This paper considers the dynamic coverage control of multi-rotor Unmanned Aerial Vehicles (UAVs) with the limited sensory range, which aims to collect sensor information from all points of interest in the given task area until the desired prescribed level is reached. However, the unknown environments are usually unavoidable for coverage task, where the presence of various obstacles and communication interferences affect the flight safety and communication stability of UAVs. Therefore, collision avoidance and connectivity maintenance are considered as the two safety issues in this paper, in which connectivity maintenance ensures the communication environment for UAVs to collaboratively accomplish task, and collision avoidance is used for UAVs to avoid obstacles and neighbors. In order to realize dynamic coverage control with safety constraints based on local environment information, this paper proposes the reinforcement learning-based algorithm with shield, where the shield designed by discrete-time Control Barrier Function (CBF) not only ensures the safety of the UAVs in the learning and control phases, but also maximizes the coverage performance of UAVs. In addition, each UAV only relies on local information to generate safe actions for advancing the coverage process during the execution phase. Finally, the effectiveness of the algorithm is verified by numerical simulations and physical experiment. Note to Practitioners—A typical application scenario of dynamic coverage control is search and rescue (SAR), in which UAVs equipped with multiple sensors focus on monitoring areas where trapped people may be present, e.g., anomalous areas detected by infrared sensors due to human body temperature. Since SAR always occurs in unknown environments, it is crucial to ensure the safety of UAVs during missions, of which the safety issues considered in this paper include collision avoidance and connectivity maintenance. To perform the SAR mission safely, we construct the CBF-based shield, which minimizes corrections the exploration actions of the UAVs and ensures the safety of the cluster during the mission. In addition, UAVs are difficult to obtain global environmental information in unknown environments and only rely on their sensors to collect local information. Therefore, the reinforcement learning algorithm with shield proposed in this paper adopts the centralized training and decentralized execution strategy, where the UAVs only need local observation information to plan their next actions. Physical experiments were also conducted to validate the feasibility of implementing the proposed algorithm using real UAVs.

This paper originates from two well-accepted challenges in the control of a quadrotor with a cable-suspended payload: 1) designing a refined controller based on high-precision payload swing modeling to achieve the quantized prescribed robustness; and 2) resolving the trajectory tracking performance degradation issue caused by input saturation. To combat these challenges, we start with establishing a precise payload swing model. The experimental investigation reveals the fact that neglect of realistic factors like cable-joint dry friction and cable elasticity has significant impacts on the precision of the existing models, especially under small swing angles. Therefore, we propose a new integrated drag model including a novel sign of the payload airspeed-dependent term, which lumps all payload swing damping factors together. This model is experimentally verified to be precise to provide the payload swing disturbance spectrum for quantitatively designing the bandwidth of the uncertainty and disturbance estimator (UDE). Furthermore, we resolve the input saturation issue by augmenting the classic UDE-based controller with a tracking differentiator (TD). Rigorous performance analysis derives a clear relationship between the control performance and the UDE parameter, which forms a simple yet effective parameter tuning guideline for practical applications to ensure the prescribed robustness and trajectory tracking accuracy. The effectiveness and advantage of the proposed controller are verified via comparative experiments in different flight scenarios. Note to Practitioners—The control input saturation issue and multi-parameter optimization are frequently encountered in engineering practices. When applying the TD to address the input saturation, the selection of parameter r in the TD depends on the reference continuity. Specifically, if the reference is continuous, then r can be large; otherwise r should be small to smooth the reference to avoid input saturation. For the multi-parameter optimization, the feedback gains$k_{p}$and$k_{d}$should be tuned first to guarantee the system stability, followed by decreasing the UDE parameter T to improve the system robustness. However, the feasible range of T may be restricted by the measurement noise and actuator bandwidth in practice. The proposed algorithm is applicable not only in aerial transportation systems but also in addressing other disturbance rejection problems.

In the quadrotor-suspended-payload system, simultaneously achieving trajectory tracking and payload anti-swing control is essential but challenging. We reveal that the fast maneuver required by the anti-swing control may not be effectively executed by the classic quadrotor dual loop control framework, due to the inherent non-ideal inner loop actuation bandwidth in practice. To address this issue, we propose a novel hierarchical control framework incorporating the uncertainty and disturbance estimator (UDE)–based backstepping control. The proposed framework consists of three loops, where the outer loop tracking and anti-swing controller and the inner loop attitude controller are both designed in the conventional way, and more importantly, a newly proposed intermediate loop is induced between them. In the intermediate loop, the robust dynamic compensator is developed based on an experimentally verified actuator model to extend the quadrotor actuation bandwidth, such that the outer loop control bandwidth requirements are quantitatively satisfied. Furthermore, the system stability is proved by the Lyapunov method along with the system performance analysis based on the singular perturbation theorem. Finally, comparative simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the proposed controller.

Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

This article constructs a leader–follower architecture by introducing intermittent-influence opinion leaders to the DeGroot model and analyzes the influence of this type of leaders on the evolution of opinions. Different from the existing studies where the leaders can convey their opinions to the followers uninterruptedly, the leaders in this article can only convey its opinion by broadcasting at some intermittent moments. First, we analyze the relationship between the leaders’ broadcast moments and the consensus opinion of followers and explain that the marginal revenue of the broadcasts is diminishing. Second, we describe the connotation of assimilation and calculate the minimum number of broadcasts required for the leaders to assimilate the followers’ opinions. Finally, aiming to make the consensus opinion of the followers and the leaders’ as close as possible, we give an optimal strategy on how to select followers to broadcast. The correctness of theoretical results is verified by numerical simulations.

As a fundamental structure in real-world networks, in addition to graph topology, communities can also be reflected by abundant node attributes. In attributed community detection, probabilistic generative models (PGMs) have become the mainstream method due to their principled characterization and competitive performances. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to the existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the block model of graph structure, our model incorporates the attribute by describing its effect on node popularity. To characterize the effect quantitatively, we analyze the community detectability for our model and then establish the requirements of the node popularity term. This leads to a new scheme for the crucial model selection problem in choosing and solving attributed community detection models. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition. Second, extensive experiments indicate that our method not only outperforms the competing approaches on the employed datasets, but also shows better applicability to networks with various node attributes.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

Signed digraphs with both positive and negative weighted edges are widely applied to explain cooperative and competitive interactions arising from various social, biological, and physical systems. This article formulates and solves the asynchronous tracking control problem of multiagent systems with input uncertainties on switching signed digraphs. In the interaction setting, we assume that the leader moves at a time-varying acceleration that cannot be measured by the followers accurately, and further suppose that each agent receives its neighbors' states information at certain instants determined by its own clock, which is not necessary to be synchronized with those of other agents. Using dynamically changing spanning subdigraphs of signed digraphs to describe graphically asynchronous interactions, the asynchronous tracking problem is equivalently transformed into a convergence problem of products of general substochastic matrices (PGSSM), in which the matrix elements are not necessarily non-negative and the row sums are less than or equal to 1. With the help of the matrix analysis technique and the composition of binary relations, we propose a new and original method to deal with the convergence problem of PGSSM, and further establish a spanning tree condition for asynchronous tracking control. Finally, the validity of the theoretical findings is verified through several numerical examples.

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.

With the popularization of network equipment and the rapid development of information technology, the scale and complexity of wireless sensor networks (WSNs) continue to expand. How to effectively locate link failures has become a challenging problem in WSNs. In this paper, we propose a novel method of locating link failures based on distributed cluster consensus protocol and graph decomposition technique. In our method, the initial data is injected into sensor nodes for distributed interactions, and then link failures can be located by observing and comparing the output data of the nodes. The proposed method is suitable for the situations with both single-link failure and multi-link failures, and has no limitations on the number, distribution and correlation of link failures. Necessary and sufficient conditions are provided to guarantee the accuracy of the proposed method in locating link failures. At last, the effectiveness of the proposed method is verified by both real and simulation experiments.

In this paper, we investigate the issue of security on the remote state estimation in cyber-physical systems (CPSs), where a wireless sensor utilizes the channel hopping scheme to transmit the data to the remote estimator over multiple channels in the presence of periodic denial-of-service attacks. Assume that the jammer can interfere with a subset of channels at each attack time in active period. For an energy-constraint jammer, the problem of how to select the number of channels at each attack time to maximally deteriorate the CPS performance is investigated. Based on the index of average estimation error, we introduce two different attack strategies, which include selecting identical number of channels and unequal number of channels at each attack time, and further show theoretically that the attack effect by selecting unequal number of channels is better than that of selecting identical number of channels. By formulating the problem of selecting the number of channels as integer programming problems, we present the corresponding algorithm to approximate the optimal attack schedule for both cases. The numerical results are presented to validate the theoretical results and the effectiveness of the proposed algorithms.

This article addresses the bipartite tracking consensus for a set of mobile autonomous agents over directed cooperation-competition networks. Here, cooperative and competitive interactions among the agents are described by positive and negative edges of the directed network topology, respectively. Both fixed and switching network topologies are considered. For the case with fixed network topology, the matrix product technique is utilized to derive the convergence result. For the case with switching network topologies, some key results related to the composition of binary relations are the main technical tools of analyzing the error system. In addition, the upper bound for the spectral radius of the system matrix is given to ensure the convergence of the system even if the single uncoupled system is strictly unstable. The applicability of the derived results is verified through two simulation experiments.

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts.

This paper formulates and solves an asynchronous sampled-data containment control problem of second-order multi-agent systems, in which each agent only receives the neighbors' information at certain sampling instants determined by its own clock, not all sampling instants. It is not assumed that the time sequence in which each agent receives its neighbors' information is evenly spaced. A distributed containment control protocol in the asynchronous sampled-data setting is designed. Main research tools, including nonnegative matrix theory and the composite of binary relation, are used to derive a necessary and sufficient condition guaranteeing that all the followers asymptotically converge to a convex hull formed by the static leaders. An example is provided to demonstrate the effectiveness of our theoretical result.

This paper proposes a new model and its analysis results for time-varying structurally balanced networks. Through model transformations, the system stability problem is converted into the problem of product convergence of infinite sub-stochastic matrices (PCISM). Further, by constructing a new digraph for each interaction topology, the problem of PCISM can be handled by virtue of the properties of row-stochastic matrices. When all leaders belong to only one of the two subgroups, it is shown that the followers that are in the same subgroup as the leaders gradually enter the convex hull formed by the leaders' states, while the others gradually enter the convex hull formed by the leaders' sign-inverted states. And when both subgroups contain leaders, a sufficient algebraic graph condition is established to ensure that all followers can enter the convex hull consisting of the leaders' states and sign-inverted states together. Moreover, it is also found that the followers keep active after entering the convex hulls. Finally, the bipartite containment performance is verified by a simulation test.

In this paper, the security issue of cache-enabled wireless networks is considered, where data transmission from the small base station (SBS) to the user is interfered by denial-of-service (DoS) attacks. To deal with this type of DoS attacks, we firstly investigate the energy dispatch problem from the perspective of the SBS. We further establish an optimization model by using the average number of subfiles requested from the macro base station (MBS) to measure the system performance, and then present the corresponding algorithm to derive the optimal scheduler. Additionally, numerical simulations are carried out to validate the effectiveness of the proposed algorithm.

In this contribution, we propose and investigate the containment control issue for general linear multiagent systems (MASs) under the asynchronous setting, where the network topology is not subjected to any structural restrictions and the roles of the leaders and the followers are entirely determined by the network topology. It is assumed that the interaction time instants of each agent, at which this agent interacts with its neighbors, are independent of the other agents' and can be unevenly distributed. An asynchronous distributed algorithm is proposed to implement the control strategy of linear MASs. The non-negative matrix theory and the composition of binary relations are utilized to handle the asynchronous containment control issue. It is shown that the leaders in each closed and strongly connected component of the network topology will reach a common state and the followers will gradually enter the dynamic convex hull constructed by the leaders. Moreover, it is also proved that the system matrix can be strictly unstable, and the upper bound of the system matrix's spectral radius is explicitly stated. Finally, two simulation examples are also provided to verify the efficacy of our theoretical results.

This article investigates the security issue based on the proactive channel hopping scheme in wireless networked control systems (WNCS), where the sensor sends the measurement data to the controller through multiple channels attacked by a periodic denial-of-service jammer. For a jammer with the limited energy, the number of attacks, which denotes the amount of time expended in launching the attack, increases with the decline in the channel number attacked at each time, which has an effect on the success of receiving the data. On the basis of this, the problem of making an optimal tradeoff between the channel number attacked at each time and the attack number to degrade the WNCS performance maximally is investigated. We formulate this problem as an integer programming problem based on the linear quadratic Gaussian control cost function. Then, a necessary condition of the optimal solution without integrality constraints for this optimization problem is given for the scenario where the sensor utilizes one channel at each time to perform the data transmission. We further investigate this problem for the case where the sensor can select several channels at each time to transmit the measurement data. Moreover, for both cases the corresponding algorithms are presented to approximate optimal schedules. Finally, the theoretical results and the validity of the algorithm proposed are verified via the numerical results.

This paper is concerned with the containment control problem for discrete-time high-order multi-agent systems with switching topologies under the asynchronous setting. Based on the distributed asynchronous consensus protocol using only each agent's own information and its neighbors' partial information, the asynchronous high-order containment control problem with switching topologies is transformed into a product problem of infinite time-varying row-stochastic matrices. Then the properties of row-stochastic matrices are explored to derive a sufficient condition involving graph topologies for asynchronous containment control of high-order multi-agent systems. The theoretical results are finally validated through numerical simulations.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.

In this paper, three optimal linear formation control algorithms are proposed for first-order linear multiagent systems from a linear quadratic regulator (LQR) perspective with cost functions consisting of both interaction energy cost and individual energy cost, because both the collective object (such as formation or consensus) and the individual goal of each agent are very important for the overall system. First, we propose the optimal formation algorithm for first-order multi-agent systems without initial physical couplings. The optimal control parameter matrix of the algorithm is the solution to an algebraic Riccati equation (ARE). It is shown that the matrix is the sum of a Laplacian matrix and a positive definite diagonal matrix. Next, for physically interconnected multi-agent systems, the optimal formation algorithm is presented, and the corresponding parameter matrix is given from the solution to a group of quadratic equations with one unknown. Finally, if the communication topology between agents is fixed, the local feedback gain is obtained from the solution to a quadratic equation with one unknown. The equation is derived from the derivative of the cost function with respect to the local feedback gain. Numerical examples are provided to validate the effectiveness of the proposed approaches and to illustrate the geometrical performances of multi-agent systems.