This article investigates the mean-square output consensus problem for heterogeneous linear multiagent systems (MASs) over random packet loss channels. Agent heterogeneity is reflected in possibly different state dimensions and dynamic parameters. In addition to heterogeneity, a major challenge arises from relaxing the commonly adopted independent and identically distributed (i.i.d.) assumption on packet losses. To capture temporal correlations that are prevalent in practice, packet losses are modeled by a discrete-time Markov process. Since existing consensus controllers designed for i.i.d. losses may fail under Markovian packet losses, novel dedicated control schemes are developed. Two packet loss scenarios are considered: identical and nonidentical packet losses. For identical packet losses, where all channels drop packets simultaneously, both analytical and numerical consensus conditions are derived to guarantee consensus of the distributed observers. The analytical condition reveals the interplay among packet loss rate, communication topology, and system dynamics, while the numerical conditions are more computationally tractable. An output-regulation-based controller is then designed to achieve mean-square output consensus. For the more general case of nonidentical packet losses, edge Laplacian theory is employed to decouple packet loss processes from the communication topology, leading to consensus conditions for the distributed observers, as well as corresponding controllers that guarantee mean-square output consensus. Finally, numerical simulations are utilized to validate the results.

This article addresses the formation control problem of underactuated multi-autonomous surface vehicles (ASVs) under denial-of-service (DoS) attacks on the communication network and complex uncertainties, including unknown ASV dynamics, external disturbances, and obstacles. First, a novel distributed target estimator (DTE) using a first-order low-pass filter is designed to estimate the state of the target based on partial observability under target information constraints and DoS attacks. Second, a safe guidance law for the ASVs is developed using the estimated target state and a control barrier function. Third, a “dual-adaptation control and learning” bidirectional fusion model is constructed. Specifically, on one hand, a distributed adaptive formation controller based on value decomposition reinforcement learning (VDRL) is designed. By decomposing the global value function, this controller addresses the credit allocation issue in cooperative formation and allows the ASVs to adjust their strategies autonomously based on the environment. On the other hand, an adaptive mechanism is used to improve the computation strategy of the global value function in VDRL, enhancing the learning efficiency and stability of the reinforcement learning (RL) algorithm in complex environments. The proposed VDRL-based formation control algorithm of the underactuated multi-ASVs ensures accurate target state estimation and convergence of formation errors under DoS conditions. A rigorous theoretical analysis is further used to ensure the closed-loop stability of the multi-ASV systems. Finally, simulation results validate the effectiveness of the proposed distributed formation control algorithm.

The rapid advancement of artificial intelligence has significantly enhanced the intelligence of autonomous vehicles (AVs). However, owing to the complexity of AV behavior and the high dimensionality of driving environments, the objective and practical quantitative evaluation of AV intelligence remains a significant and unresolved challenge. This paper proposes a robust training-based comprehensive evaluation (RTCE) system specifically designed to assess the intelligence of AVs in the time dimension. Beginning with a foundation model, the first generation of AVs is developed by training in the initial naturalistic traffic scenarios. To effectively test the intelligence of the AVs, we propose an adversarial trajectory optimization technique to generate challenging, critical test scenarios that evaluate the learning capabilities of AVs in complex environments. Through robust training in these complex scenarios, the second generation of AVs is obtained. To objectively and effectively quantify the intelligence of AVs, we further propose a comprehensive evaluation metric system encompassing five dimensions and 14 evaluation metrics. The intelligence score of each AV is computed using the objective multi-criteria decision-making approach. The proposed intelligence evaluation method is validated using various self-evolution autonomous driving algorithms. The results demonstrate that the RTCE method can quantitatively and effectively test the intelligence of AVs in a multi-dimensional and automated manner. Furthermore, the proposed method is flexible and generalizable, making it adaptable to different testing platforms and autonomous driving algorithms.

This paper aims to investigate the challenging problem of a multi-agent game with multiple pursuers and a single evader in an environment with multiple unknown uncertainties. A coupled approach combining decentralized observers and reinforcement learning (RL) controllers is proposed to deal with this scenario. Firstly, decentralized observers driven by auxiliary control laws are introduced to estimate the states of uncertain systems, with their best responses obtained through the adaptive dynamic programming (ADP) method. The estimated states, which reflect the actual states of the pursuers’ systems, are concurrently transmitted to the RL controllers. Subsequently, the controllers are trained with observer-based heterogeneous-agent proximal policy optimization (OHAPPO) algorithm, in which a novel global multi-function cost is designed. The algorithm utilizes the advantage decomposition for policy updates in the way of credit assignment, resulting in more stable and efficient updates compared to traditional value decomposition. Moreover, to further enhance the performance of both observers and controllers, a sequential game is established between them, where observers’ policies are influenced by controllers’ optimal control and vice versa. Finally, the simulation results verify the effectiveness of the designed OHAPPO algorithm in the pursuit-evasion game.

This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.

This article deals with the observer-based control problem of networked periodic piecewise systems under encoding-decoding frameworks. An encoder with a uniform quantizer, which can compress and encrypt data, is provided to process the measurements from the sensors. The processed data is transmitted over the network to the decoder to recover the original data and then to the remote control station, thereby reducing the communication burden and ensuring data security. Then, by constructing the periodic Lyapunov function with linear interpolation terms, exploiting an effective technique-singular value decomposition-sufficient conditions with linear matrix inequality (LMI) constraints for selecting the observer and controller parameters are derived to achieve the exponentially ultimate boundedness of closed-loop systems. Moreover, to eliminate extra steady-state errors caused by encoding-decoding mechanisms (EDMs), a dynamic quantization factor that can make the asymptotic upper bound tend to zero is designed. Finally, numerical examples are provided to illustrate the effectiveness of the derived theoretical results.

This article investigates the controllability of networked sampled-data systems with various time delays on both control and transmission channels. Necessary and sufficient controllability conditions are first derived for systems with a single delay and then extended to systems with multiple delays. It is found that delays in control signals have no effects on the overall controllability. For a networked system whose topology matrix has only zero eigenvalues, delays of neither control nor transmission signals will affect the overall controllability. It is proved that an uncontrollable mode 1 of such a networked sampled-data system cannot be altered by arbitrary delays. Finally, the networked sampled-data system with first-order holders is discussed, which is modeled as a variant of time-delayed system, and some easy-to-verify algebraic conditions on the controllability are given based on matrix rank checking.

Deep neural networks (DNNs) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this brief, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stability for any compatible neural network parameters. In addition, improved control performance can be achieved by further optimizing neural network parameters. Besides, we provide explicit upper bounds on tracking errors in terms of controller parameters, which allows us to achieve the desired tracking performance by properly selecting the controller parameters. Furthermore, when system models are unknown, we propose an improved Lagrangian neural network (LNN) structure to learn the system dynamics and design the controller. We show that in the presence of model approximation errors and external disturbances, the closed-loop stability and tracking control performance can still be guaranteed. The effectiveness of the proposed approach is demonstrated through simulations.

In this study, the fault-tolerant consensus control (FTCC) challenge is investigated for nonlinear multiagent systems (MASs) in the simultaneous occurrence of abrupt and incipient actuator/sensor faults in the physical level and hybrid Deception/Denial-of-Service (DoS) attacks in the cyber level. For security enhancement and/or safety maintenance purposes, an unknown state and fault decoupling-based augmented estimator is first devised, and a distributed event-triggered FTCC protocol is then developed to achieve strength against hostile attacks and faults, respectively, with the incorporation of augmented state estimation, neighboring sensor fault estimation, and latest successfully triggered output interaction. By constructing dual indicators along with average dwelling time and attack frequency technique, criteria of exponential mean-square consensus of the nonlinear MASs subject to hybrid attacks are obtained. In the end, simulation is outlined to illustrate the efficacy and improvements of the developed event-triggered FTCC methodology.

Autonomous marine vehicles, which provide a platform for the successful implementation of special tasks, such as maritime rescue, maritime measurement, and dangerous goods monitoring, have been widely utilized. In the last decade, considerable attention has been paid to the analysis, modeling, and networked control of autonomous marine vehicles. This survey provides recent advances in networked and DRL-based control for autonomous marine vehicles. Typical mathematical models of autonomous marine vehicles are introduced first as the foundation for control of autonomous marine vehicles. Then, networked and DRL-based control for autonomous marine vehicles is reviewed. Finally, some challenges and open issues are presented to motivate the future research.

False data injection attacks are widely investigated to exploit the cyber-vulnerability of Cyber-Physical System. However, the existing attack policies only consider the cyber-vulnerability in the one-way communication channel. In this letter, a bidirectional stealthy false data injection attack is proposed to bypass the hostile data detector and degrade the estimation performance in the distributed Kalman filtering system. The stealthiness and the influence of the bidirectional stealthy attack is demonstrated by the theoretical analysis. Furthermore, the alternate transmission protocol is proposed to prevent the bidirectional stealthy attack policy. To remedy the protocol and improve the detection sensitivity, an attack detector with multiple factors is proposed. Besides, the choosing principle of the detection factors is analyzed. Simulation examples are presented to investigate the bidirectional stealthy attack and the developed estimator.

In this article, a distributed active disturbance rejection formation tracking control strategy is developed for a quadrotor unmanned aerial vehicle (UAV) swarm system with switching communication topologies. The proposed control strategy consists of two parts: 1) attitude-loop control and 2) position-loop control. First, a distributed cascade active disturbance rejection control (CADRC) method is designed for the attitude subsystem. With this attitude control method, the stability of the attitude subsystem can be achieved even in the presence of unknown time-varying disturbances. Second, a distributed formation tracking control method is designed for the position subsystem. This position control method ensures that the quadrotor UAV swarm maintains dynamic formation flying and accurately tracks a predetermined trajectory. Through stability analysis, it can be proved that the proposed control strategy can ensure the stability of the whole swarm system. Finally, the proposed control strategy is applied to a quadrotor UAV swarm system to verify its effectiveness and the ability to suppress the influence of unknown time-varying disturbances.

The controllability analysis of networked systems is challenging due to their high dimensionality and complex structure. The influence of sampling on network controllability is rarely studied, making it an important topic to explore. In this article, the state controllability of multilayer networked sampled-data systems is studied, considering the deep network structure, multidimensional node dynamics, various inner couplings, and sampling patterns. Necessary and/or sufficient controllability conditions are proposed and validated by numerical and practical examples, requiring less computation than the classic Kalman criterion. Single-rate and multirate sampling patterns are analyzed, showing that adjusting the sampling rate of local channels can affect the controllability of the overall system. It is shown that the pathological sampling of single-node systems can be eliminated by an appropriate design of interlayer structures and inner couplings. In the case of systems with drive-response mode, the overall system may not lose controllability even when the response layer is uncontrollable. The results demonstrate that mutually coupled factors collectively affect the controllability of the multilayer networked sampled-data system.

This study investigates the distributed secure control problem of multiple unmanned surface vehicles (USVs) in the presence of wave-induced disturbances and unified abrupt and incipient rudder angle faults in physical layer, and aperiodic Denial-of-Service (DoS) attacks in cyber layer. Multi-USVs with rudder angle fault and DoS attack modeling are first established. Then, the decentralized unknown input observer (UIO)-based fault estimation and distributed secure control approach is developed in a co-designed framework for multi-USVs with cyber–physical threats. Advantages of the proposed secure scheme are: 1) actuator faults, DoS attacks, and event-triggering strategies with varying action instants, durations, and locations are synchronously addressed and 2) the criteria of exponential consensus are derived by virtue of attack frequency and average dwelling time technique without prior knowledge of unknown wave-induced perturbation bounds and elimination of Zeno behavior in an event-based mechanism. Comparative simulations outline the performance and advantage of the proposed distributed secure control algorithm.

Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a bifunctional push-grasping synergistic strategy for goal-agnostic and goal-oriented grasping tasks. Our method integrates pushing along with grasping to pick up all objects or pre-assigned goal objects with high action efficiency depending on the task requirement. We introduce a bifunctional network, which takes in visual observations and outputs dense pixel-wise maps of$Q$values for pushing and grasping primitive actions, to increase the available samples in the action space. Then we propose a hierarchical reinforcement learning framework to coordinate the two tasks by considering the goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce the training difficulty of the hierarchical framework, we design a two-stage training method to train the two types of tasks separately. We perform pre-training of the model in simulation, and then transfer the learned model to the real world without any additional real-world fine-tuning. Experimental results show that the proposed approach outperforms existing methods in task completion rate and grasp success rate with less motion number. Supplementary material is available at https://github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks.

In this article, the problem of decentralized event-triggered fault-tolerant control (FTC) for a class of interconnected nonlinear systems with unknown strong coupling and actuator failures is considered. In order to enable each subsystem output to track the desired trajectory, a new decentralized adaptive control scheme is given. First, an event-triggering mechanism is introduced to reduce the signal transmission frequency between the controller and the actuator. Second, a fuzzy high-gain observer is designed for each subsystem to estimate unknown nonlinear functions and actuator efficiency factor. Third, a decentralized FTC strategy is proposed to compensate for the effects of actuator failures and achieve the desirable system tracking performance. With the aid of graph theory, it is shown that all the closed-loop signals are semiglobally uniformly ultimately bounded, and the tracking error of each subsystem can converge to an arbitrarily small residual set by adjusting a design parameter. The effectiveness of the proposed scheme is demonstrated by a practical interconnected system.

Testing and evaluation are critical steps in the development and deployment of autonomous vehicles (AVs). This paper aims to provide an online adaptive generation framework for critical boundary driving scenarios (CBDS) with flexible complexity and diversity. Both static scenarios and dynamic scenarios are comprehensively investigated based on real traffic flow data, where the generated static scenarios contain variables of drivable area, weather visibility, and road friction coefficient on autonomous vehicles, and the generated dynamic scenarios consider the effects of mixed traffic streams of AVs, human-driven vehicles, bicycles, and pedestrians. Two complexity models are proposed to characterize the complexity of static scenarios and dynamic scenarios separately, which help to construct feedback for the adaptive generation of CBDS. The scenario-based test is carried out on a joint simulation platform, and the multi-dimensional evaluation system, including safety, driving comfort, driving performance, and traffic coordination, is developed to assess the performance of AVs. Based on the proposed complexity models and multi-dimensional evaluation system, the generation of CBDS is transformed into online environmental parameter optimization. Moreover, the naturalistic driving scenarios generation, scenario complexity calculation, intelligent driving algorithm execution, and intelligent driving evaluation are all concatenated together to form a closed loop so as to adaptively generate critical boundary driving scenarios. Extensive simulations are conducted at the intersection with two different types of intelligent driving algorithms, which show the effectiveness of the proposed framework.

It is common for communities in real-world networks to possess hierarchical and overlapping structures, which make community detection even more challenging. In this paper, by investigating consensus process of the classical DeGroot model in opinion dynamics, we propose a novel method based on the cumulative opinion distance (COD) to discover hierarchical and overlapping communities. It is shown that this method is different from those classical algorithms relying on static fitness metrics that depict the inhomogeneous connectivity across the network. The proposed method is validated from two aspects. First, by estimating the eigenvectors of adjacency matrices, we investigate the detectability limit of our algorithms on random networks, which together with the results concerning the convergence speed of consensus guarantees the performance of our method theoretically. Second, experiments on both large scale real-world networks and artificial benchmarks show that our method is very effective and competitive on hierarchical modular graphs. In particular, it outperforms the state-of-the-art algorithms on overlapping community detection.

Understanding intra-urban human mobility patterns and their potential driving forces are vital to city planning and commercial site selection. In this paper, we first investigate the functions of urban regions and how different region types dynamically influence people’s trip decisions. Furthermore, we characterize urban circadian rhythms by time-vary inter-regional transition probabilities between these regions with different functions, and integrate them into intervening opportunity model to predict human mobility. Public transportation card data in Shanghai are used to demonstrate the effectiveness of the model in terms of station passenger flows, travel time and trip flux. By taking regional function into consideration, the proposed model significantly improved the prediction accuracy. Quantitative analysis ulteriorly indicates that trip intentions and regional features are critical elements in trip flux prediction, especially in the afternoon and evening when people have an abundance of opportunities to travel by their own volition. When the function of a certain region changes, our model is able to make reasonable predictions accordingly. The results indicate the importance of considering individual travel motivation and regional function in modeling human mobility. The proposed model could serve as a guide for popularity and trip flux prediction in urban planning and reconstruction.

Grasping objects in cluttered scenarios is a challenging task in robotics. Performing pre-grasp actions such as pushing and shifting to scatter objects is a way to reduce clutter. Based on deep reinforcement learning, we propose a Fast-Learning Grasping (FLG) framework, that can integrate pre-grasping actions along with grasping to pick up objects from cluttered scenarios with reduced real-world training time. We associate rewards for performing moving actions with the change of environmental clutter and utilize a hybrid triggering method, leading to data-efficient learning and synergy. Then we use the output of an extended fully convolutional network as the value function of each pixel point of the workspace and establish an accurate estimation of the grasp probability for each action. We also introduce a mask function as prior knowledge to enable the agents to focus on the accurate pose adjustment to improve the effectiveness of collecting training data and, hence, to learn efficiently. We carry out pre-training of the FLG over simulated environment, and then the learnt model is transferred to the real world with minimal fine-tuning for further learning during actions. Experimental results demonstrate a 94% grasp success rate and the ability to generalize to novel objects. Compared to state-of-the-art approaches in the literature, the proposed FLG framework can achieve similar or higher grasp success rate with lesser amount of training in the real world. Supplementary video is available at https://youtu.be/KTGj1fGU6ho.

Overtaking the lead vehicle on two-way roads in the presence of several oncoming vehicles is a complex task for autonomous vehicles. In this paper, we formulate the overtaking behavior of an ego vehicle based on a deep reinforcement learning (DRL) method. First, a two-way urban road is created, wherein the ego vehicle aims to reach the destination safely and efficiently while considering multiple traffic participants. We use different intelligent driver model (IDM) parameters to account for different drivers' habits. Furthermore, we introduce different responses of other vehicles when the ego vehicle takes overtaking maneuver. Then, a hierarchical control framework is proposed to manage vehicles on the road, which supervises vehicle behaviors at the high layer and controls the motion at the lower layer. The DRL method named Proximal Policy Optimization is applied to derive the high-level decision-making policies. A self-attention mechanism is further introduced to improve the performance of our algorithm. Finally, the overtaking maneuvers of the ego vehicle in different training timesteps are analyzed and how the responses of other vehicles affect the ego one's overtaking behavior is investigated. Simulation results show that our approach can achieve good performance to deal with the two-way road autonomous overtaking task. Supplementary video is available at https://youtu.be/jPEGjM7cBuk.

This article refocuses on the global coordination of networked systems with input saturation and external disturbances. With the help of a novel backstepping approach, a multihop relay control algorithm subject to a group of modified heterogenous saturation functions is constructed, in which the number of the hop is equal to the order of the networked system. It is proved that these modified heterogenous saturation functions can develop a global unsaturated control algorithm for each agent. Then, some sufficient and necessary conditions are provided for the global consensus of the connected networked systems with input saturation in the absence of external disturbances. In the presence of the input saturation and external disturbances, a sufficient condition irrelevant to the interactive network topology information is proposed for the global swarm of connected networked systems. Finally, numerical simulations are provided to verify the theoretical results.

In this paper, an algorithm on circular circumnavigation of nonholonomic agents is proposed. The agents are required to enclose a static target with predefined radius and circumferential speed. The algorithm completely relies on local bearing angle measurement. Cyclic pursuit is adapted to coordinate the agents and generate an even formation at the circle. Theoretical analysis on the stability of algorithm is given. The applicability and effectiveness of the algorithm is testified with simulations on the unmanned surface vessel (USV) platform.

In this article, we take the coordination control problem of linear time-invariant networked systems with uncertain additive disturbance and uncertain initial states into consideration. A distributed interval observer is first constructed for uncertain networked systems in which the control algorithm of each agent involves only the upper bound information and the lower bound information of the interval observer associated with itself and its neighbors, respectively. With the help of the cooperativity theory, it is proved that the interval observer can estimate the piecewise state for each agent and the interval-observer-based control algorithm can drive the uncertain system to achieve coordination behavior. Then, time-varying coordinate transformation is introduced to construct a novel interval observer which can eliminate the cooperativity premise on the system matrices and bound the states of all agents in real time. It is shown that the novel interval-observer-based control algorithm can guide the uncertain system to reach coordinated behavior. Finally, the numerical simulations are provided to verify the theoretical results.

Time-delayed interactions are of vital importance in analysis and control of real networked systems. As for the limited noisy observations, data-driven modeling of these complex time-delayed systems is a central and challenging topic in numerous fields of science and engineering. Due to nonuniform lags usually embedded in the real-world systems, the inclusion of all lagged components would result in the false causal analysis. In this paper, based on data-fusion strategy, we put forward a novel approach for identifying nonlinear continuous time-delayed dynamical systems with nonuniform lags, termed Feature Selection Nonlinear Conditional Granger Causality (FSNCGC). In detail, rather than treating all the lagged components equally, we present a feature selection method based on information theory to select the candidate lagged components of driving variables, which minimizes the criterion of the mean conditional mutual information between unselected lagged components and target variable. Moreover, for each target variable, we just consider the specific selected lagged components for nonlinear conditional Granger causal analysis with F-test judgement. Finally, we apply our proposed method to a canonical nonlinear continuous time-delayed dynamical system. All of the results demonstrate that our proposed method performs well and provides a viable perspective for time-delayed network reconstruction.

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

This paper addresses the robust semiglobal coordinated control of multiple-input multiple-output multiagent systems with input saturation together with dead zone and input additive disturbance. Observer-based coordinated control protocol is constructed, by combining the parameterized low-and-high-gain feedback technique and the high-gain observer design approach. It is shown that, under some mild assumptions on agents' intrinsic dynamics, the robust semiglobal consensus or robust semiglobal swarm can be approached for undirected connected multiagent systems. Then, specific guidelines on the selection of the low-gain parameter, the high-gain parameter, and the high-gain observer gain have been provided. At last, numerical simulations are presented to illustrate the theoretical results.

We consider a model of competition on complex networks, in which two competitors are fixed to opposite states while other agents, called normal agents, adjust their states according to a distributed consensus protocol. Suppose that one of the competitors could enhance its influence by creating new links. A natural question is, when the number of new links is limited due to the limited resource, how to add these links so as to maximize the influence of the given competitor over the other one (called competitiveness). We consider two competitiveness maximization problems: Problem 1 tries to maximize the number of supporters of the competitor, while Problem 2 tries to maximize the total supporting degree of normal agents toward the competitor. We prove that Problem 1 is NP-hard. We also show that the objective function of Problem 2 is monotonous and submodular, and hence there exists a polynomial-time greedy algorithm (GA) approximately solving Problem 2. Several centrality-based heuristic algorithms of less computational burden are also designed to provide approximate solutions to these two problems. We carry out extensive simulations to check the performances of these algorithms in six real networks. We find that GA always provides the best approximate solution to Problem 2, while for Problem 1, GA only has the best performance in directed networks. Furthermore, among those heuristic algorithms, an algorithm based on centrality in descending order is better than its counterpart in ascending order in solving Problem 2 in statistical sense. But for Problem 1, the performance of the centrality-based heuristic algorithms is more sensitive to the network structure and the locations of competitors.

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impression-level data, such as user cookies, encourages user behaviour targeting, and hence has significantly improved the effectiveness of ad campaigns. However, a fundamental drawback of RTB is its instability because the bid decision is made per impression and there are enormous fluctuations in campaigns' key performance indicators (KPIs). As such, advertisers face great difficulty in controlling their campaign performance against the associated costs. In this paper, we propose a feedback control mechanism for RTB which helps advertisers dynamically adjust the bids to effectively control the KPIs, e.g., the auction winning ratio and the effective cost per click. We further formulate an optimisation framework to show that the proposed feedback control mechanism also has the ability of optimising campaign performance. By settling the effective cost per click at an optimal reference value, the number of campaign's ad clicks can be maximised with the budget constraint. Our empirical study based on real-world data verifies the effectiveness and robustness of our RTB control system in various situations. The proposed feedback control mechanism has also been deployed on a commercial RTB platform and the online test has shown its success in generating controllable advertising performance.

Recent literature on flocking in multi-agent systems show that a minority of informed agents in a group of dynamic agents can influence a majority to follow a virtual leader. However, it is not reported how to select these informed agents from the group in order to increase the number of agents which will eventually follow the virtual leader. In this paper, we propose a cluster-based informed agents selection method to achieve this objective. The proposed method enables us to select informed agents such that they are spatially evenly distributed within the group of agents. We carried out extensive simulations to analyze performances of the proposed method against the traditional random-based informed agents selection method. Simulation results show that the proposed method can increase the number of agents which eventually follow the virtual leader for a given number of informed agents. Therefore, the proposed cluster-based informed agents selection method is useful for leading the majority of a group with less number of informed agents.

Existed works on consensus in networks have been focused on reaching an agreement among states of nodes in a network. In this work, we propose a discrete-time edge consensus protocol for complex networks. By mapping the edge topology into a corresponding line graph, we prove that consensus can be achieved among the states of all edges in a connected network. Theoretical analysis and simulation results are provided to show the effectiveness of the model and the influence of network topology.

This paper investigates a social learning model, in which each individuals' neighbors are composed by two parts: one consists of those who have similar beliefs; the other consists of those picked up according to certain probabilities. Each individual updates her beliefs by combining the Bayesian posterior beliefs based on her private signals and weighted averages of the beliefs of her neighbors. It is shown that the whole group may be divided into clusters by only communicating with those having similar beliefs, especially when bound of confidence is relatively small. Adding probabilistic neighbors guarantees the whole group achieving consensus effectively, which means communicating beyond the difference of beliefs may improve social learning.

In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or without an adaptive strategy, which require global information of the underlying network such as the eigenvalues of the coupling matrix of the whole network or a centralized adaptive control scheme, we propose a novel decentralized adaptive pinning-control scheme for cluster synchronization of undirected networks using a local adaptive strategy on both coupling strengths and feedback gains. By introducing this local adaptive strategy on each node, we show that the network can synchronize using weak coupling strengths and small feedback gains. Finally, we present some simulations to verify and illustrate the theoretical results.

We study a discrete-time model of opinion dynamics in which each agent has its own confidence and influence radius. We investigate the influence of heterogeneity in confidence/influence distribution on the behavior of the network. We consider three cases: 1) each agent has the same confidence and influence radius; 2) each agent's confidence radius is inversely proportional to its influence radius; 3) each agent's confidence radius is not related to its influence radius. We find that heterogeneity does not always promote consensus, and there is an optimal heterogeneity so that the size of the largest consensus cluster reaches maximum.

This paper investigates a distributed tracking problem for multi-agent systems with a varying-velocity leader. The leader modeled by a double integrator can only be perceived by followers located within a sensing distance. The objective is to drive the followers with bounded control law to maintain connectivity, avoid collision and further track the leader, with no need of acceleration measurements. Two cases are considered: the acceleration of the leader is bounded; and the acceleration has a linear form. In the first case, the relative velocities of neighbors are integrated and transmitted as a new variable to account for the uncertain time-varying acceleration. In the second case, two distributed estimators are added for the leader's position and velocity. Simulations are presented to show the effectiveness of the proposed control laws.

Efficient routing is essential in everyday life. Although various hierarchical algorithms exist for computing shortest paths, their heavy precomputation/storage costs and/or query costs hinder their application to large road networks. By detecting a hierarchical community structure in road networks, we develop a community-based hierarchical graph model that supports efficient route computation on large road networks. We then propose a new hierarchical routing algorithm that can significantly reduce the search space over the conventional algorithms with acceptable loss of accuracy. Experimental results on a New York road network demonstrate the performance of the algorithm.

The paper concerns with the problem of asynchronous gossip-based dynamic averaging estimation with communication constraints, where each agent estimates the local time-varying parameters individually, then random pairs of connected agents iteratively and locally perform a pairwise average of their estimations through quantized information communication. How quantization affects the evolution of the gossip-based dynamic averaging estimation algorithm is investigated. We prove that the agents' states converge to a random variable that deviates from the average of the estimated parameters. We derive a strong result about the upper bound for the asymptotic mean square error of the states, which just captures effect of the quantized precision and is independent of the network parameters.

In this paper, we propose a decentralized adaptive pinning control scheme for synchronization of undirected networks using a local adaptive strategy to determine both coupling strengths and feedback gains. By applying this local adaptive strategy, we show that the network can achieve synchronization with small coupling strengths and feedback gains. We provide some simulations to verify and illustrate the theoretical results.

In this paper, we study cooperative behaviors on homogeneous networks with different topological randomness. We find that, compared with the square lattice, the topological randomness promotes the emergence of cooperation in the networked prisoner's dilemma game. In order to explain this phenomenon, we observe the dynamical coevolution and organization of cooperators and defectors, and find almost all the individuals are altering their strategies at the steady state. These fluctuating individuals are easier to compose larger clusters when the network becomes more disordering. Therefore, the cooperators in random homogeneous networks can protect themselves more efficiently from the exploitation of defectors than those in regular lattices.

Many real networks have the similar power-law form connection distribution. However, networks with the same degree distribution may have quite different dynamical behaviors and other topological properties. In this paper, a synchronization-preferential rewiring mechanism for enhancing the synchronizability of a dynamical network while keep the power-law degree distribution unchanged is proposed. We also find that, as the synchronizability of the network enhances, the characteristic path length and assortativity decrease, but the maximum betweenness centrality does not have obvious decreasing trend.

Recent advances in complex network research have stimulated increasing interest in understanding the relationship between the topology and dynamics of complex networks. In this work, we investigate the synchronizability of a class of continuous-time dynamical networks with two kinds of scale-free topologies. Robustness of the synchronizability of the two scale-free dynamical networks with respect to random or specific removal of nodes is also compared.

We investigate the bifurcation behavior of a discrete-time model of Internet congestion control system, which stands for a single link shared by multiple sources. By choosing the gain parameter and the number of sources as bifurcation parameters, we prove the existence of period doubling bifurcation in the model and study the stability of bifurcation. We also find that chaotic behavior of the model can be controlled by limiting the amplitudes of the sending rates.

In the present work, we study the feedback control of a scale-free network of coupled Henon maps. A condition is derived for locally asymptotically stabilizing such a discrete-time complex dynamical network onto the homogenous stationary state by applying local feedback controllers to a fraction of network nodes. Numerical studies verify the effectiveness.

The El Farol bar problem is a simple model of congestion and coordination behaviours that occur with shared resources. From a control-theoretic point of view, this problem can be viewed as a decentralized control problem. This work is a first step towards solving the El Farol bar congestion problem based on control theory. A decentralized proportional-integral (PI) control strategy is proposed and investigated via simulation. This strategy provides a simple mechanism for a large collection of decentralized decision makers to solve a complex congestion problem.

The increasing outbreaks of computer viruses lead to a significant threat to the Internet. In this review we firstly give a brief introduction of computer virus including definition, classification, and characteristics. Then we turn on the overview that how viruses spread on networks in different topologies, especially in the context of computer viruses spreading on the Internet. Recent epidemic studies on the uncorrelated and correlated networks in terms of homogeneity and heterogeneity have been reviewed and compared their distinctions with regard to the main characteristic in epidemiology: epidemic threshold.

In this paper, the problem of making a nonchaotic continuous-time system chaotic via nonlinear time-delay feedback is studied. The designed controller is a combination of a feedback stabilization law and a time-delay feedback law with an arbitrarily small-amplitude, which together can make the system chaotic. An example is included for illustration.