregative strategies. Also, the structured non-convex cost function of the leader is the composition of a canonical function and a vector-valued geometrical operator that relies on its strategy and followers’ strategies. In the leader-follower scheme, when the leader has knowledge of the best responses of the followers in a closed form, the SE strategy will be the optimal choice due to its relatively low cost. When the leader does not know the exact expression of followers’ best responses or the leader’s dominance is threatened, NE will be what all players are committed to achieving. The widespread existence of nonconvexity creates a significant challenge for computing the above equilibria in different circumstances. The results in existing convex games are not directly applicable to such a non-convex case, as they get trapped in local equilibria or stationary points rather than global equilibria. Here, we adopt the canonical transformation to reformulate the non-convex games and present the existence condition based on the canonical duality theory. Then two projection gradient algorithms are designed to pursue the SE and the NE, followed by proving the convergence of the algorithms.

Synchronization on multi-layer networks are paid increasingly attention in recent years, and the interplay between network dynamics and structures in multi-layer networks remains open. Focusing on interactions between synchronization dynamics and network structures from the perspective of coupled delays, this paper investigates the intra-layer and inter-layer synchronization on delayed duplex networks without and with additional controllers via Lyapunov stability theory and Halanay inequality, presents the synchronization criteria which are not only applied for the delayed duplex networks with general intra-layer topologies but for the case of networks without coupling delays, and gives the upper bound of delay thresholds for the first time, characterizing the interaction between intra-layer (inter-layer) synchronization and inter-layer (intra-layer) connections. Further on, we analyze the influence of coupling strengthes, topological structures and control gains on the threshold of delay, and obtain some interesting results: 1) the network with single kind of coupling delays (either intra-layer or inter-layer) can admit larger threshold than that with both kinds of coupling delays, and the network with controllers can admit larger threshold than that without controllers. 2) For intra-layer (inter-layer) synchronization in the network with just inter-layer (intra-layer) delays, the threshold of inter-layer (intra-layer) delays is bounded by an upper bound determined by the topology and coupling strength of the inter-layer (intra-layer). Finally, extensive numerical simulations are provided to verify the present theoretical results.

This paper proposes a subgraph-based hierarchical Q-learning network (SgHQN) approach to solve the optimal resource scheduling problem for complex industrial networks. In the industrial network, each connection between two individual stations has limited communication bandwidth, while each station has limited computing and storage capability and is only accessible to its local information. The resource packages flowing within the industrial network are treated as agents that have different sizes and different levels of decision-making priority. This makes the industrial resource scheduling problem on the industrial network a multi-level decision-making problem with information asymmetry. Specifically, the resource packages with lower decision-making priority have knowledge of the decisions made by those with higher priority, but not vice versa. To solve this resource scheduling problem with information asymmetry, an SgHQN model is developed by exploiting partial observations. It is found that the proposed SgHQN can be used to solve resource scheduling problems for general industrial networks. Numerical experiments simulating industrial scheduling scenarios demonstrate the effectiveness and advantages of our method.

China’s high-speed railway (HSR) has entered the era of networked operation. Any internal disturbance or eternal disturbance may result in delays of some trains and even cascading delays, which will not only reduce the traffic efficiency of HSR, but also break passengers’ travel and lower their satisfaction. Studying the delay propagation mechanism could assist the dispatcher in suppressing the negative effect of disturbances. However, current studies seldom consider the withholding strategy’s impact on delay propagation. Inspired by this, this article proposes a novel bi-directional delay propagation model combined with the trains’ operation trajectory and stations’ withholding strategy. Moreover, the operation constraint, station capacity constraint, and interlocking constraint are also considered. Then, the primary delay under section disruption (SD) and section temporary speed limit (STSL) are derived based on the location of the disturbance, duration time of the disturbance, and the operation strategy. Then, a max-plus algebra-based delay propagation model is established to compute the corresponding secondary delays. Also, the All Pair Critical Path algorithm is modified to incorporate the station capacity constraint in the searching process. Simulations based on the real China HSR subnetwork are implemented to verify the proposed model. Compared with the current study, the proposed model could accurately unfold the delay propagation in the opposite train heading direction. Besides, the relationship among disturbance duration, primary delay, and accumulative delay for the SD scenario and the relationship among temporarily limited velocity, primary delay, and accumulative delay for the STSL scenario are revealed.

This article proposes an optimal controller for a team of underactuated quadrotors with multiple active leaders in containment control tasks. The quadrotor dynamics are underactuated, nonlinear, uncertain, and subject to external disturbances. The active team leaders have control inputs to enhance the maneuverability of the containment system. The proposed controller consists of a position control law to guarantee the achievement of position containment and an attitude control law to regulate the rotational motion, which are learned via off-policy reinforcement learning using historical data from quadrotor trajectories. The closed-loop system stability can be guaranteed by theoretical analysis. Simulation results of cooperative transportation missions with multiple active leaders demonstrate the effectiveness of the proposed controller.

Pinning control has been attracting wide attention for the study of various complex networks for decades. This article explores grounded theory on the pinning synchronization of the emerging multiplex dynamical networks. The multiplex dynamical networks under study can describe many real-world scenarios, in which different layers have distinct individual dynamics of node. In this work, we build the bridge between multiplex structures and network dynamics by using the Lyapunov stability theory and the spectral graph theory. Furthermore, by analyzing spectral properties of the grounded super-Laplacian matrices, we set up several graph-based synchronization criteria for multiplex networks via pinning control. In addition, we overcome the difficulties induced by distinct node dynamics in different layers, and find that interlayer coupling strengths promote intralayer synchronization of multiplex networks. Finally, a collection of numerical simulations verifies the effectiveness of theoretical results.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Distributed output formation optimal tracking problems for multiagent systems over time-varying topologies with asynchronous and intermittent communications are investigated. Each agent collaboratively computes and tracks the optimal output formation reference that minimizes a global objective function formed by summing local objective functions. Simultaneously, this reference satisfies global constraints composed of local nonlinear inequality constraints and local closed convex set constraints. An asynchronous distributed estimator-based tracking control protocol is designed utilizing the constrained stochastic subgradient random projection method and the Lyapunov stability theory. Sufficient conditions for asymptotic convergence are given. It is revealed that the states of agents with constraints under asynchronous and intermittent communications converge asymptotically to the optimal reference signal using only neighboring information within the predefined formation. Finally, a numerical example is provided to validate the theoretical results.

The finite-time output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multiagent system (MAS) is investigated in this article, where the dynamics of the agents can be nonidentical, and leader's input is unknown. The target of this article is that the outputs of followers need to track leader's output and realize the desired formation in finite time. First, for removing the assumption that all agents are required to know the information of leader's system matrices and the upper boundary of its unknown control input in previous studies, a kind of finite-time observer is constructed by exploiting the neighboring information, which can estimate not only the leader's state and system matrices but also can compensate for the effects of unknown input. On the basis of the developed finite-time observers and adaptive output regulation method, a novel finite-time distributed output TVFT controller is proposed with the help of the technique of coordinate transformation by introducing an extra variable, which removes the assumption that the generalized inverse matrix of follows' input matrix needs to be found in the existing results. By means of the Lyapunov and finite-time stability theory, it is proven that the expected finite-time output TVFT can be realized by the considered heterogeneous nonlinear MASs within a finite time. Finally, simulation results demonstrate the efficacy of the proposed approach.

This article discusses the robust predefined output containment (RPOC) control problem for heterogeneous nonlinear multiagent systems having multiple uncertain nonidentical leaders. In order to solve this problem, a new kind of distributed observer-based RPOC control framework is presented. First, for obtaining the information of nonidentical leaders' dynamics, including uncertain parameters in leaders' system matrices, output matrices, states, and outputs, four kinds of adaptive observers are constructed in a fully distributed form without any knowledge of the dynamics of nonidentical leaders, exactly. Second, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers, where the adaptive observers can make up for the uncertain parameter in followers' dynamics, and the solutions of output regulation equations can be obtained adaptively by the developed adaptive strategy. Furthermore, with the help of the output regulation method and Lyapunov stability theory, the RPOC criteria for the considered system under unknown nonidentical leaders' dynamics are derived from the constructed controller. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

This article investigates the time-varying group formation tracking problems for multiagent systems where the agents are divided into multiple groups and each one achieves different goals. Specially, competition is allowed between different subgroups and there exists cooperation within each group. On this premise, distributed Nash equilibrium seeking strategy is utilized to search for the optimal relative evolutionary trend of each group, and formation tracking control protocol is designed to enable followers in each group to track their leader. Moreover, considering that the velocity signals are usually not available in practical situations, Nash equilibrium seeking strategy and formation tracking control are modified without velocity measurements. According to Lyapunov-based theory, it is proven that both the convergence of the Nash equilibrium seeking between subgroups and the convergence of formation tracking error within each group can be fulfilled. A simulation is provided to demonstrate the theoretical results.

Industrial sensors capture critical information for intelligent manufacturing maintenance. To promote equipment upgrading and manufacturing processes, intelligent decisions, and information learning play an important role. Although deep learning methods historically obtain excellent results, there is always a tradeoff between fine-tuning existing networks or designing models from scratch for sensor data processing. In this article, we propose the multihead attention self-supervised (MAS) representation model, which is a self-supervised learning-based sensor feature extraction network. To the best of our knowledge, this is the first time a self-supervised contrastive learning method using positive samples that represent multidimensional industry sensor data is being used for anomaly detection. We review alternative data augmentation methods proposed for better-representing sensor sequence data. We use this insight to design a new structure that adapts to the temporal characteristics of the application. We apply our method to a real-world water circulation system that uses a variety of industrial sensors. The effectiveness of the proposed MAS methods is demonstrated.

Domain generalization (DG) aims to train a model with access to a limited number of source domains for generalizing it across various unseen target domains. The key to solving the DG problem is disentangling domain-invariant features (i.e., semantic factors) from domain-specific features (i.e., variation factors) to facilitate generalizable representation learning. Previous studies either implicitly model the semantic and variation factors or ineffectively constrain the disentangling process, thus rendering the disentanglement incomplete and ineffective. In this study, we propose a novel approach, namedDualVAE, to explicitly model and disentangle both the semantic and variation factors. DualVAE is based on the variational autoencoder (VAE) architecture. However, it differs from the conventional VAE in that it consists of two paths, which explicitly model the semantic and variation factors. In addition to the reconstruction loss of VAE and the classification loss, three types of regularizations, namely statistical independence regularization, factorized prior regularization, and prediction consistency regularization, are proposed to further facilitate the disentanglement of factors. Experimental results on representative DG benchmarks show that our method performs favourably against previous state-of-the-art methods. Ablation and visualization results demonstrate that semantic and variation factors can be effectively disentangled.

This article investigates the practical time-varying output formation tracking (TVOFT) problem for heterogeneous nonlinear multiagent systems (MASs) having multiple leaders, where agents herein could have heterogeneous dynamics and interact with each other under event-triggered communications. It is required that the outputs of followers not only track the predefined convex combination of multiple leaders but also achieve the desired time-varying formation simultaneously. The existing works on formation tracking problems for MASs with multiple leaders depend on the assumption that each follower is a well-informed or uninformed follower, where the well-informed follower is required to have all the leaders as its neighbor. To remove the limitation, a fully distributed observer-based formation tracking control protocol is developed and employed. First, an adaptive state observer with an edge-based event-triggered mechanism for estimating the states of multiple leaders is proposed based on the neighboring interactions, which eliminates the unexpected Zeno behavior. Second, a novel observer is constructed for each follower by exploiting the output information of the follower, in which the adaptive neural network (NN)-based approximation is exploited to compensate for the unknown nonlinearity. A practical TVOFT control protocol is then generated by the proposed observers, where the parameters are determined by an algorithm including five steps. With the help of Lyapunov stability theory and output regulation method, a practical TVOFT criterion for the considered closed-loop system is derived. Finally, the effectiveness of the proposed control scheme is illustrated by a numerical example.

In this article, we consider the problem of distributed game-theoretic learning in games with finite action sets. A timestamp-based inertial best-response dynamics is proposed for Nash equilibrium seeking by players over a communication network. We prove that if all players adhere to the dynamics, then the states of players will almost surely reach consensus and the joint action profile of players will be absorbed into a Nash equilibrium of the game. This convergence result is proven under the condition of weakly acyclic games and strongly connected networks. Furthermore, to encounter more general circumstances, such as games with graphical action sets, state-based games, and switching communication networks, several variants of the proposed dynamics and its convergent results are also developed. To demonstrate the validity and applicability, we apply the proposed timestamp-based learning dynamics to design distributed algorithms for solving some typical finite games, including the coordination games and congestion games.

This article addresses finite-time connectivity-preserving rendezvous problems of networked uncertain Euler-Lagrange systems, where two types of time-varying leaders are investigated, and only a subset of followers can have access to the leader’s trajectory. The distributed estimation and control architecture is then established to solve this problem with an emphasis on the settling-time estimation. In particular, in the first layer, the finite-time distributed estimators are developed to estimate and reconstruct the states of both linear and nonlinear leaders, respectively. In the second layer, distributed controllers are designed for consensus tracking in a finite-time using estimated leader information. Further, to account for limited sensing ranges, another distributed algorithm is given via an artificial potential field to guarantee finite-time rendezvous. Numerical simulation results are given to validate the effectiveness of the proposed designs.

The stabilization problem of two categories of discrete-time linear time-varying (LTV) systems subject to unbounded distributed input delays is investigated in this article. A truncated predictor feedback law is first built for a category of systems under some common assumptions. Then, under some weakened assumptions, a predictor-type feedback law is developed for the other category of more general systems. The global exponential stability of the closed-loop systems is proved. Furthermore, the result on the truncated predictor feedback control law includes many existing results on LTV systems subject to bounded input delays and linear time-invariant (LTI) systems subject to unbounded input delays as special cases. Finally, the results of simulations validate the effectiveness of the developed control laws.

This article investigates the problem of cooperative guidance against maneuvering target under malicious attacks. In consideration of the false-data injection attacks (FDIAs), a reputation-based cooperative guidance law with fault tolerance is proposed to drive multiflight vehicles to reach a maneuvering target simultaneously. A novel prescribed performance function (PPF) with predefined-time convergence is presented by taking into account the limitation of available capacity. By incorporating the reputation system based on confidence factors and trust factors, which are leveraged to identify the attacked communication links or vehicle members, the fault-tolerant behavior can be achieved to resist the effects resulted from the FDIAs. The effectiveness of the reputation-based fault-tolerant cooperative guidance method is verified by numerical simulation.

In this article, we aim to answer the following question: What kind of games can guarantee convergence of the (full-information or partial-information, continuous-time or discrete-time) gradient play? To the best of our knowledge, current works on Nash equilibrium seeking are mainly established on the monotonicity condition. We introduce a concept called stability condition to continuous games, which includes the monotonicity condition as a special case. We prove that the stability condition is necessary and sufficient for convergence of gradient play. In detail, we show that, if the step size is fixed and within a given bound, the full-information and partial-information gradient play is guaranteed to converge to the Nash equilibrium in strongly stable games. If the step size is diminishing, then convergence of the gradient play can be obtained for strictly stable games. We present a game that is stable but not monotone to illustrate our theoretical developments.

This article addresses the problem of distributed Nash equilibrium seeking over networks for games with finite action sets. Gradient-like and consensus-based methods commonly used for continuous action spaces fail to work for this case. To this end, we propose a utility decoupling method to reformulate the original game into an augmented game, which preserves the Nash equilibrium and weakly acyclic property, yet enjoys a utility coupling network the same as the communication network. In this way, a variety of full-information game-theoretic learning dynamics for the augmented game turns into partial-information Nash equilibrium seeking dynamics for the original game. We proceed to apply the developed utility decoupling method to formulate three types of distributed Nash equilibrium seeking dynamics, including distributed best-response dynamics, distributed fictitious play, and distributed regret matching for weakly acyclic games. In the last, a typical color assignment game is utilized to empirically illustrate the validity and effectiveness of our approach.

This article analyzes a security problem for industry cloud control systems (CCSs), where a malicious false data injection (FDI) attacker may deteriorate the system performance by tampering with the data being transmitted. To improve the robustness of CCSs against FDI attacks, a redundancy-based sensor configuration scheme is provided through analysis of the observability under attacks of the nodes in CCSs. Then, a defending resource allocation scheme is developed based on a two-stage Stackelberg game in order to optimize the overall defense capability of the CCSs with limited defending resource. In this two-stage Stackelberg game, the defender of the CCSs acts first and allocates the defending resource to secure the measurements of wireless sensors. With the knowledge of the defender’s strategy, the attacker then decides which target nodes to launch attacks on. The optimal resource allocation strategy is then designed in the sense of the Stackelberg equilibrium. Furthermore, the defense problem under different attacking resource constraints is investigated. Numerical examples illustrate the effectiveness of the proposed approach.

Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is a severe performance drop compared with {real-valued} networks, due to its intrinsic frequent weight oscillation during training. In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training. We identify that the weight oscillation mainly stems from the non-parametric scaling factor. To address this issue, we propose to parameterize the scaling factor and introduce a weighted reconstruction loss to build an adaptive training objective. For the first time, we show that the weight oscillation is controlled by the balanced parameter attached to the reconstruction loss, which provides a theoretical foundation to parameterize it in back propagation. Based on this, we learn our ReBNN by calculating the balanced parameter based on its maximum magnitude, which can effectively mitigate the weight oscillation with a resilient training process. Extensive experiments are conducted upon various network models, such as ResNet and Faster-RCNN for computer vision, as well as BERT for natural language processing. The results demonstrate the overwhelming performance of our ReBNN over prior arts. For example, our ReBNN achieves 66.9% Top-1 accuracy with ResNet-18 backbone on the ImageNet dataset, surpassing existing state-of-the-arts by a significant margin. Our code is open-sourced at https://github.com/SteveTsui/ReBNN.

Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which consists of a feature extractor and a classifier. The feature extractor is shared across sequentially arrived tasks or classes, but one specific group of weights of the classifier corresponding to one new class should be incrementally expanded. Consequently, the parameters of a continual learner gradually increase. Moreover, as the classifier contains all historical arrived classes, a certain size of the memory is usually required to store rehearsal data to mitigate classifier bias and catastrophic forgetting. In this paper, we propose a non-incremental learner, named AttriCLIP, to incrementally extract knowledge of new classes or tasks. Specifically, AttriCLIP is built upon the pre-trained visual-language model CLIP. Its image encoder and text encoder are fixed to extract features from both images and text. Text consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute word bank and serve as attributes. As we compute the visual and textual similarity for classification, AttriCLIP is a non-incremental learner. The attribute prompts, which encode the common knowledge useful for classification, can effectively mitigate the catastrophic forgetting and avoid constructing a replay memory. We evaluate our AttriCLIP and compare it with CLIP-based and previous state-of-the-art continual learning methods in realistic settings with domain-shift and long-sequence learning. The results show that our method performs favorably against previous state-of-the-arts. The implementation code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/AttriCLIP.

The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quantization stands out as a solution by representing the network in low-bit parameters and operations. However, there is a significant performance drop when performing low-bit quantized DETR (Q-DETR) with existing quantization methods. We find that the bottle-necks of Q-DETR come from the query information distortion through our empirical analyses. This paper addresses this problem based on a distribution rectification distillation (DRD). We formulate our DRD as a bi-level optimization problem, which can be derived by generalizing the information bottleneck (IB) principle to the learning of Q-DETR. At the inner level, we conduct a distribution alignment for the queries to maximize the self-information entropy. At the upper level, we introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy. Extensive experimental results show that our method performs much better than prior arts. For example, the 4-bit Q-DETR can theoretically accelerate DETR with ResNet-50 backbone by 6.6× and achieve 39.4% AP, with only 2.6% performance gaps than its real-valued counterpart on the COCO dataset11Code: https://github.com/SteveTsui/Q-DETR.

This paper analyzes the security problem of cloud control systems (CCSs), where a malicious false data injection (FDI) attacker may deteriorate the system performance by tampering with the measurements being transmitted. The CCS defender allocates defense budgets among nodes to ensure the safe operation of CCSs. The strategic interactions between the FDI attacker and the CCS defender are modeled as a Stackelberg game, and the optimal strategies for both sides are analyzed in the sense of Nash equilibrium. Numerical examples illustrate the main results of this paper.

Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid development of 5G network technologies, an enormous number of mobile devices (MDs) have been connected to ENs. As a result, how to maximize the profit of ENs has become increasingly more challenging since it involves massive heterogeneous decision variables about task allocation among MDs, ENs, and a cloud data center (CDC), as well as associations of MDs to proper ENs dynamically. To tackle such a challenge, this work adopts a divide-and-conquer strategy that models applications as multiple subtasks, each of which can be independently completed in MDs, ENs, and a CDC. A joint optimization problem is formulated on task offloading, task partitioning, and associations of users to ENs to maximize the profit of ENs. To solve this high-dimensional mixed-integer nonlinear program, a novel deep-learning algorithm is developed and named as a Genetic Simulated-annealing-based Particle-swarm-optimizer with Stacked Autoencoders (GSPSA). Real-life data-based experimental results demonstrate that GSPSA offers higher profit of ENs while strictly meeting latency needs of user tasks than state-of-the-art algorithms.

This paper investigates the optimal formation control of a heterogeneous multiagent system consisting of multiple quadrotors and ground vehicles via reinforcement learning to achieve the time-varying formation under switching topologies. A distributed observer is firstly constructed to generate references using local information for each vehicle to form time-varying formation and the convergence of the observer under switching topologies is proven. Then, reinforcement learning methods are provided for the heterogeneous vehicle group to realize the optimal tracking control without information of vehicle dynamical model. Simulation tests are given to confirm the effectiveness of the proposed method.

The problem of seeking Nash equilibrium (NE) based on aggregative games under quantization constraints is full of challenges. Although the NE seeking algorithm in continuous-time systems has been studied, this problem in discrete-time systems still needs to be solved urgently. To address this problem, three distributed algorithms are first proposed under three quantization cases, adaptive, random, and time-varying quantizations, based on doubly stochastic communication topology networks. Then, the actions of players would eventually converge to NE under the conditions of vanishing step size and strong monotonicity are proved. Moreover, the convergence rate of the three quantization cases are analyzed, respectively. Finally, numerical experiments are implemented on plug-in hybrid electric vehicles (PHEVs) to validate the effectiveness of the proposed distributed algorithms. Comparing the convergence rates of the three proposed algorithms, the convergence effect of the adaptive quantization is better than that of the other two quantization cases.

The practical output containment problem for heterogeneous nonlinear multiagent systems under external disturbances generated by an exosystem is investigated in this article. It is required that the outputs of followers converge to the predefined convex combination of leaders' outputs. One of the major challenges in solving such a problem lies in dealing with the coupling among different nonlinearities, state dimensions, and system matrices of heterogeneous agents. To overcome the aforementioned challenge, a distributed observer-based control protocol is developed and employed. First, an adaptive state observer for estimating the states of all the leaders is constructed based on the neighboring interactions. Second, two new classes of observers are constructed for each follower exploiting the output information of the follower, in which the adaptive neural networks (NNs)-based approximation is exploited to compensate for the unknown nonlinearity in the followers' dynamics. A practical output containment control protocol is then generated by the proposed observers, where the control parameters are determined by an algorithm including two steps. Furthermore, with the help of the Lyapunov stability theory and the output regulation method, the practical output containment criteria for the considered closed-loop system under the influences of external disturbances are derived on the basis of the presented control protocol. Finally, the derived theoretical results are illustrated by a simulation example.

This article investigates uniformly predefined-time bounded consensus of leader-following multiagent systems (MASs) with unknown system nonlinearity and external disturbance via distributed adaptive fuzzy control. First, uniformly predefined-time-bounded stability is analyzed and a sufficient condition is derived for the system to achieve semiglobally (globally) uniformly predefined-time-bounded consensus. Therein, the settling time is independent of initial conditions and can be defined in advance. Then, for first-order MASs, distributed adaptive fuzzy controllers are designed by combining neighboring consensus errors to drive all following agents to globally track the leader's state within predefined time. For second-order MASs, by formulating filtered errors, the consensus errors between following agents and the leader are shown to be bounded if the filtered errors are bounded. Furthermore, with the distributed controllers designed based on filtered errors, second-order MASs achieve semiglobally uniformly predefined-time-bounded leader-following consensus. Finally, two numerical examples are simulated, including: 1) a first-order leader-following MAS and 2) a second-order Lagrangian system consisting of single-link manipulators, to demonstrate the performance of the proposed controllers.

In this article, an augmented game approach is proposed for the formulation and analysis of distributed learning dynamics in multiagent games. Through the design of the augmented game, the coupling structure of utility functions among all the players can be reformulated into an arbitrary undirected connected network while the Nash equilibria are preserved. In this case, any full-information game learning dynamics can be recast into a distributed form, and its convergence can be determined from the structure of the augmented game. We apply the proposed approach to generate both deterministic and stochastic distributed gradient play and obtain several negative convergent results about the distributed gradient play: 1) a Nash equilibrium is convergent under the classic gradient play, yet its corresponding augmented Nash equilibrium may be not convergent under the distributed gradient play and, on the other side, 2) a Nash equilibrium is not convergent under the classic gradient play, yet its corresponding augmented Nash equilibrium may be convergent under the distributed gradient play. In particular, we show that the variational stability structure (including monotonicity as a special case) of a game is not guaranteed to be preserved in its augmented game. These results provide a systematic methodology about how to formulate and then analyze the feasibility of distributed game learning dynamics.

The distributed Nash equilibrium (NE) seeking problem for multicoalition games has attracted increasing attention in recent years, but the research mainly focuses on the case without agreement demand within coalitions. This article considers a class of networked games among multiple coalitions where each coalition contains multiple agents that cooperate to minimize the sum of their costs, subject to the demand of reaching an agreement on their state values. Furthermore, the underlying network topology among the agents does not need to be balanced. To achieve the goal of NE seeking within such a context, two estimates are constructed for each agent, namely, an estimate of partial derivatives of the cost function and an estimate of global state values, based on which, an iterative state updating law is elaborately designed. Linear convergence of the proposed algorithm is demonstrated. It is shown that the consistency-constrained multicoalition games investigated in this article put the well-studied networked games among individual players and distributed optimization in a unified framework, and the proposed algorithm can easily degenerate into solutions to these problems.

The enclosing subgraph of a target link has been proved to be effective for prediction of potential links. However, it is still unclear what topological features of the subgraph play the key role in determining the existence of links. To give a possible answer to this question, in this paper, we propose a neural network based learning method for link prediction with only 1-hop neighborhood information. In detail, we extract the one-hop neighborhood of a target link as the enclosing subgraph, then encode the subgraph into different types of topological features, and lastly feed these features to train a fully connected neural network for link prediction. The experimental results show that our proposed learning method with the 1-hop neighborhood features could outperform those heuristic-based methods and achieve nearly equal performance to the state-of-the-art learning-based method WLNM and SEAL. Furthermore, it is observed that these features can be concatenated with attribute vectors to greatly promote the link prediction performance in attributed graphs. This indicates that the topological pattern within an enclosing subgraph, which determines the existence of a possible link, can be aggregated by some elementary subgraph features.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

Modeling the spatiotemporal relationship (STR) of traffic data is important yet challenging for existing graph networks. These methods usually capture features separately in temporal and spatial dimensions or represent the spatiotemporal data by adopting multiple local spatial-temporal graphs. The first kind of method mentioned above is difficult to capture potential temporal-spatial relationships, while the other is limited for long-term feature extraction due to its local receptive field. To handle these issues, the Synchronous Spatio-Temporal grAph Transformer (S2TAT) network is proposed for efficiently modeling the traffic data. The contributions of our method include the following: 1) the nonlocal STR can be synchronously modeled by our integrated attention mechanism and graph convolution in the proposed S2TAT block; 2) the timewise graph convolution and multihead mechanism designed can handle the heterogeneity of data; and 3) we introduce a novel attention-based strategy in the output module, being able to capture more valuable historical information to overcome the shortcoming of conventional average aggregation. Extensive experiments are conducted on PeMS datasets that demonstrate the efficacy of the S2TAT by achieving a top-one accuracy but less computational cost by comparing with the state of the art.

In this article, a consensus-based multipopulation game dynamics approach is proposed for distributed Nash equilibria seeking and optimization. The approach is fundamentally different from existing population dynamics from that: 1) the underlying communication network underlying the population game dynamics could be arbitrary undirected connected graph and more importantly 2) the proposed approach works in a distributed manner even when the objective functions of players are strongly coupled with each other. The proposed approach greatly extends the applicability of population game dynamics in distributed optimization and learning problems. A distributed constrained optimization problem and a traffic routing problem are utilized to illustrate the feasibility of the proposed multipopulation game dynamics approach.

Topology identification of multilayer networks is of great significance in the fields of information, engineering, and society. Current research on topology identification of multilayer networks requires the knowledge of the specific number of layers and the corresponding nodes in each layer in advance. However, the information is often unknown in reality. Herein, for a multilayer network with unknown layers, where node dynamic functions are in the quadratic form, we can obtain the specific layers and the topology structure based on compressive sensing. Notably, the number of layers can be obtained in two ways: 1) by the number of diverse node dynamic functions or 2) inner coupling matrices. The effectiveness and robustness of our method are verified through numerical simulations.

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection.

Chaotic dynamics is an important source for generating pseudorandom binary sequences (PRBS). Much efforts have been devoted to obtaining period distribution of the generalized discrete Arnold's Cat map in various domains using all kinds of theoretical methods, including Hensel's lifting approach. Diagonalizing the transform matrix of the map, this article gives the explicit formulation of any iteration of the generalized Cat map. Then, its real graph (cycle) structure in any binary arithmetic domain is disclosed. The subtle rules on how the cycles (itself and its distribution) change with the arithmetic precision$e$eare elaborately investigated and proved. The regular and beautiful patterns of Cat map demonstrated in a computer adopting fixed-point arithmetics are rigorously proved and experimentally verified. The results can serve as a benchmark for studying the dynamics of the variants of the Cat map in any domain. In addition, the used methodology can be used to evaluate randomness of PRBS generated by iterating any other maps.

One key factor affecting the distributed Nash equilibrium (NE) seeking in aggregative games is the unbalanced communication structure for multiple players. Although some results on seeking NE over undirected or weight-balanced graphs were established, how to address the distributed NE seeking problem over general directed communication graphs is still an outstanding challenge. This paper addresses the NE seeking problem for a class of aggregative games with general directed communication graphs. To achieve this objective, two new kinds of distributed discrete-time NE seeking algorithms are developed for aggregative games over fixed digraphs and time-varying digraphs, respectively. In particular, motivated by the heavy-ball method in optimization studies, a momentum term is introduced to the update law of the players’ actions and it is numerically verified that this momentum term accelerates the convergence of the proposed algorithms. For both strongly connected fixed graph and$B$-strongly connected time-varying graph, it is theoretically proved that the actions of players will converge to the NE of aggregative games for the case of decreasing step-size implemented by the proposed NE seeking algorithms if the cost functions and the aggregation of players satisfy some certain conditions. Finally, the developed NE seeking algorithms are applied to the energy consumption control of plug-in hybrid electric vehicles (PHEVs), which demonstrates the effectiveness of the theoretical results.

In the context of global integration, the theory of financial complex networks has made significant contributions to the establishment of stable financial systems and effective regulatory systems. This survey paper presents a systematic methodology for the multi-layer financial complex networks and their applications. Several typical financial networks in existing research are first summarized: the interbank network, the credit network, the international trade network, and the dealer network. The main methods used in the existing literature to analyze the structure of financial networks include maximum entropy methods, network characterization, community detection, and dynamic multi-layer network. Financial risk contagion as well as spillover effects are the core issues that most of the literature focuses on. Finally, this paper reviews the shortcomings of the existing literature and suggests future research directions in this area.

This paper investigates the coordination control problem for a special nonlinear heterogeneous multi-agent system consisting of tail-sitter unmanned aerial vehicles and unmanned ground vehicles with uncertainties and disturbances. A robust hierarchical pinning control scheme is proposed for the heterogeneous multi-agent system to restrain the uncertainties and disturbances and achieve coordination scenarios. The heterogeneous multi-agent system can realize coordination tasks by selecting proper pinning nodes and estimating coupling strength. The robustness of the whole system is proven utilizing the Lyapunov stability theorem. The effectiveness of the robust hierarchical pining control method is validated by simulation scenarios.

Using reinforcement learning (RL) algorithm to optimize guidance law can address non-idealities in complex environment. However, the optimization is difficult due to huge state-action space, unstable training, and high requirements on expertise. In this paper, the constrained guidance policy of a neural guidance system is optimized using improved RL algorithm, which is motivated by the idea of traditional model-based guidance method. A novel optimization objective with minimum overload regularization is developed to restrain the guidance policy directly from generating redundant missile maneuver. Moreover, a bi-level curriculum learning is designed to facilitate the policy optimization. Experiment results show that the proposed minimum overload regularization can reduce the vertical overloads of missile significantly, and the bi-level curriculum learning can further accelerate the optimization of guidance policy.

The adaptive practical optimal time-varying formation tracking problems of the disturbed high-order multi-agent systems with a noncooperative leader are considered. Different from the former achievements, the effects of the leader’s unknown control input and followers’ external disturbances are both considered in the optimal time-varying formation tracking issues. Firstly, an adaptive practical optimal time-varying formation tracking protocol is proposed. The extended state observers and adaptive neural networks are introduced to estimate the integrated uncertainty and value function for the adaptive protocol, respectively. Then, an algorithm is presented to determine the control parameters for the adaptive optimal protocol and neural networks weights update laws. Thirdly, the stability and the optimal formation tracking property are analyzed for the closed loop disturbed high-order multi-agent system. Finally, the numerical simulation results are presented for revealing the effectiveness of the obtained theoretical methods.

This paper investigates the formation control problem for linear multi-agent systems under switching directed topologies. Based on absolute or relative outputs, we propose two distributed observer-based event-triggered control schemes. Both schemes can guarantee the boundedness of formation errors under sufficient conditions. The schemes can also avoid Zeno behaviors by giving an estimation for the lower bound of sampling intervals. Finally, simulations and experiments validate the proposed approaches.

Topology identification of complex networks is an important and meaningful research direction. In recent years, the topology identification method based on adaptive synchronization has been developed rapidly. However, a critical shortcoming of this method is that inner synchronization of a network breaks the precondition of linear independence and leads to the failure of topology identification. Hence, how to identify the network topology when possible inner synchronization occurs within the network has been a challenging research issue. To solve this problem, this article proposes improved topology identification methods by regulating the original network to synchronize with an auxiliary network composed of isolated chaotic exosystems. The proposed methods do not require the sophisticated assumption of linear independence. The topology identification observers incorporating a series of isolated chaotic exosignals can accurately identify the network structure. Finally, numerical simulations show that the proposed methods are effective to identify the structure of a network even with large weights of edges and abundant connections between nodes.

Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative and has a simpler form than those in the existing literature. Then, sufficient conditions are presented to realize synchronization of a complex network (with a target system) within a settling time via three different kinds of simple controllers. In general, controllers designed to achieve fixed-time stability consist of three terms and are discontinuous. However, in our mechanisms, the controllers only contain two terms or even one term and are continuous. Thus, our controllers are simpler and of more practical applicability. Finally, three examples are provided to illustrate the correctness and effectiveness of our results.

These days, the synchronization of multiplex networks is an emerging and important research topic. Grounded framework and theory about synchronization and control on multiplex networks are yet to come. This article studies the intralayer synchronization on a multiplex network (i.e., a set of networks connected through interlayer edges), via the pinning impulsive control method. The topologies of different layers are independent of each other, and the individual dynamics of nodes in different layers are different as well. Supra-Laplacian matrices are adopted to represent the topological structures of multiplex networks. Two cases are considered according to impulsive sequences of multiplex networks: 1) pinning controllers are applied to all the layers simultaneously at the instants of a common impulse sequence and 2) pinning controllers are applied to each layer at the instants of distinct impulse sequences. Using the Lyapunov stability theory and the impulsive control theory, several intralayer synchronization criteria for multiplex networks are obtained, in terms of the supra-Laplacian matrix of network topology, self-dynamics of nodes, impulsive intervals, and the pinning control effect. Furthermore, the algorithms for implementing pinning schemes at every impulsive instant are proposed to support the obtained criteria. Finally, numerical examples are presented to demonstrate the effectiveness and correctness of the proposed schemes.

Time-varying group formation-containment tracking problems for general linear multiagent systems with unknown control input are investigated. Agents are classified into tracking leaders, formation leaders, and followers and assigned in groups. Tracking leaders with unknown control inputs provide unpredictable trajectories as macroscopic moving references. Formation leaders accomplish desired subformations while following the trails of tracking leaders. At the same time, followers converge into different convex hulls spanned by formation leaders. First, formation-containment tracking protocols are designed with neighboring relative information and effects of unknown input of tracking leaders. Then, the design of group division is analyzed by adjusting the properties in Laplacian matrices, which represent interaction relationships. An algorithm to determine the parameters in control protocols is proposed, and the formation tracking feasible constraint is presented. Next, it is proved that the general linear multiagent system can achieve time-varying group formation-containment control effectively with errors uniformly asymptotically converging to zero under designed protocols. Finally, a numerical simulation is given to verify the effectiveness of obtained theoretical results.

In this article, time-varying group formation (TVGF) tracking problems for general linear multiagent systems (GLMASs) with switching interaction topologies are investigated. Different from previous studies, a novel TVGF tracking approach is proposed, where all agents are divided into three types: the virtual leader, the group leader, and the follower. The virtual leader is designed to assign the trajectory of GLMASs. Subgroups can be interacted with each other by cooperation among group leaders such that the relative configuration between different groups can be adjusted simultaneously. The followers in each group can achieve time-varying subformations. Moreover, under the influence of external disturbances and switching topologies, based on the distributed observer, two different distributed adaptive control protocols are constructed without using any global information such as the eigenvalue of the Laplacian matrix related to the communication topologies, the upper boundness of the leader’s input, and so on. The algorithms to determine parameters of control protocols are also presented. Furthermore, the closed-loop stability of GLMASs is proven by the Lyapunov theory. Finally, numerical simulations are given to verify the effectiveness of theoretical results.

With the rapid development of swarm intelligence, the consensus of multiagent systems (MASs) has attracted substantial attention due to its broad range of applications in the practical world. Inspired by the considerable gap between control theory and engineering practices, this article is aimed at addressing the mean square consensus problems for stochastic dynamical nonlinear MASs in directed networks by designing proportional-integral (PI) protocols. In light of the general algebraic connectivity, consensus underlying PI protocols for a directed strongly connected network is investigated, and due to the M -matrix approaches, consensus with PI protocols for a directed network containing a spanning tree is studied. By constructing appropriate Lyapunov functions, combining with the stochastic analysis technique and LaSalle's invariant principles, some sufficient conditions are derived under which the stochastic dynamical MASs realize consensus in mean square. Numerical simulations are finally presented to illustrate the validity of the main results.

Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. A new structure variance loss is introduced to build a bridge between deep encoder-decoders and variance minimization, and provides a new way to minimize the variance by forcing different intermediate decoding outputs (paths) to reach an agreement. We also design a focal weighting strategy to effectively combine multiple losses in a scale-balanced way, so that the supervision information is sufficiently enforced throughout the encoder-decoders. To evaluate the proposed method on the pixel-level estimation task, a novel multipath residual encoder is proposed and extensive experiments are conducted on four challenging density estimation and crowd counting benchmarks. The experimental results demonstrate the superiority of our EDS over other paradigms, and improved estimation performance is reported using our deeply supervised encoder-decoder.

Person re-identification (Re-ID) has been promoted by the significant success of convolutional neural networks (CNNs). However, the application of such CNN-based Re-ID methods depends on the tremendous consumption of computation and memory resources, which affects its development on resource-limited devices such as next generation AI chips. As a result, CNN binarization has attracted increasing attention, which leads to binary neural networks (BNNs). In this article, we propose a new BNN-based framework for efficient person Re-ID (BiRe-ID). In this work, we discover that the significant performance drop of binarized models for Re-ID task is caused by the degraded representation capacity of kernels and features. To address the issues, we propose the kernel and feature refinement based on generative adversarial learning (KR-GAL and FR-GAL) to enhance the representation capacity of BNNs. We first introduce an adversarial attention mechanism to refine the binarized kernels based on their real-valued counterparts. Specifically, we introduce a scale factor to restore the scale of 1-bit convolution. And we employ an effective generative adversarial learning method to train the attention-aware scale factor. Furthermore, we introduce a self-supervised generative adversarial network to refine the low-level features using the corresponding high-level semantic information. Extensive experiments demonstrate that our BiRe-ID can be effectively implemented on various mainstream backbones for the Re-ID task. In terms of the performance, our BiRe-ID surpasses existing binarization methods by significant margins, at the level even comparable with the real-valued counterparts. For example, on Market-1501, BiRe-ID achieves 64.0% mAP on ResNet-18 backbone, with an impressive 12.51× speedup in theory and 11.75× storage saving. In particular, the KR-GAL and FR-GAL methods show strong generalization on multiple tasks such as Re-ID, image classification, object detection, and 3D point cloud processing.

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme.

The missile-target assignment (MTA) is a typical weapon-target assignment problem in Command and Control of modern warfare. Despite the significance of the problem, traditional algorithms still lack efficiency, solution quality, and practicability in the adversarial environment. In this article, we propose a data-driven policy optimization with deep reinforcement learning (PODRL) for the adversarial MTA. We design a comprehensive reward function to motivate the optimization of assignment policy. As such, the learned policy can implicitly model the penetration of missiles under an adversarial environment in a data-driven way. We also present a fair sample strategy to improve the sample efficiency and accelerate the policy optimization. Experimental results show that PODRL can adaptively generate satisfactory solutions in both small-scale and large-scale instances. Furthermore, we evaluate the effectiveness of PODRL in a multiobjective scenario. The result demonstrates that a well-optimized policy can achieve high-quality allocation and demand forecast of the missile resources simultaneously.

In this study, the security problem of discrete nonlinear systems with injection attacks is discussed by designing two kinds of iterative learning schemes with the partial information and delay. The definitions of security and insecurity are presented and the injection attack is considered in a discrete nonlinear system. The contribution of this study is twofold: 1) two kinds of iterative learning schemes are designed to be with the partial information and delay due to limited bandwidth of networks and 2) two criteria are proposed to analyze the security and insecurity of a discrete nonlinear system with the two kinds of iterative learning schemes. Finally, numerical results are presented to illustrate the validity of the obtained criteria.

Since the speed that the$p$th moment of an output signal norm tends to 0 can be used to measure the quality of the output signal, the stability of the$p$th moment of complex networks has been widely concerned. This article studies fixed-time synchronization in the$p$th moment for time-varying delay stochastic multilayer networks, where fixed-time synchronization of multilayer networks is realized by using nonlinear feedback or delay feedback, and the concrete expression of fixed settling time is evaluated. Compared with some existing fixed-time control methods, the optimal combination relationship between the exponential power of fixed-time controllers and the$p$th moment is given, and the designed controllers are continuous function. Finally, the effectiveness of the method is verified by numerical simulations.

1-bit detectors show great promise for resource-constrained embedded devices but often suffer from a significant performance gap compared with their real-valued counterparts. The primary reason lies in the error during binarization. This paper presents a layer-wise searching (LWS) strategy to generate 1-bit detectors that maintain a performance very close to the original real-valued model. The approach introduces angular and amplitude loss functions to increase detector capacity. At 1-bit layers, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework. We also learn the scale factor by minimizing the amplitude loss in the same student-teacher framework. Extensive experiments show that LWS-Det outperforms state-of-the-art 1-bit detectors by a considerable margin on the PASCAL VOC and COCO datasets. For example, the LWS-Det achieves 1-bit Faster-RCNN with ResNet-34 backbone within 2.0% mAP of its real-valued counterpart on the PASCAL VOC dataset.

The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a priori. Although DPDP plays an important role in modern logistics and supply chain management, state-of-the-art DPDP algorithms are still limited on their solution quality and efficiency. In practice, they fail to provide a scalable solution as the numbers of vehicles and sites become large. In this paper, we propose a data-driven approach, Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve industry-scale DPDP. In our method, the delivery demands are first forecast using spatial-temporal prediction method, which guides the neural network to perceive spatial-temporal distribution of delivery demand when dispatching vehicles. Besides, the relationships of individuals such as vehicles are modelled by establishing a graph-based value function. ST-DDGN incorporates attention-based graph embedding with Double DQN (DDQN). As such, it can make the inference across vehicles more efficiently compared with traditional methods. Our method is entirely data driven and thus adaptive, i.e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically. We have conducted extensive experiments over real-world data to evaluate our solution. The results show that ST-DDGN reduces 11.27% number of the used vehicles and decreases 13.12% total transportation cost on average over the strong baselines, including the heuristic algorithm deployed in our UAT (User Acceptance Test) environment and a variety of vanilla DRL methods. We are due to fully deploy our solution into our online logistics system and it is estimated that millions of USD logistics cost can be saved per year.

Industrial Internet of Things (IIoT), as an important industrial branch of the Internet of Things (IoT), has an essential purpose to improve intelligent industrial production. For this purpose, IIoT big data should be efficiently processed to mine valuable information. In handing the IIoT big data, cloud-edge computing is getting more attention to reduce the interaction latency to meet the real-time requirement, especially in the field of prognostic and health management (PHM). It is expected that artificial intelligence (AI) technologies will significantly change the manner of processing IIoT big data. Therefore, new methods about PHM, combining cloud-edge computing with AI technologies, are required to process the IIoT big data for intelligent industrial manufacturing. As an essential element of PHM, predicting the remaining useful life (RUL) of industrial equipment plays an increasingly crucial role, especially for industrial intelligence. However, traditional methods pay much attention on prediction accuracy and neglect the influence of computing time. In this article, by combining cloud-edge computing with AI technology, a new data-driven method, namely, cloud-edge-based lightweight temporal convolutional networks (LTCNs), for RUL prediction is proposed. First, to meet the real-time requirement, a cloud-edge computing and AI-based framework for RUL prediction is presented. Second, a new model structure named LTCN is proposed and applied in the framework. Real-time prediction results will be obtained in the edge plane and higher accuracy prediction results will be obtained through historical information in the cloud plane. Third, an incremental learning approach based on updating partial parameters of LTCN is discussed to improve the accuracy of prediction models with newly collected data. Experiments show that our method can improve the prediction accuracy and reduce the computational time of RUL.

The article pays attention to a two-layer multi-weighted network, and studies finite-time (FT) intra-/inter-layer quasi-synchronization of two-layer multi-weighted networks. Firstly, FT stability and quasi-stability theorems of dynamical system are discussed, and the results show that the maximum convergence time of FT quasi-stability is smaller than that of FT stability for the dynamic system. Secondly, novel sufficient criteria are gained for FT intra-/inter-layer quasi-synchronization of two-layer multi-weighted networks. Thirdly, the relationship among multiple weights number, the topological structure, inner coupling modes, coupling strengthes and across layers are established. In particular, the results show that the smaller multiple weights number do not necessarily lead to faster quasi-synchronization, and the multiple weights number may have a positive feedback effect on FT intra-/inter-layer quasi-synchronization. When the multiple weights number is greater than a certain value, FT intra-layer quasi-synchronization and FT inter-layer quasi-synchronization of networks can be realized simultaneously, and the conditions of taking the minimum convergence time are also given. Finally, numerical examples verify the effectiveness of the proposed method.

Large-scale risk events in the venture capital (VC) market can easily lead to systemic risk in the financial market. This paper collects the comprehensive dataset of Chinese VC market from 1999 to 2020, and constructs the multi-layer networks of VC market. After that, a risk contagion model of venture capital market is designed considering both the capital loss transmission and social relations effect (investor herd behavior). By simulating various situations with different risk origins and scales, the speed of risk contagion and the total impacts on the overall stability of the market (network) are compared. We find that the herd behavior of the market will generally aggravate the consequences of risk contagion. Compared with unicorns, the failure of a leading VC firm can cause a wider range of risk contagion. The contagion consequences caused by the failure of random financing companies are more serious than those caused by random VC firms. Moreover, we identify that telecommunications services and information technology are high-risk industries in VC market. Based on the empirical results, this article provides policy implications for the market regulatory sectors to prevent VC market risks.

Venture capital market is one of the most important financial markets, which plays an important role in promoting industrial innovation and economic development. In this study, the complete data set of all venture capital events occurred in China from 2009 to 2020 was used to construct two kinds of complex networks, whose topological property and dynamic evolution trend of the network are studied. Specifically, we construct the co-investment network among investor, as well as the binary complex network of investor-entrepreneur, and propose the evaluation method of the importance and performance of venture capital institutions. Based on the proposed method, we identify some leading venture capital institutions with high performance and importance. Furthermore, this paper enlightens venture capital practitioners by discovering the investment behavior and preference of these leading institutions.

In reality, individuals will spread their opinions by word of mouth, meanwhile sharing their opinions on social platforms. To gain a clear insight into this kind of behavior, we propose a diffusion model of various opinions in a two-layer interconnected network using some statistical characteristics of network structures. Theoretical analysis reveals that the final fraction of any opinion in one layer will get identical to that of the same opinion in the other layer. In particular, when the seed fraction of an opinion in one layer is different from that in the other layer, the diffusion behavior and final prevalence of opinions not only rely on the seed fractions of opinions, but also depend on the attributes of individuals that are active on different layers, including their inter-layer linking patterns and linking number. Further analysis shows that mass media will promote the spread of the opinion that is in accordance with its own. Finally, we illustrate the effectiveness of analytical results by simulating the spread of two opinions under four inter-layer linking patterns on three types of two-layer interconnected networks. The findings throw new light on some interesting phenomena in society, and facilitate decision-makers to orientate the prevalence of a special product.

Predefined mymargin finite-time output containment control problem for nonlinear multi-agent systems with multiple dynamical leaders under directed topology is investigated, where the outputs of followers can converge to the predefined convex hull formed by the multiple leaders within a finite time, and the leaders can have unknown control inputs. Firstly, for the directed topological structure among the followers, a distributed adaptive observer is designed to estimate the whole states of all the leaders under the influences of the leaders' unknown inputs. By utilizing Hardy's inequality and common Lyapunov theory, the finite-time convergence of the proposed observer is proved. On the basis of this conclusion, a predefined distributed containment control protocol including the desired convex combinations of the leaders is developed for each follower by using the given weights. Then an algorithm is proposed to design the control parameters in the proposed containment control protocol. With the help of the output regulation theory, the finite-time output containment criterion for nonlinear multi-agent systems in the presence of the leaders' unknown inputs is derived. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the theoretical results.

This paper investigates the formation control problem for a group of nonholonomic mobile robots (NMRs) with unknown parameters and deception attacks. The information transmitted among different robots is represented by a directed graph and only a subset of the robots can obtain the full information of the desired trajectory directly. For those robots which cannot access to the reference trajectory directly, distributed estimators are designed to estimate the unknown trajectory information by using only locally available information. Besides, the sensor-to-controller transmitting channels and the communication among connected robots are suffering from deception attacks. Adaptive laws and compensation terms are designed to handle the issue of attack-induced uncertainties. Then, a novel distributed resilient formation control scheme is designed, based on which a sufficient condition is developed to guarantee that the formation errors converge to a compact set and all the closed-loop signals are bounded. Experimental results are given to validate the theoretical studies.

Over the past decades, the synchronization of complex networks with directed topologies has received considerable attention owing to its extensive applications in the realistic world. Design of proportional-integral-derivative (PID) control protocols for achieving synchronization with directed networks is known to be a challenging task. The purpose of this paper is to establish a connection between the PID control protocols and synchronization of complex dynamical networks with directed topologies. Based on the classical complex network model, we investigate global synchronization with PD controller of a balanced strongly connected directed network and global synchronization with PI controller of a strongly connected directed network, and a directed network containing a spanning tree, respectively. Several sets of sufficient conditions are established under which the network reaches global synchronization. The simulation examples are presented to verify the efficiency of the theoretical results.

In this paper, the finite-time output consensus problem is considered for a class of second-order multiagent systems (MASs), where the mismatched disturbance exists in the dynamics of each agent, and the communication topology is directed. First of all, a basic backstepping control protocol is proposed to solve the finite-time consensus problem without mismatched disturbance. Then, a finite-time disturbance observer is designed to estimate the mismatched disturbance, based on which, two adaptive finite-time consensus protocols are proposed to solve the finite-time output consensus and tracking consensus problems without using any global information with respect to the communication topology. Finally, two simulation examples are illustrated to verify the theoretical results.

Fully adaptive practical time-varying output formation tracking issues of high-order nonlinear stochastic multiagent systems with multiple leaders are researched, where the adaptive fuzzy-logic system (FLS) is introduced for estimating the mismatched integrated uncertain items. Distinctive with former results, stochastic noise is considered in the dynamics, and the followers are required for achieving the time-varying output formation tracking in probability of the convex combination of the leaders' outputs. First, a fully adaptive practical time-varying output formation tracking protocol is put forward, which only utilizes the neighboring relative information, and the global interaction topology information is not used. Besides, the designed protocol employs the adaptive FLSs to estimate the mismatched uncertainties of the followers and the leaders, and the uncertain boundary functions of the stochastic noise. Then, the design process of control protocol and parameter adaptive update law is summarized within four steps in an algorithm. Third, the stability and the properties of the proposed protocol and algorithm are analyzed by employing the Lyapunov theories and stochastic stability theories. Finally, numerical simulation results illustrate the effectiveness of achieved protocol and algorithm.

This article is concerned with fixed-time synchronization of the nonlinearly coupled neural networks with discontinuous activation and mismatched parameters. First, a novel lemma is proposed to study fixed-time stability, which is less conservative than those in most existing results. Then, based on the new lemma, a discontinuous neural network with mismatched parameters will synchronize to the target state within a settling time via two kinds of unified and simple controllers. The settling time is theoretically estimated, which is independent of the initial values of the considered network. In particular, the estimated settling time is closer to the real synchronization time than those given in the existing literature. Finally, two numerical simulations are presented to illustrate the effectiveness and correctness of our results.

Over the course of the past two decades, a great deal of researchers has investigated synchronization of networked systems with determined node dynamics. However, in many realistic situations, noise is inevitable in man-made and naturally occurring networked systems. Therefore, dynamical networked systems with stochastic perturbations have gained substantial attention and have been extensively studied both in theoretical research and in practical applications. The primary goal of this paper is focused on the novel synchronization protocol design problem of nonlinear stochastic dynamical networked systems. Sufficient conditions are given to select protocol parameters. By employing stochastic analysis techniques and selecting appropriate Lyapunov functions, we proved that global synchronization of nonlinear stochastic dynamical networked systems can be reached in mean square. Numerical examples are depicted to demonstrate the efficiency of the theoretical results.

Different spreading processes in multiplex networks may interact with each other and display intertwined effects. In this paper, we propose a theoretical framework called infection-probability-dependent interlayer interaction propagation processes in multiplex networks with an arbitrary number of layers, to more precisely depict the intertwined effects which bring challenges to the existing state-dependent interlayer interaction models. Specifically, the spreading rate of each node is regulated by the proposed spreading rate function (SRF) which depends on both the intrinsic dynamics in its layer and the infection probabilities of its counterparts. We propose an algorithm to obtain the spreading threshold of each layer of the proposed theoretical framework. We analyze the three-layer tuberculosis-awareness-flu model with the SRF of each node being the expectation of infection-probability-dependent spreading rate. This paper gives a thorough and detailed numerical investigation of the impact and interaction of system settings and the spreading threshold of each layer. We find that for tuberculosis spreading which is in competing relation with awareness and cooperation relation with flu, the epidemic threshold is a constant when other layers' intrinsic spreading rates are small. The cooperation layer has dramatic influence on the constant while the competing layer has no effect on it.

In this paper, the synchronization problem of complex networks with linearly diffusively coupled nonidentical nodes is investigated. Starting with the boundedness condition of network trajectories, we introduce an invariant set such that it contains all limit points of ultimately synchronous trajectories. Then, we develop a decomposition technique for the heterogeneous network. With this decomposition, the synchronization of the network can be investigated by the convergence of one decomposed network and the synchronization of the other decomposed homogeneous-like network. Moreover, for a particular case that the invariant set is a linear subspace, conditional synchronization analysis is provided to reduce the coupling complexity between the two decomposed networks. It is noted that our decomposition technique is quite simple yet general: by this technique, the synchronization of various heterogeneous complex networks can be transformed into the stability of nonlinear systems and synchronization of homogeneous-like complex networks. Finally, we present several numerical examples to demonstrate the effectiveness of the theoretical results. In particular, we use an example to show that our theoretical procedure is also feasible for some heterogeneous networks with a general invariant submanifold instead of linear subspace.

A great deal of attention from various scientific communities has been recently drawn to complex network systems (CNSs), with many profound results established in this active research field. This article provides a state-of-the-art survey on coordination and control of CNSs with switching network topologies, with emphasis on relationships between the switchings among different topology candidates and the network controllability, and between the switchings among different topology candidates and the emergence of coordination behaviors (including synchronization, consensus, and containment) of such CNSs. First, some fundamental properties of CNSs and the essentials of analytical methodologies for the stability of the fixed point of switched dynamical systems are briefly reviewed. Then, network controllability and the emergence of coordination behaviors of CNSs with switching topologies and the corresponding analytical approaches are discussed in detail, where some of the existing results along these topics are presented in a tutorial-like fashion. This article ends by presenting some interesting future research topics on the coordination and control of CNSs with switching topologies.

In this paper, the cooperative formation control problem is investigated for multiple two-wheeled mobile robots with unknown parameters. By combining the hierarchical decomposition and prescribed performance bound (PPB) technique, distributed adaptive formation controllers are designed based on dynamic surface control. It is proved in the Lyapunov sense that all the closed-loop signals are semi-globally bounded and the tracking error converges to a compact set, which can be made arbitrarily small by adjusting the design parameters appropriately. Furthermore, formation maintenance, connectivity preservation and collision avoidance can be achieved with the proposed control scheme. Simulations are also given to verify the effectiveness of the theoretical results.

With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

This article addresses the leader-following consensus of a class of multi-agent systems (MASs) subjected to saturation. Unlike previous literature, the followers are with heterogeneous dynamics. To solve this problem, we employ the low-gain feedback technique and the parameterized algebraic Riccati equations to design the controllers. For the fixed and switching network topologies, sufficient conditions are put in place to guarantee the semiglobal stability of the consensus error system. Numerical results are also provided to validate the effectiveness of the control design.

Many networked systems display some kind of dynamics behaving in a style with both continuous and impulsive communications. The cooperation behaviors of these networked systems with continuous connected or impulsive connected or both connected topologies of communications are important to understand. This paper is devoted to the synchronization of the networked system with continuous and impulsive hybrid communications, where each topology of communication mode is not connected in every moment. Two kind of structures, i.e., fixed structure and switching structures, are taken into consideration. A general concept of directed spanning tree (DST) is proposed to describe the connectivity of the networked system with hybrid communication modes. The suitable Lyapunov functions are constructed to analyze the synchronization stability. It is showed that for fixed topology having a jointly DST, the networked system with continuous and impulsive hybrid communication modes will achieve asymptotic synchronization if the feedback gain matrix and the average impulsive interval are properly selected. The results are then extended to the switching case where the graph has a frequently jointly DST. Some simple examples are then given to illustrate the derived synchronization criteria.

Complex networks with time-varying nodal parameters are of considerable interest and significance in many areas of science and engineering. Reconstructing networks with unknown but continuously bounded time-varying nodal parameters from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient and feasible, completely data-driven approach to predicting the structures of networks with unknown but continuously bounded time-varying nodal parameters in the presence or absence of noise. In particular, the reconstruction framework is implemented on several different kinds of artificial, two-layer and real complex networks composed of various parameter-varying nodal dynamics. Through numerical simulations, we demonstrate that, networks structures can be fully reconstructed with limited available information and presence or absence of noise, though systemic parameters are continuously time-varying. In addition, our method is also applicable to structure identification of multilayer networks as well as networks with constant nodal parameters. We expect our method to be useful in addressing issues of significantly current concern in the information era, natural networks, and large-scale multilayer networks.

In this paper, a controller with multilayer structure is proposed to regulate the thermostatically controlled loads (TCLs), so that power sharing and comfort states consensus can be achieved. Since TCLs have great potential to reduce the fluctuations caused by photovoltaic in the building microgrid community, the control of a cluster of TCLs has practical significance. The multilayer structure can capture the nature of inner and inter communications among the building microgrids. The self-triggered mechanism is adopted to avoid continuous data transmission. The controller is validated through study of two different building microgrid communities with different number of TCLs.

When implemented in the digital domain with time, space and value discretized in the binary form, many good dynamical properties of chaotic systems in continuous domain may be degraded or even diminish. To measure the dynamic complexity of a digital chaotic system, the dynamics can be transformed to the form of a state-mapping network. Then, the parameters of the network are verified by some typical dynamical metrics of the original chaotic system in infinite precision, such as Lyapunov exponent and entropy. This article reviews some representative works on the network-based analysis of digital chaotic dynamics and presents a general framework for such analysis, unveiling some intrinsic relationships between digital chaos and complex networks. As an example for discussion, the dynamics of a state-mapping network of the Logistic map in a fixed-precision computer is analyzed and discussed.

Based on the classical network model, this paper investigates the PD and PI control for synchronization of the complex dynamic directed network with delayed nodes. We obtain some sufficient conditions for global synchronization. In particular, using derivative and integral protocols are better than using traditional protocols in achieving synchronizaiton. Finally, some simulation examples are given to theoretical results.

Based on structural controllability of complex networks and a constructed gene network with 9,241 nodes for Arabidopsis thaliana, we classified nodes into five categories via their roles in control or node deletion, including indispensable, neutral, dispensable, driver, and critical driver nodes. The indispensable nodes can increase the number of drivers after deletion, which are never drivers or critical drivers. About 10 percent of nodes are indispensable. However, more than 60 percent of nodes are neutral ones. More than 62 percent of nodes are drivers, which indicates the gene network is very difficult to be fully controlled. Gene Ontology (GO) enrichment analysis reveals that different sets of nodes have preferred biological functions and processes. The indispensable nodes are significantly enriched as essential genes, drought responsive and abscisic acid (ABA) independent genes, transcriptional factors (TFs), core cell cycle genes, and ABA and Gibberellin (GA) related genes. The critical drivers are enriched as receptor kinase-like genes, while shorted in WRKY TFs and functional genes that are enriched in the indispensable nodes. Robustness analysis based on node and edge additions, edge rewiring indicate the obtained conclusions are robust to network perturbations. Our investigations clarify control roles of some gene families and provide potential implications for identifying functional genes in other plant species, such as drought responsive genes and TFs.

The problem of distributed formation control for multiple quadrotor aircraft in the form of leader-follower structure is considered in this paper. Based on a nonsmooth backstepping design, a novel consensus formation control algorithm is proposed and utilized. First, for the position control subsystem, based on the linear quadratic regulator optimal design method, a formation control law for multiple quadrotor aircraft is designed such that the positions of all the quadrotor aircraft converge to the desired formation pattern. The designed formation control law for position systems will generate the desired attitude for the attitude control systems. Second, for the attitude control subsystem described by unit quaternion, by employing the technique of finite-time control and switch control, a global bounded finite-time attitude tracking controller is designed such that the desired attitude can be tracked by the multiple quadrotor aircraft in finite time. Finally, numerical example is performed to demonstrate that all quadrotor aircraft converge to the desired formation pattern in the 3-D-space.

In network science and the data mining field, a long-lasting and significant task is to predict the propagation capability of nodes in a complex network. Recently, an increasing number of unsupervised learning algorithms, such as the prominent PageRank (PR) and LeaderRank (LR), have been developed to address this issue. However, in degree uncorrelated networks, this paper finds that PR and LR are actually proportional to in-degree of nodes. As a result, the two algorithms fail to accurately predict the nodes' propagation capability. To overcome the arising drawback, this paper proposes a new iterative algorithm called SpectralRank (SR), in which the nodes' propagation capability is assumed to be proportional to the amount of its neighbors after adding a ground node to the network. Moreover, a weighted SR algorithm is also proposed to further involve a priori information of a node itself. A probabilistic framework is established, which is provided as the theoretical foundation of the proposed algorithms. Simulations of the susceptible-infected-removed model on 32 networks, including directed, undirected, and binary ones, reveal the advantages of the SR-family methods (i.e., weighted and unweighted SR) over PR and LR. When compared with other 11 well-known algorithms, the indices in the SR-family always outperform the others. Therefore, the proposed measures provide new insights on the prediction of the nodes' propagation capability and have great implications in the control of spreading behaviors in complex networks.

As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top-k PSPs using the existing methods, and then we use an idea which is similar to top-k PSPs mining to mine the top-k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top-k NSPs, we introduce two weights, wPand wN, to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP+. To the best of our knowledge, Topk-NSP+is the first algorithm that can mine the top-k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP+is very efficient in mining the top-k NSPs in the sense of computational cost and scalability.

This paper investigates the leader-following consensus of a class of multi-agent systems (MASs) with actuator saturations. Different from the traditional literature, the followers in our paper are governed by nonidentical dynamics. With the help of low gain feedback method and parameterized algebraic Riccati equation, we propose design algorithms of the feedback gains and coupling strength. Sufficient conditions are given to ensure the semi-global stability of the tracking error system. Numerical results are also given to verify the effectiveness of our theoretical method.

Structural controllability of complex networks has been a research focus in recent years. However, few works considered the structural controllability of biological networks, especially for dynamic biological networks. In this paper, structural controllability of one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae) have been investigated. The five dynamic networks included two endogenous and three exogenous ones. We clarified the controllability properties of these networks, as well as explored the differences among different types of networks. Our results revealed that the structural properties of different types of networks are significantly different. We found that the TRNs are rather difficult to be fully controlled. However, the dynamic TRNs were relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous TRNs than the endogenous ones. Based on the structural controllability analysis of subnetworks with the same sizes, as well as randomly perturbed networks by preserving degree distributions, we further illustrated that the structural controllability of the six networks may be mainly dominated by their degree distributions. Our investigations clarify the control properties of TRNs in S. cerevisiae, it provides some insights on real-world control of biological networks.

In this note, the asymptotic consensus tracking problem is addressed for uncertain multi-agent systems (MASs) with undirected communication topologies and a high-dimensional leader, where the uncertainties may contain unmodeled dynamics and external disturbance which are prior unknown. To remove the effect of high-dimensional leader, an observer based compensation controller is firstly designed. A neural-adaptive based feedback controller is then designed. Note that the feedback term contains a discontinuous controller which is used to eliminate the effect of imprecise approximation of neural network. Furthermore, if the leader is assumed to be globally reachable, it is shown that asymptotic consensus tracking is achieved in MAS by choosing appropriate control parameters. The obtained theoretical result is finally validated by simulation.

This paper initiates a systematic methodology for real-time chaos-based video encryption and decryption communications on the system design and algorithm analysis. The proposed system design and algorithm analysis have been validated on an FPGA hardware platform via Verilog Hardware Description Language (Verilog HDL). Based on the fundamental anti-control principles of dynamical systems, a 6-D real domain chaotic system is designed, and then the corresponding Verilog HDL algorithm is developed. The proposed Verilog HDL algorithm is utilized to design a real-time chaos-based secure video communication system, with a generalized design principle derived, which is implemented on an FPGA hardware platform equipped with an XUP Virtex-II chip. Following this line, the designed working mechanism is demonstrated by hardware experiments. The security performance is tested using the TESTU01 statistical test suites, the differential analysis, and the sensitivity of key parameters mismatch. Both theoretical analysis and experimental results validate the feasibility and reliability of the proposed system.

Over the last decades, distributed output regulation problems have received much consideration due to its extensively applications in real world practices. Traditionally, it is assumed that each node obtains the same signal. However, an important observation is that each agent possesses different measurement due to the observability or configuration of the systems. To solve the output regulation problem in this case, we proposed a cooperative output regulation network, where each agent obtains a part of system output measurement on states of plant and exosystem. Distributed state observers and disturbance observers are designed in order to fuse the observed data. Different from traditional literatures, our design shows that even none of the agents can locally reconstruct the state of exosystem, it is still possible to design a networked system to track the reference signal properly. Conditions that guarantee the existence of parameters are given in the cases, where the network topologies are fixed and time-varying, respectively. The simulation results verify our method very effectively.

This paper focuses on the construction of distributed observers in the presence of arbitrarily large communication time delays. In contrast with the traditional centralized observer with the ability to acquire full output of the plant, we design a set of distributed observers, each having access to partial output of the plant through a distributed sensor network. More specifically, each observer obtains partial plant output and communicates with its neighboring observers through consensus protocols. The communication among the network is subject to arbitrarily large time delays. We consider three representative network topologies and for each topology establish conditions to guarantee the observation error systems be exponentially stable. We also consider the design of a pinning synchronization problem as a dual problem of the design of distributed observers. Numerical simulation is carried out to verify the effectiveness of our theoretical analysis.

The second-order Kuramoto oscillator models (KM) with diffusive and sinusoidal coupling were considered intensively in power networks. In this paper, the synchronization of an extended second-order Kuramoto oscillator model is studied using a parameterized approach. It is shown that the synchronization of the extended second-order KM is equivalent to the synchronization of a simple extended first-order KM. Sufficient conditions for synchronizability are established for mean-field type couplings. Simulation studies in oscillator networks as well as in power networks with transmission losses are given to illustrate the effectiveness of the theoretical results.

Over the last two decades, synchronization, as a typical collective behavior in complex networks, has received an increasing attention. To reveal the inherent mechanism of synchronization in complex networks with delayed nodes, this paper aims at developing a novel synchronization approach by using the PD control strategy. Based on a classical network model, we investigate the synchronization of complex networks with delayed nodes under PD control strategy and obtain several sufficient conditions for global synchronization. In particular, the addition of derivative action may make the network achieve synchronization better than the traditional strategies. Finally, a simulation example is provided to verify the effectiveness of the proposed theoretical results.

In the operation of a photovoltaic system, one of the most important issues is absorbing maximum power from the PV array under continuous and rapid changing irradiance condition. The sampling points obtained at different moments are not on the PV characteristic curve with the same irradiance, so the MPPT strategy may misjudge. In this paper, a novel method to track the MPP is presented, which is based on three-point disturbance observation. The proposed algorithm utilizes three operating points that work in different duty cycle, using two points to restore a virtual operating point which is the same PV characteristic curve as the rest of the point. The proposed algorithm suppress the oscillation and misjudgment problem of traditional P&O method. And simulation and experimental results validate the performance of the proposed algorithm under continuous and rapidly changing irradiation conditions.

Complex networks are ubiquitous in nature and society. The functions and features of complex networks are various when these networks have different nodal dynamics and network topologies. Reconstructing networks with high-order nodal dynamics or different system parameter vectors from limited measurable information is a fundamental problem for using and controlling these networks. Based on the Lasso method, we present an efficient and feasible, completely data-driven approach to predict the structures of complex networks in the presence or absence of noise when the systemic parameter is uncertain, that is, the node dynamical parameter vector of network can vary. The numerical simulations indicate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter vector is varying and in the presence or absence of noise, this method is effective and robust.

Distributed practical node-to-node state consensus problem is studied in this paper for a class of two-layer multi-agent systems. It is supposed that there are two layers, i.e., the leaders' layer and followers' layer, in the considered multi-agent systems. Unlike most existing results on distributed consensus of multi-agent systems, the control objective in this paper is to make the states of each follower located on followers' layer track those of its corresponding leader located on leaders' layer. Furthermore, the network topologies of the leaders and the followers may be heterogeneous. Based on the assumption that the states of leaders are uniformly bounded, some sufficient criteria for node-to-node practical consensus are obtained by using differential equation theory.

In the gene regulatory network, the gene regulation loop often occurs with multiple positive feedback, multiple negative feedback and coupled positive and negative feedback forms. In the above gene regulation loops, the auto-activation loop acts are ubiquitous regulatory motifs in complex bio-molecular networks. In this paper, we study the effect of the substrate concentration for gene expression considering an additional positive feedback loop (APFL). First, we find that the substrate concentration can tune the stochastic switch behavior in the additional positive feedback loop. Then, we study the relationship of the substrate concentration with the positive feedback strength in the aspect of the stochastic switch behavior. Furthermore, we also find the gene differentiation phenomenon in the different substrate concentration without colored noise and with colored noise, respectively, when the feedback strength in the additional positive feedback loop (APFL) is sufficiently large.

The design of networked observers has recently received much consideration. This paper is concerned with the design of distributed observers with arbitrarily large communication time delays. A traditional centralized observer assumes access to the full output the plant. In the real world situations, the output is often measured by distributed senors connected through a communication network. We propose the design of a set of distributed observers with time delays in the communication channels. Each observer is regarded as an agent, which obtains local information of the plant and communicates with other observers through an undirected communication network, with arbitrarily large time delays. Conditions are established under which the states of all observers converge exponentially to the plant state. Numerical results are proposed to illustrate our results.

In this paper, the problem of stochastic input-to-state stability (SISS) is investigated for a class of stochastic switched nonlinear systems. A sufficient condition is first derived to estimate an upper bound on a stochastic process. Based on that, the SISS problem is addressed for stochastic nonlinear systems be virtue of the indefinite Lyapuno function approach. For the convenience of simulation, the SISS property of stochastic switched nonlinear systems i further analyzed by the average dwell-time technique. An illustrative example together with numerical simulatio s is presented to demonstrate the efficiency of the proposed results.

Consensus of multiagent systems (MASs) is an intriguing topic in recent years due to its widely used application in robotics, biology, computer, and social science. In the real world, the evolution of MAS is inevitably involved in dynamical environments and the recent development of MAS calls for novel tools for the analysis of MAS with dynamic topology. In addition, the interactions between agents are generally nonlinear and environmental noises are ubiquitous in the communication channels between agents. However, the existing investigation on MAS places little attention on nonlinear models and the inner relationship between external disturbance and consensus is still unclear. Facing these problems, this paper considers an MAS in which the interactions between agents are nonlinear and the communication between agents are infected by environmental noises. By using a novel method of nonsmooth Lyapunov candidate, it has been demonstrated that such an MAS can realize robust consensus under the conditions of jointly (sequentially) connected topology and bounded noises. Finally, simulation results validate the effectiveness of these criteria.

The friction between wheel and track is usually called adhesion force, and it is the critical factor for the movement of trains. On one hand, excessive driving force of a train may lead to insufficient utilization of the adhesion effect and cause wasted energy; on the other hand, insufficient driving force of a train brings inefficient train operation. To balance the issues of energy consumption, operational efficiency, and security, it is necessary to control a train to obtain its maximal adhesion force, particularly in the cases of fast acceleration and emergency braking. However, since engineering experiments indicate a complex nonlinear relationship between the adhesion force and the slip ratio of a train, such a control problem is difficult and challenging, particularly when the optimal slip ratio is unknown. Facing this problem, this paper proposes a novel control method based on the modification of the famous super-twisting sliding mode algorithm, and rigorous mathematical analysis is given to guarantee the ultimate boundedness of the proposed algorithm. Furthermore, by considering four different control scenarios, detailed control and estimation algorithms are both proposed. Simulation result verifies that the proposed control strategy can control the train to obtain its maximum adhesion force.

Multiagent systems (MASs) are ubiquitous in our real world. There is an increasing attention focusing on the consensus (or synchronization) problem of MASs over the past decade. Although there are numerous results reported on the convergence of a discrete-time MAS based on the infinite products of matrices, few results are on the convergence rate. Because of the switching topology, the traditional eigenvalue analysis and the Lyapunov function methods are both invalid for the convergence rate analysis of an MAS with a switching topology. Therefore, the estimation of the convergence rate for a discrete-time MAS with time-varying delays remains a difficult problem. To overcome the essential difficulty of switching topology, this paper aims at developing a contractive-set approach to analyze the convergence rate of a discrete-time MAS in the presence of time-varying delays and generalized coupling coefficients. Using the proposed approach, we obtain an upper bound of the convergence rate under the condition of joint connectivity. In particular, the proposed method neither requires the nonnegative property of the coupling coefficients nor the basic assumption of a uniform lower bound for all positive coupling coefficients, which have been widely applied in the existing works on this topic. As an application of the main results, we will show that the classical Vicsek model with time delays can realize synchronization if the initial topology is connected.

With the rapid development of sensor networks in the last decade, the cooperative design for networked observers has received an increasing attention from engineering community. This paper aims at developing a unified framework for cooperative design of networked observers to stabilize LTI plants. Apart from the traditional centralized design of MIMO system, the proposed cooperative design approach only utilizes the local information of each sensor. For undirected networks, this paper obtains a sufficient and necessary condition for the existence of the parameters that lead to the stabilization of the LTI plant. In particular, we give the detailed design procedures for the parameters of networked observers, including feedback gains and the coupling strength. The numerical simulation is also given to validate the proposed theoretical results.

This paper is concerned with stability analysis of multiple equilibria for recurrent neural networks. A new type of activation function, namely, discontinuous Mexican-hat-type activation function, is proposed for recurrent neural networks. Then with the aid of the fixed point theorem, some sufficient conditions for coexistent multiple equilibria are obtained to guarantee that such n-neuron recurrent neural networks can have at least 4nequilibria. In view of the theory of strict diagonal dominance matrix, further stability analysis reveals that 3nequilibria are locally exponentially stable. The new results considerably improve the existing multistability results in the literature.

Noise can induce various dynamical behaviors in nonlinear systems. White noise perturbed systems have been extensively investigated during the last decades. In gene networks, experimentally observed extrinsic noise is colored. As an attempt, we investigate the genetic toggle switch systems perturbed by colored extrinsic noise and with kinetic parameters. Compared with white noise perturbed systems, we show there also exists optimal colored noise strength to induce the best stochastic switch behaviors in the single toggle switch, and the best synchronized switching in the networked systems, which demonstrate that noise-induced optimal switch behaviors are widely in existence. Moreover, under a wide range of system parameter regions, we find there exist wider ranges of white and colored noises strengths to induce good switch and synchronization behaviors, respectively; therefore, white noise is beneficial for switch and colored noise is beneficial for population synchronization. Our observations are very robust to extrinsic stimulus strength, cell density, and diffusion rate. Finally, based on the Waddington's epigenetic landscape and the Wiener-Khintchine theorem, physical mechanisms underlying the observations are interpreted. Our investigations can provide guidelines for experimental design, and have potential clinical implications in gene therapy and synthetic biology.

A systematic methodology is proposed for a chaotic map-based real-time video encryption and decryption system with advanced Reduced Instruction Set Computer machine (ARM)-embedded hardware implementation. According to the anticontrol principle of dynamical systems, first, an 8-D discrete-time chaotic map-based system is constructed, which possesses the required property of 1-1 surjection in the integer range$[{0,\,N-1}]$, where$N$is the number of frame pixels, suitable for position scrambling of each video frame. Then, an 8-D discrete-time hyperchaotic system is designed for encryption–decryption of red, green, and blue (RGB) tricolor pixel values. Using the ARM-embedded platform super4412 model with Cortex-A9 processor, together with the standard QT cross-platform, an integrated chaotic map-based real-time secure video communication system is designed, implemented, and evaluated. In addition, the security performance of the designed system is tested using criteria from the National Institute of Standards and Technology statistical test suite. The main feature of this method is that, both scrambling–antiscrambling of RGB tricolor pixel positions and encryption–decryption of pixel values are realized simultaneously for enhancing the security. As is well known, compared with numerical simulations, hardware implementation for such a secure video communication system is very difficult to achieve, but we successfully implemented and tested in a real-world network environment. Both theoretical analysis and experimental results validate the feasibility and real-time performance of the new secure video communication system.

Cooperative behaviors are widespread in biological and social populations. Yet the evolution of cooperation is still a puzzle in evolutionary theory. Recent researches have indicated that complex interactions among individuals may promote the evolution of cooperation under weak selection. However, the selection effect on cooperation has not been completely understood. This paper aims to characterize the impact of selection on the emergence of cooperation in evolutionary dynamics on complex networks. By theoretical analysis and numerical simulation, it is found that selection favors defection over cooperation for the birth-death process, while it may favor cooperation over defection for the death-birth process. Furthermore, we come to the condition on which cooperation is dominant over defection. In particular, there exists an optimal selection intensity which favors cooperation the best for the death-birth process. The obtained results indicate that appropriate selection can promote the evolution of cooperation in structured populations under some circumstances.

In traditional control theory, a single observer has access all the measured outputs of the plant to estimates its asymptotically. In many real world engineering systems, it may be difficult to build a single observer that has access to all the measured outputs. One way around this difficulty is to build a network of cooperative observers, each of which obtains a portion of the measurement outputs, that collectively produce an asymptotic estimate of the plant state. In this paper, we construct a network of such observers for a continuous-time linear system. Assuming that these observers are connected through an undirected connected network, we establish a necessary and sufficient condition on the plant parameters under which the network of observers will achieve asymptotic omniscience. A network of cooperative observers is said to achieve asymptotic omniscience if their states all converge to the plant state asymptotically. Numerical simulation results are presented to validate theoretical results. The design of cooperative observers sheds some light on the solution of some other real-world problems, such as the design of networked location-based services and sensor networks.

Strategy selection is a fundamental problem for the evolutionary game process in structured populations. This paper aims at investigating the strategy selection problem by introducing a continuous evolutionary game dynamics on complex networks. It is shown that the population preference for strategies keeps unchanged under random drift in the evolutionary process. However, because of selection, the population will favor one strategy over the other based on the population structure and game payoffs. In particular, for the prisoners dilemma game, our results show that the cooperation is never favored in complete networks. However, it is greatly promoted by cycle networks. The above results are consistent with those in the traditional discrete evolutionary game dynamics. It should be especially pointed out that the proposed framework provides a potential effective tool for analyzing and controlling the evolutionary process in populations.

Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

In this paper, pinning synchronization on complex networks of networks is investigated, where there are many subnetworks with the interactions among them. The subnetworks and their connections can be regarded as the nodes and interactions of the networks, respectively, which form the networks of networks. In this new setting, the aim is to design pinning controllers on the chosen nodes of each subnetwork so as to reach synchronization behavior. Some synchronization criteria are established for reaching pinning control on networks of networks. Furthermore, the pinning scheme is designed, which shows that the nodes with very low degrees and large degrees are good candidates for applying pinning controllers. Then, the attack and robustness of the pinning scheme are discussed. Finally, a simulation example is presented to verify the theoretical analysis in this paper.

Over the last decade, complex networks have emerged to be a promising research field in the area of circuits and systems. This mini-review paper introduces the special session that deals with theory and applications of complex networks and provides brief review of their advances and challenges. The paper further promotes some important research topics in the field with emphasis on the multidisciplinary research interests.

This paper studies second-order consensus in multi-agent systems with sampled position and velocity data. A distributed linear consensus protocol with second-order dynamics is first designed, where both sampled position and velocity data are utilized. A necessary and sufficient condition based on the sampling period, the coupling gains, and the spectra of the Laplacian matrix, is established for reaching consensus of the system in this setting. It is found that second-order consensus in such a multi-agent system can be achieved by appropriately choosing the sampling period determined by a polynomial with order three. In particular, second-order consensus cannot be reached for a sufficiently large sampling period while it can be reached for a sufficiently small one under some conditions. Then, the coupling gains are carefully designed under the given network structure and the sampling period. Furthermore, the consensus regions are characterized for the spectra of the Laplacian matrix. On the other hand, second-order consensus in delayed undirected networks with sampled position and velocity data is then discussed. A necessary and sufficient condition is also given, by which appropriate sampling period can be chosen to achieve consensus in multi-agent systems. Finally, simulation examples are given to verify and illustrate the theoretical analysis.

This paper aims at further investigating the pinning impulsive control of complex dynamical networks. In detail, we introduce a novel approach for analyzing the synchronization stability of complex networks with impulsive signals. Moreover, we prove that one random selective impulsive controller can always pin a directed strongly connected complex network to its homogeneous solution under suitable coupling strength and impulsive signal. A simple example is then given to validate the above theoretical results.

It is well known that the evolutionary dynamics characterizes the process of competition and evolution of phenotypes and behaviors in a population. Intuitively, the individual with a higher fitness will have a higher survival probability, which should be reflected in the evolutionary dynamic model. However, due to the computational complexity of fixation probability, it is very difficult to prove the existence of this property in evolutionary dynamics on complex networks. This paper aims at providing a rigorously theoretical proof for the global existence of such property in the local evolutionary dynamics by using the coupling and splicing techniques. In particular, we also prove that the fixation probability is monotone increasing for the initial nodes set of mutants. Numerical simulations are also given to validate the proposed approaches.

In order to increase the efficiency of photovoltaic (PV) system as much as possible, the maximum power point tracking (MPPT) technology has been paid more attention during the past decades. Among all the maximum power point tracking strategies, the variable step-size MPPT algorithm is widely employed due to its high tracking accuracy and fast speed. However, most of the existing methods are based on perturbation and observation method (P&O), and some essential parameters such as maximum step size and scaling factors are difficult to decide very accurately, due to the fact that the slope of power versus voltage (dP/dU) is asymmetric at the maximum power point (MPP). In order to avoid this defect, a novel variable step-size MPPT algorithm is proposed in this paper, which could adjust the step size online intelligently. Compared with other variable step-size MPPT algorithm, the new method does not need to calculate the maximum step size and speed factor. It can provide a simple and effective way to ameliorate both dynamic and steady state response characteristics. Plenty of theoretical derivation and comprehensive simulation results demonstrate that the new algorithm is much faster and more accurate than those traditional fix step-size methods.

With the widely application of mobile commerce (m-commerce), there is an urgent need to introduce identity authentication mechanism to mobile devices and make m-commerce secure to users. Since fingerprints can be viewed as an important characteristic of a natural person, fingerprint authentication (FA) can provide excellent access control for mobile phone users. Generally speaking, there exist two methods for applying of FA to mobile devices: one solution is trying to introduce an extra sensor to a mobile device, the other solution is trying to authenticate an image captured by a mobile device directly. However, the drawback of the first method is that devices with specific sensors are usually unavailable, the critical difficulty for the second solution comes from minutiae matching since different images would be obtained by a camera with different snap in different positions are different. Facing with these difficulties, this paper trying to provide a possible novel method which combines 3D reconstruction with minutiae matching: reconstruction of 3D minutiae from two 2D images first and then match the obtained image with the reconstructed 3D minutiae.

It is well known that the selection of fixation probability is the fundamental problem for the evolutionary dynamics in structured populations. This paper aims to introduce a general approach for investigating the evolutionary dynamics in a class of structured populations. It includes the evolutionary game dynamics and constant selection dynamics with different asynchronous updating rules, such as ‘birth-death’, ‘voter model’, ‘death-birth’, and ‘imitation’. It should be pointed out that the proposed method provides an effective way to resolve the evolutionary dynamics on general graphs. In particular, it introduces a useful calculating tool to analyze various evolutionary dynamics on small order graphs.

Software systems represent one of the most complex man-made systems. In this paper, we analyze the evolution of Object-Oriented (OO) software using complex network theory from a multi-granularity perspective. First, the software net works are constructed for a multi-version software system at different levels of granularity. Then, some parameters used in complex network theory are introduced to study the topological characteristics of these software networks. By investigating the parameters' values in consecutive software networks, we have a better understanding about software evolution. A case study on an open source OO project, Azureus, is conducted as an example to illustrate our approach. It uncovers some underlying dynamic characteristics of OO systems. These results provide a different dimension to our understanding of software system dynamics and also are very useful for the design and development of OO software systems.

Multi-agent systems are ubiquitous in the world. Recently, multi-agent systems have received increasing attention from mathematics, physics, engineering sciences, and social science communities. This paper firstly introduces several fundamental concepts and then reviews several representative models of multi-agent systems, including the Boids model, Vicsek model, Couzin-Levin model and its invariants, and various complex dynamical networks. Based on these models, it further investigates the dynamical behaviors of multi-agent systems, such as consensus, convergence, adaptation, and consensus decision-making. Moreover, it briefly reviews the main progress in the control of multi-agent systems. Finally, it looks ahead into some important research topics on multi-agent systems, with regard to modelling, analysis, and control.

This paper further investigates the dynamical properties of a class of basic biochemical networks modules - single substrate and single product with no inhibition (SSN) module. It is well known that the biological networks can be modelled by using different kinetics according to their reaction types. This paper introduces the SSN model under the law of mass action and proves that it can admit uniquely globally asymptotically stable positive equilibrium. Moreover, we prove that the SSN module under Hill kinetics can admit uniquely asymptotically stable positive equilibrium. It indicates some potential applications in the understanding of some biological networks modules and the design of some specific network controllers.

Over the last two decades, multi-scroll chaos generation has seen promising advances and becomes an active research field. This paper initiates a novel approach to design various grid multi-wing butterfly chaotic attractors from piecewise Lü system based on switching control and heteroclinic orbit. It should be especially pointed out that these generating multi-wing chaotic attractors are chaotic in the sense of Smale horseshoe from Shilnikov theorem. Moreover, there are some potential engineering applications in the future because of the simplicity of the proposed design approach.

IAM ABOUT to start my second year of service as the Editor-in-Chief (EiC) of the IEEE TRANSACTIONS ON NEURAL NETWORKS (TNN). Needless to say, my first year as the EiC has been full of excitement and challenges. Transitioning this position from my predecessor to me went very smoothly during the months of September 2009 to January 2010. During the past year, we have accumulated 50+ Associate Editors (AEs) handling roughly 600 new submissions (not counting resubmissions and revised submissions). With the help of these AEs and my predecessor, I was quickly able to learn to do my job, and as such, the transition had very few glitches. The easy part of my job is checking whether a submission is in compliance with our guidelines and where it is within the scope of the TRANSACTIONS, before it is assigned to an AE for handling. The difficult part of my job has been dealing with some papers with three or more reviewers, all of whom agreed to review them but for some reason failed to respond to repeated automatic-review reminders. AEs handling these papers have to take several extra steps to remind reviewers through phone calls or e-mails, look for replacement reviewers, or review the papers themselves. Most authors have been appreciative of the work of the AEs and reviewers, and they accept our decisions without a problem. The backlog of papers has been kept short over the last year. We have maintained an organized printing and paperacceptance schedule, with papers typically printed in the journal within 2‐3 months of acceptance. Our page budget has been kept constant in the past few years (roughly 2060 pages per year), and we expect to hold the same page count for next year.

The aim of this paper is to introduce the special session on Recent Advances in Complex Networks: Theories and Applications at ISCAS 2010 by giving a brief outline of the subject and presenting some recent advances in a field of our own interest, i.e., Control and Synchronization of Complex Networks, with special attention to adaptive synchronization and control strategies.

Complex networks are everywhere. The object-oriented software system is a typical example. Based on analyzing evolving structure of the Object-Oriented software systems, we consider accelerating growth of network as power-law growth, which can be easily generalized to real systems as better than linearly growing ones. For scale-free network with preferential linking and increasing links via a power law, we focus on exploring the generic mechanisms of scale-free behavior. We propose a scale-free model that can predict the emergence of scale-free behavior in good agreement with power-law growth and scale-free property, which excel the existing scale-free network models. Moreover, we use the obtained predictions to fit the degree distribution of software network describing scale-free structure with the accelerating growth. The combined analytical and numerical results indicate the emergence of a new set of model that considerably enhance our ability to characterize and model complex evolving networks, which can not only represent software system but also simulate other complex interactive systems, such as the World Wide Web, Internet, social networks and so on.

Complex networks are ubiquitous in the world. Many phenomena in nature can be described by the complex networks, such as brain structures, protein-protein interaction networks, scientific citation networks, food web, social interactions, the Internet, and so on. The study of complex networks is a young and active area of scientific research inspired largely by the empirical investigations of many real-world complex networks such as computer networks and social networks. It is very necessary to briefly review the main advances in the analysis, control and applications of complex networks over the last decade.

This paper deals with the hybrid synchronization problem of two coupled complex networks. By using the linear feedback controller, several useful hybrid synchronization criteria of two coupled networks are obtained based on the Lyapunov stability theory and Lasalle's invariant principle. Analytical results also show that two coupled complex networks can realize hybrid synchronization under suitable conditions. Numerical simulations are then given to verify the effectiveness of the proposed hybrid synchronization scheme.

This paper introduces a novel complex network model to evaluate the reputation of virtual organizations. By using the Lyapunov function and linear matrix inequality approaches, the local synchronization of the proposed model is further investigated. Here, the local synchronization is defined by the inner synchronization within a group which does not mean the synchronization between different groups. Moreover, several sufficient conditions are derived to ensure the local synchronization of the proposed network model. Finally, several representative examples are given to show the effectiveness of the proposed methods and theories.

Software evolution and update play a vital role in software engineering. It has many advantages, such as improving the efficiency of programming, reducing the cost of maintenance and promoting the development of software systems. This paper further analyzes the evolution and update processes of three typical kinds of real-world object-oriented software systems by using the tools of complex networks. It discovers some underlying dynamical evolution characteristics and rules of the object- oriented software systems. These results are very useful for the design and development of the object-oriented software systems.

In real-world complex networks, there exists many uncertain information, such as uncertain topological structures and uncertain system parameters. Without question, the topology identification and parameter identification are two traditionally challenging questions in complex networks. Based on the adaptive observers, our approach can identify the topological structures and system parameters of the uncertain complex dynamical networks together. In particular, our method is also very effective for the complex networks with different node dynamics. Moreover, the proposed approach can be used to monitor the online evolution of network topological structures and system parameters. Finally, several typical simulations are used to verify the effectiveness of the proposed approach.

Over the last decade, complex networks have been intensively studied across many fields, especially in Internet technology, biological engineering, and nonlinear science. This paper will briefly review the main advances in the investigation of complex networks, with emphasis on the recent progress of complex networks in control and synchronization.

This paper proposes a novel multiscroll chaotic system, which is different from Chua’s circuit and all its variants in most aspects of the algebraic form, circuit design, and geometrical structure of the attractor. In particular, the multiscroll attractor of this new system is more complex than that of the generalized Chua’s circuit when they both have the same number of scrolls. The dynamical behaviors of the new system are then analyzed, including the bifurcation diagram and the Lyapunov exponent spectra. Moreover, a module-based circuit diagram is designed for realizing various multiscroll attractors. Finally, experimental circuits are implemented with physical observations reported.

Based on the sawtooth wave function, this paper initiates an approach for generating novel multi-wing butterfly chaotic attractors from the generalized first and second types of modified Lorenz systems. Our theoretical analysis shows that every index-2 saddle-focus equilibrium corresponds to a unique wing in the butterfly attractors. Compared with the traditional ring-shaped multiscroll Lorenz chaotic attractors, the proposed multi-wing butterfly chaotic attractors are much easier to be constructed and implemented by analog circuits. Furthermore, a module-based unified circuit diagram is designed for realizing various multi-wing attractors.

The study of synchronization of population dynamics is extremely important for predicting and evaluating the risk of global extinctions. The migration in a network of patch populations (metapopulation) inevitably involves various environmental noises or outside disturbances, which make the migration is timely evolving and spatially extended. Thus the time-invariant discrete biological networks are often insufficient to capture the key features of real-world biological networks. Here, a time-varying discrete biological network is proposed to characterize the practical metapopulation for the first time. Based on this model, several novel local synchronous criteria are then attained, which provide some new insights into the ecological conservation and biological diversity. Moreover, these synchronous criteria are also applicable to the synchronization of complex networks in other biological and engineering systems.

A novel approach is proposed for generating multidirectional multi-scroll chaotic attractors from the fractional differential systems, including one-directional (1-D) n-scroll, two-directional (2-D) n times m-grid scroll, and three-directional (3-D) n times m times l-grid scroll chaotic attractors. It is the first time in the literature to report the multi-directional multi-scroll chaotic attractors from a fractional differential system. Furthermore, some underlying dynamical mechanics are briefly investigated for the fractional differential multi-scroll systems.

Over the last few decades, complex networks have been intensively studied throughout many fields of science, especially in biological and engineering sciences. This paper briefly reviews the main advances in the complex biological and engineering networks, aiming to bridge the gap between the complex biological and engineering networks. Biologists pay more attention to the mechanisms and local dynamics of individuals, however, engineers are more interesting in the global dynamical behaviors. It is the time for the biologists and engineers to work together for better understanding the complex networks.

This paper further investigates and answers two fundamental questions in the complex dynamical networks: i) how many nodes should a general complex dynamical network with fixed network structure and coupling strength be pinned to reach network synchronization? ii) how much coupling strength should a general complex dynamical network with fixed network structure and pinning nodes be employed to reach network synchronization? In the above framework, the coupling-configuration matrix and the inner-coupling matrix are not necessarily symmetric. Also, the pinning nodes can be randomly selected. Furthermore, our adaptive pinning controllers are rather simple compared with some traditional controllers. Finally, a BA network example is then given to show the effectiveness of the proposed synchronization criteria.

Over the last two decades, generating complex multi-scroll chaotic attractors via simple electronic circuits or simple systems has seen rapid development. This paper provides a brief overview of the subject on multi-scroll chaotic attractors generation, including some fundamental theories and design methodologies.

This paper proposes a novel threshold control approach for creating multi-scroll chaotic attractors. The general jerk circuit is used as an example to show the working principle of this method. The controlled jerk circuit can emerge various limit cycles and n-scroll chaotic attractors by adjusting the upper threshold, lower threshold, and the width of inner saturated plateau. The dynamical mechanism of the threshold control is then further explored by analyzing the system dynamical behaviors. In particular, this method is effective and simple to implement since we only need monitor a single state variable and reset it if it exceeds the thresholds. It indicates the potential engineering applications for various chaos-based information systems.

This paper introduces a novel four-order system, which can generate one-directional (1-D) n-torus, two-directional (2-D) n /spl times/ m-torus, three-directional (3-D) n /spl times/ m /spl times/ l-torus, four-directional (4-D) n /spl times/ m /spl times/ l /spl times/ p-torus chaotic attractors. Furthermore, a novel block circuit diagram is designed for the hardware implementation of multi-directional grid multi-torus chaotic attractors. This is the first time in the literature to experimentally verify a 5 /spl times/ 5 /spl times/ 3 /spl times/ 3-torus chaotic attractors.

A systematic circuit design approach is proposed for experimental verification of hyperchaotic 2, 3, 4-scroll attractors from a generalized Matsumoto-Chua-Kobayashi (MCK) circuit. The recursive formulas for system parameters are rigorously derived for improving the hardware implementation.