It has been reported that local memory information could enhance certain consensus performance of multiagent networks, such as protecting privacy and accelerating consensus. This article aims to investigate whether memory information can improve the robustness and scalability of consensus networks. The robustness is measured by the & ell;(2) gains from disturbances to consensus errors, and the scalability means that consensus can be preserved without retuning control parameters as the network scale increases. Using the linear combination of previous and current iteration states of agents and their neighbors, a memory-based consensus protocol is developed and we provide a necessary and sufficient condition for achieving consensus. Then, we establish the analytic expression of the & ell;(2) gain, which is exclusively determined by control parameters and nonzero minimum and maximum Laplacian eigenvalues. Furthermore, we show how tuning the memory coefficient can improve both robustness and scalability, and the optimal control parameters are further derived. Interestingly, we observe a positive correlation between robustness and scalability.

This paper proposes an adaptive distributed event-triggered secure consensus control scheme for achieving fully distributed self-triggered secure consensus control of nonlinear multiagent systems with sequential communication link scaling attacks. Firstly, attacks on the communication link for nonlinear multiagent systems are modeled by sequential communication link scaling attacks, which include communication link attacks or faults, deception attacks, DoS attacks, and sequential scaling attacks, etc. Based on an event-triggered control mechanism, the impacts of attack intervals on triggering sequences are analyzed. Then, an adaptive event-triggered secure consensus control scheme is proposed, which contains an edge-based adaptive event-triggered protocol and a dynamic event-triggered function for each agent, and can be implemented in a fully distributed and self-triggered fashion. Furthermore, by utilizing the Lipchitz condition and the properties of the Laplacian potential, sufficient conditions for nonlinear multiagent systems with sequential communication link scaling attacks to achieve secure consensus control are given, where the attack frequency and duration that the system can render are presented. Finally, the Zeno phenomenon is excluded and a simulation example is provided.

In this paper, a robust optimal three-dimensional cooperative guidance law with input saturation is proposed for intelligent aerial vehicles to intercept an unknown maneuvering target. The problem of cooperative interception is formulated as a leader-follower optimal tracking control problem based on the differential graphical game subject to a nonautonomous leading vehicle with bounded control inputs. Utilizing the backstepping method, the guidance law is divided into a feedforward part for generating the desired state signals and compensates for the impact of input saturation, and a data-driven feedback part based on the differential graphical game that regulates tracking errors due to unknown target maneuvers while optimizing interactive performance indices. The uniform ultimate bounded property of the tracking errors in the closed-loop system can be guaranteed and the predefined interactive cost function can be optimized by the proposed guidance law. Simulation examples of cooperative interception are provided to validate the effectiveness of the proposed approach.

This paper is devoted to addressing the lag synchronization issue of semi-Markovian jumping complex-valued networks with time-varying delay. Firstly, two types of mode-dependent controllers are designed, i.e., a continuous distributed delayed memorized controller and a discrete distributed delayed impulsive controller, which are utilized to facilitate lag synchronization between the considered master and slave systems. Secondly, by introducing some novel integral inequalities, the achieved results not only effectively overcome the limitation d(t) < 1 in earlier literature, but also expand the feasible domain because of the addition of relaxed matrices N and X. Additionally, sufficient conditions for attaining mean-square lag synchronization of the master-slave systems can be obtained by resorting to numerous inequality approaches, stochastic analysis techniques, and Lyapunov stability theory. Meanwhile, the desired controller gain matrices can be designed by solving derived matrix inequalities. As a final point, two numerical examples are proposed to validate the established theoretical criteria.

Satellite observation is an important way to understand the earth. However, due to the problems such as satellite aging, cloud obstruction, and other interferences during the imaging and transmission process, remote sensing images inevitably produce various defects. Hence, it is necessary to quickly detect defects to calibrate the imaging system and avoid the waste of satellite resource. Current researches on defect detection in remote sensing images are not comprehensive, which only focus on partial defect categories, such as cloud and stripe. To this end, we construct the first large-scale High-resolution Remote Sensing image Defect detection dataset (HRSD). The proposed dataset contains more than 1.2 million manually annotated patches from eight different satellites, covering various common defect categories and including multiple image modalities (i.e., panchromatic and multispectral). The dataset also has rich diversity which covers different landforms in multiple regions. Furthermore, to realize the detection of multiple defect categories simultaneously, we design a feature aggregation graph network (FAGN) based on the position correlation and semantic similarity among image patches, which fully utilizes the distribution characteristics of defects to achieve accurate defect detection. Extensive experiments on the HRSD dataset demonstrated the effectiveness of FAGN. We will release the HRSD dataset and FAGN model later.

Motivated by widespread dominance hierarchy, growth of group sizes, and feedback mechanisms in social species, we are devoted to exploring the scalable second-order consensus of hierarchical groups. More specifically, a hierarchical group consists of a collection of agents with double-integrator dynamics on a directed acyclic graph with additional reverse edges, which characterize feedback mechanisms across hierarchical layers. As the group size grows and the reverse edges appear, we investigate whether the absolute velocity protocol and the relative velocity protocol can preserve the system consensus property without tuning the control gains. It is rigorously proved that the absolute velocity protocol is able to achieve completely scalable second-order consensus but the relative velocity protocol cannot. This result theoretically reveals how the scalable coordination behavior in hierarchical groups is determined by local interaction rules. Moreover, we develop a hierarchical structure in order to achieve scalable second-order consensus for networks of any size and with any number of reverse edges.

This article investigates completely distributed secure consensus control (SCC) of high-order linear and Lipschitz nonlinear multiagent systems (MASs) in the presence of interaction link attacks, respectively, where the design criteria are independent of the interaction topology and the parameters of interaction link attacks. An estimator-based adaptive SCC protocol is proposed to realize SCC, where coupling weights of the virtual distributed reference state estimator (VDRSE) are adaptively adjusted to eliminate the impacts of interaction link attacks. Then, the leader–follower and leaderless structures are unified into a general directed graph framework by decomposing the Laplacian matrix in terms of the root node and nonroot node, and sufficient conditions for VDRSEs achieving reference state consensus and high-order linear MASs achieving SCC are given, respectively. Moreover, main results of high-order linear MASs are extended to Lipschitz nonlinear MASs. Finally, two numerical examples are presented in order to validate the theoretical results.

The formation control for a quadrotor swarm with the energy consumption constraint and/or the regulation performance constraint is challenging, where both the guaranteed-performance formation strategy and the guaranteed-cost formation strategy cannot realize the optimal control. Especially, the flying experiment of the optimal time-varying formation is very difficult to realize. A new control protocol with an optimization index is proposed to realize the optimal time-varying formation and the optimization index is integrated as a Kronecker product form containing the Laplacian matrix of the communication topology of a quadrotor swarm. Moreover, an analytic criterion for optimal time-varying formation achievability is proposed, and explicit expressions of the formation center function and the minimum value of the optimization index are presented, respectively. Furthermore, an optimal formation achievability algorithm is proposed and a formation flying experiment is performed, where the outer-loop position control input is converted into the inner-loop attitude control and an Euler-angle loop controller and an angular velocity loop controller are introduced.

This paper formulates two novel theoretical designs of input -to -state stabilizing control for a class of recurrent neural networks with multiple proportional delays. The analysis tool developed in this paper is based on Lyapunov function and inverse optimality method, which does not require solving Hamilton-Jacobi-Bellman equations. Two inverse optimal feedback laws are constructed via the dimensions of state and input, which ensure the input -state stability for the considered system. When the dimensions of state and input are different, we establish a scalar function and give one of the control laws by Sontag's formula. Furthermore, the designs of inverse optimal control reach both global inverse optimality and global asymptotic stability of the system for some meaningful cost functional. Four numerical examples are provided to show the effectiveness of the inverse optimal control.

In this article, we consider the cooperative output regulation for linear multiagent systems (MASs) via the distributed event-triggered strategy in fixed time. A novel fixed-time event-triggered control protocol is proposed using a dynamic compensator method. It is shown that based on the designed control scheme, the cooperative output regulation problem is addressed in fixed time and the agents in the communication network are subject to intermittent communication with their neighbors. Simultaneously, with the proposed event-triggering mechanism, Zeno behavior can be ruled out by choosing the appropriate parameters. Different from the existing strategies, both the compensator and control law are designed with intermittent communication in fixed time, where the convergence time is independent of any initial conditions. Moreover, for the case that the states are not available, the output regulation problem can further be addressed by the distributed observer-based output feedback controller with the fixed-time event-triggered compensator and event-triggered mechanism. Finally, a simulation example is provided to illustrate the effectiveness of the theoretical results.

Summary In this article, we investigate the mean‐square consensus tracking problem of nonlinear MASs subjected to external disturbances under stochastic switching topologies. An aperiodically intermittent control strategy is proposed to guarantee consensus tracking in the mean‐square sense, which depends only on the local state information of the neighbors and the leader. The stochastic switching signal of the interaction topologies is modeled as a continuous‐time Markov process, and the aperiodically intermittent control method is driven by a discrete‐time series of stochastic samples, that are independent of each other. Moreover, several sufficient conditions are established to ensure the feasibility of the control strategy. The proofs are given by employing matrix inequality theory and Lyapunov stability theory. Finally, numerical simulations are provided to verify the feasibility of the proposed theoretical results.

This paper develops new practical stability criteria for impulsive stochastic functional differential systems with distributed-delay dependent impulses by using the Lyapunov–Razumikhin approach and some inequality techniques. In the given systems, the state variables on the impulses are concerned with a history time period, which is very appropriate for modelling some practical problems. Moreover, different from the existing practical stabilization results for the systems with unstable continuous stochastic dynamics and stabilizing impulsive effects, we take the systems with stable continuous stochastic dynamics and destabilizing impulsive effects into account. It shows that under the impulsive perturbations, the practical exponential stability of the stochastic functional differential systems can remain unchanged when the destabilizing distributed-delay dependent impulses satisfy some conditions on the frequency and amplitude of the impulses. In other words, it reveals that how to control the impulsive perturbations such that the corresponding stochastic functional differential systems still maintain practically exponentially stable. Finally, an example with its numerical simulation is offered to demonstrate the efficiency of the theoretical findings.

This paper investigates the enclosing control of hybrid multi-agent systems under directed networks. Firstly, we establish some criteria for continuous-time and discrete-time multi-agent systems to achieve enclosing control. Secondly, according to the communication modes among agents, four distributed enclosing control protocols are proposed for hybrid multi-agent systems. Then, under the proposed protocols, we give the corresponding sufficient conditions to guarantee enclosing control. Finally, the correctness of our results is verified by simulations.

Summary The formation category for multiple quadrotor drones with the distributed formation protocol is addressed. By exploiting an indoor optical motion capture location system, the formation flight experiment for practical multiple quadrotor drones is performed. Firstly, by decoupling the relationship of the outer and inner loop models, the control loop of a quadrotor drone is divided into the outer position and velocity loop and inner attitude loop, and the distributed formation protocol is constructed by outer‐loop states, where the neighboring relationship term and the formation registration term can regulate formation errors and the whole motion trajectory of multiple quadrotor drones, respectively. The self‐feedback mechanism of the quadrotor drone is introduced to regulate the motion trajectory of whole formation. Then, formation achievability criteria for multiple quadrotor drones are proposed, where two control gains are determined in an analytic form, and the whole motion trajectory for multiple quadrotor drones is presented. Finally, a formation achievement algorithm is proposed, where the formation protocol is implemented by the inner‐loop attitude control of each quadrotor drone, and a formation flight experiment is performed to show the effectiveness of theoretical results.

The induced attack on the formation control of multiagent systems is investigated from the perspective of the attacker, where a new induced attack strategy is proposed to drive the whole formation of multiagent systems to the prescribed reference trajectory. Compared with denial-of-service attacks and deception attacks, a desired formation reference trajectory with the stable formation structure is reached by the induced attack instead of disrupting the whole formation. First, the induced attack signal is produced by designing an attack generation exosystem, whose dynamics can be described by the regulated attack matrix and can be adjusted to generate the prescribed reference trajectory. Then, based on the local state information, the formation vector, and the induced attack signal among partial agents, a new distributed induced attack formation protocol is proposed, which is composed of the nominal formation term and the induced attack term. Meanwhile, the induced attack design criterion is proposed by utilizing a robust H infinity$$ {H}_{\infty } $$ scheme, where the robust H infinity$$ {H}_{\infty } $$ performance bound can be configured by two performance regulating parameters and can be used to design the appropriate regulated attack matrix. Furthermore, by establishing the projection of the induced attack signal onto the formation agreement subspace, an explicit expression of the prescribed reference trajectory is determined, which depicts the movement trajectory of the entire formation of the multiagent system under the induced attack. Finally, a simulation is provided to vindicate the effectiveness of theoretical results.

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings.

This letter focuses on fully distributed secure tracking control for leader-following nonlinear multiagent systems subject to multi-link sequence scaling attacks. Firstly, an edge-based adaptive secure tracking control scheme is proposed for followers to counteract the effects of multi-link sequence scaling attacks and can be implemented in a fully distributed fashion. Then, by utilizing the Lipschitz condition and the properties of the Laplacian potential, sufficient conditions for nonlinear multiagent systems with multi-link sequence scaling attacks to achieve secure tracking control are given, where the attack duration that the system can render is presented. Finally, a numerical simulation example is provided to illustrate the theoretical results.

This paper considers max-consensus of a discrete-time multi-agent system (MAS) in directed random networks. Interactions among agents in the MAS are probabilistic and independent with each other. By using max-plus algebra and random theory, a sufficient and necessary condition is given for achieving max-consensus of the MAS. Moreover, we demonstrate that the max-consensus in four probabilistic senses (almost surely, in probability, expectation and mean square) is equivalent when expected graph is strongly connected. This ensures that max-consensus can be achieved in multi-agent systems even if random failures occur in the communication network, which is of practical importance in the fields of wireless sensor networks and distributed computing. A simulation example is presented to illustrate the effectiveness of theoretical results.

This paper considers the L2gain optimal problem for a class of discrete-time linear time-invariant with state-disturbance feedback controller and unknown system dynamic. Firstly, for a given stabilizing control policy, we establish the relation between the L2gain and a sequence of lower triangle Toeplitz matrices. Meanwhile, we show that the upper bound of optimal L2gain is proportional to the linear correlation degree between the input and disturbance matrices. Secondly, to overcome the obstacle arising from the unknown system dynamics, a data-based reinforcement learning scheme is developed for the optimal control policy by using linear matrix inequality technique and Q-learning with policy iteration. Under certain conditions, we prove that either the reinforcement learning process ends in a finite number of iterations, or the L2gain sequence is strictly monotonically convergent along the iteration axis provided that the disturbance data set can fully activate the closed-loop system. Finally, simulations are given to illustrate the effectiveness of our findings.

This article investigates the resilient bipartite consensus problem for continuous-time second-order multiagent systems in the presence of totally bounded malicious nodes under signed digraphs. An event-based resilient impulsive algorithm is employed, which cannot only mitigate the malicious nodes’ influence on the convergence of normal ones but also reduce the communication loads of agents. A necessary and sufficient condition related to the network topology is established for solving resilient bipartite consensus by using system transformation. A numerical simulation illustrates the effectiveness of the result.