In this paper, the finite-time consensus of a leader-following multi-agent network with non-identical nonlinear dynamics and time-varying topologies is investigated. All the agents, especially the leaders, have non-id...In this paper, the finite-time consensus of a leader-following multi-agent network with non-identical nonlinear dynamics and time-varying topologies is investigated. All the agents, especially the leaders, have non-identical and nonlinear dynamics. According to the algebraic graph theory, Lyapunov stability theory and Kronecker product, a control strategy strategy is established to guarantee the finite-time consensus of multi-agent network with multiple leaders. Furthermore, several numerical simulations illustrate the effectiveness and feasibility of the proposed method.展开更多
This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems(MANSs). Based on the MANSs,a new distributed differentially private consensu...This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems(MANSs). Based on the MANSs,a new distributed differentially private consensus algorithm(DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.展开更多
In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents tr...In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.展开更多
The synchronization of time-delayed multi-agent networks with connected and directed topology is studied. Based on the correlative work about the agent synchronization, a modified model is presented, in which each com...The synchronization of time-delayed multi-agent networks with connected and directed topology is studied. Based on the correlative work about the agent synchronization, a modified model is presented, in which each communication receiver is distributed a delay 7. In addition, a proportional term k is introduced to modulate the delay range and to guarantee the synchronization of each agent. Two new parameters mentioned above are only correlative to the network topology, and a theorem about their connections is derived by both frequency domain method and geometric method. Finally, the theoretical result is illustrated by numerical simulations.展开更多
Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced, and its running mechanism is establish...Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced, and its running mechanism is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is presented. This model implements the multi-layer and distributed active defense mechanism for network intrusion. The experiment results show that this model is a good solution to the network security defense.展开更多
Two protocols are presented,which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network.First,the proto...Two protocols are presented,which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network.First,the protocol without considering the communication time-delay is presented,and by using Lyapunov stability theory,the sufficient condition of stability for this multi-agent system is presented.Further,considering the communication time-delay,the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated.The main contribution of the proposed protocols is that,as well as the velocity consensus is considered,the formation control is concerned for multi-agent systems described as the second-order equations.Finally,numerical examples are presented to illustrate the effectiveness of the proposed protocols.展开更多
To investigate the leader-following formation control, in this paper we present the design problem of control protocols and distributed observers under which the agents can achieve and maintain the desired formation f...To investigate the leader-following formation control, in this paper we present the design problem of control protocols and distributed observers under which the agents can achieve and maintain the desired formation from any initial states, while the velocity converges to that of the virtual leader whose velocity cannot be measured by agents in real time. The two cases of switching topologies without communication delay and fixed topology with time-varying communication delay are both considered for multi-agent networks. By using the Lyapunov stability theory, the issue of stability is analysed for multi-agent systems with switching topologies. Then, by considering the time-varying communication delay, the sufficient condition is proposed for the multi-agent systems with fixed topology. Finally, two numerical examples are given to illustrate the effectiveness of the proposed leader-following formation control protocols.展开更多
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti...This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.展开更多
This paper investigates the consensus disturbance rejection problem among multiple high-order agents with directed graphs.Based on disturbance observers,distributed consensus disturbance rejection protocols are constr...This paper investigates the consensus disturbance rejection problem among multiple high-order agents with directed graphs.Based on disturbance observers,distributed consensus disturbance rejection protocols are constructed in leaderless and leader-follower consensus setups.Different from the previous related papers,the consensus protocols in this paper are developed in a fully distributed fashion,relying on only the state information of each agent and its neighbors.Sufficient conditions are provided to guarantee that the asymptotic stability of high-order multi-agent systems can be reached with matched disturbances.展开更多
In this paper the pinning consensus of multi-agent networks with arbitrary topology is investigated. Based on the properties of M-matrix, some criteria of pinning consensus are established for the continuous multi-age...In this paper the pinning consensus of multi-agent networks with arbitrary topology is investigated. Based on the properties of M-matrix, some criteria of pinning consensus are established for the continuous multi-agent network and the results show that the pinning consensus of the dynamical system depends on the smallest real part of the eigenvalue of the matrix which is composed of the Laplacian matrix of the multi-agent network and the pinning control gains. Meanwhile, the relevant work for the discrete-time system is studied and the corresponding criterion is also obtained. Particularly, the fundamental problem of pinning consensus, that is, what kind of node should be pinned, is investigated and the positive answers to this question are presented. Finally, the correctness of our theoretical findings is demonstrated by some numerical simulated examples.展开更多
This paper considers the formation tracking problem under a rigidity framework, where the target formation is specified as a minimally and infinitesimally rigid formation and the desired velocity of the group is avail...This paper considers the formation tracking problem under a rigidity framework, where the target formation is specified as a minimally and infinitesimally rigid formation and the desired velocity of the group is available to only a subset of the agents. The following two cases are considered： the desired velocity is constant, and the desired velocity is timevarying. In the first case, a distributed linear estimator is constructed for each agent to estimate the desired velocity. The velocity estimation and a formation acquisition term are employed to design the control inputs for the agents, where the rigidity matrix plays a central role. In the second case, a distributed non-smooth estimator is constructed to estimate the time-varying velocity, which is shown to converge in a finite time. Theoretical analysis shows that the formation tracking problem can be solved under the proposed control algorithms and estimators. Simulation results are also provided to show the validity of the derived results.展开更多
This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter contro...This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.展开更多
The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.展开更多
In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging ...In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.展开更多
Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused ...Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.展开更多
With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier...With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination me...This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination methods that are solved by neural dynamics,the proposed strategy displays greater flexibility,adaptability and scalability.Furthermore,the proposed AMAC strategy is reconstructed as a time-varying complex-valued matrix equation.By introducing a dynamic error function,a fixed-time convergent zeroing neural network(FTCZNN)model is designed for the online solution of the AMAC strategy,with its convergence time upper bound derived theoretically.Finally,the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments.Numerical results indicate that this method can reduce the formation error to the order of 10^(-6)within 1.8 s.展开更多
This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to es...This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to estimate higher-order synchronization errors,enabling the controller to rely solely on relative output measurements.This approach significantly reduces the dependence on full-state information,which is often infeasible or costly in practical engineering applications.An output feedback control strategy is developed to overcome these limitations while ensuring robust and effective synchronization.Simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the theoretical findings.展开更多
Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and colla...Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.展开更多
基金Supported by the National Natural Science Foundation of China(6147333861304164)
文摘In this paper, the finite-time consensus of a leader-following multi-agent network with non-identical nonlinear dynamics and time-varying topologies is investigated. All the agents, especially the leaders, have non-identical and nonlinear dynamics. According to the algebraic graph theory, Lyapunov stability theory and Kronecker product, a control strategy strategy is established to guarantee the finite-time consensus of multi-agent network with multiple leaders. Furthermore, several numerical simulations illustrate the effectiveness and feasibility of the proposed method.
基金supported in part by the National Key Research and Development Program of China (2016YFB0800601)
文摘This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems(MANSs). Based on the MANSs,a new distributed differentially private consensus algorithm(DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.
基金supported by National Basic Research Program of China (973 Program) (No. 2010CB731800)Key Project of National Science Foundation of China (No. 60934003)+2 种基金National Nature Science Foundation of China (No. 61074065)Key Project for Natural Science Research of Hebei Education Department, PRC(No. ZD200908)Key Project for Shanghai Committee of Science and Technology (No. 08511501600)
文摘In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.
基金the National Natural Science Foundation of China (No. 70571017)the Research Foundation from Provincial Education Department of Zhejiang of China (No. 20070928)
文摘The synchronization of time-delayed multi-agent networks with connected and directed topology is studied. Based on the correlative work about the agent synchronization, a modified model is presented, in which each communication receiver is distributed a delay 7. In addition, a proportional term k is introduced to modulate the delay range and to guarantee the synchronization of each agent. Two new parameters mentioned above are only correlative to the network topology, and a theorem about their connections is derived by both frequency domain method and geometric method. Finally, the theoretical result is illustrated by numerical simulations.
基金Supported by the National Natural Science Foundation of China (60373110, 60573130, 60502011)
文摘Inspired by the immune theory and multi-agent systems, an immune multi-agent active defense model for network intrusion is established. The concept of immune agent is introduced, and its running mechanism is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is presented. This model implements the multi-layer and distributed active defense mechanism for network intrusion. The experiment results show that this model is a good solution to the network security defense.
基金supported by the National Natural Science Foundation of China(6093400361074065)+1 种基金the National Basic Research Program of China(973 Program)(2010CB731800)the Key Project for Natural Science Research of Hebei Education Department(ZD200908)
文摘Two protocols are presented,which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network.First,the protocol without considering the communication time-delay is presented,and by using Lyapunov stability theory,the sufficient condition of stability for this multi-agent system is presented.Further,considering the communication time-delay,the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated.The main contribution of the proposed protocols is that,as well as the velocity consensus is considered,the formation control is concerned for multi-agent systems described as the second-order equations.Finally,numerical examples are presented to illustrate the effectiveness of the proposed protocols.
基金Project supported by the National Natural Science Foundation for Distinguished Young Scholars of China (Grant No. 60525303)the National Natural Science Foundation of China (Grant No. 60704009)+1 种基金the Key Project for Natural Science Research of the Hebei Educational Department (Grant No. ZD200908)the Doctorial Fund of Yanshan University (Grant No. B203)
文摘To investigate the leader-following formation control, in this paper we present the design problem of control protocols and distributed observers under which the agents can achieve and maintain the desired formation from any initial states, while the velocity converges to that of the virtual leader whose velocity cannot be measured by agents in real time. The two cases of switching topologies without communication delay and fixed topology with time-varying communication delay are both considered for multi-agent networks. By using the Lyapunov stability theory, the issue of stability is analysed for multi-agent systems with switching topologies. Then, by considering the time-varying communication delay, the sufficient condition is proposed for the multi-agent systems with fixed topology. Finally, two numerical examples are given to illustrate the effectiveness of the proposed leader-following formation control protocols.
基金supported in part by the National Natural Science Foundation of China(NSFC)(61773260)the Ministry of Science and Technology (2018YFB130590)。
文摘This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
基金supported by the National Natural Science Foundation of China (Nos. U1713223 and 61876187)by the Beijing Nova Program (No. 2018047)by the Joint Fund of Ministry of Education of China for Equipment Preresearch
文摘This paper investigates the consensus disturbance rejection problem among multiple high-order agents with directed graphs.Based on disturbance observers,distributed consensus disturbance rejection protocols are constructed in leaderless and leader-follower consensus setups.Different from the previous related papers,the consensus protocols in this paper are developed in a fully distributed fashion,relying on only the state information of each agent and its neighbors.Sufficient conditions are provided to guarantee that the asymptotic stability of high-order multi-agent systems can be reached with matched disturbances.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60973114 and 61170249)the Natural Science Foundation of Chongqing Science and Technology Commission, China (Grant Nos. 2009BA2024, cstc2011jjA40045, and cstc2013jcyjA0906)the State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, China (Grant No. 2007DA10512711206)
文摘In this paper the pinning consensus of multi-agent networks with arbitrary topology is investigated. Based on the properties of M-matrix, some criteria of pinning consensus are established for the continuous multi-agent network and the results show that the pinning consensus of the dynamical system depends on the smallest real part of the eigenvalue of the matrix which is composed of the Laplacian matrix of the multi-agent network and the pinning control gains. Meanwhile, the relevant work for the discrete-time system is studied and the corresponding criterion is also obtained. Particularly, the fundamental problem of pinning consensus, that is, what kind of node should be pinned, is investigated and the positive answers to this question are presented. Finally, the correctness of our theoretical findings is demonstrated by some numerical simulated examples.
基金Project supported by the National Natural Science Foundation of China(Grant No.61473240)
文摘This paper considers the formation tracking problem under a rigidity framework, where the target formation is specified as a minimally and infinitesimally rigid formation and the desired velocity of the group is available to only a subset of the agents. The following two cases are considered： the desired velocity is constant, and the desired velocity is timevarying. In the first case, a distributed linear estimator is constructed for each agent to estimate the desired velocity. The velocity estimation and a formation acquisition term are employed to design the control inputs for the agents, where the rigidity matrix plays a central role. In the second case, a distributed non-smooth estimator is constructed to estimate the time-varying velocity, which is shown to converge in a finite time. Theoretical analysis shows that the formation tracking problem can be solved under the proposed control algorithms and estimators. Simulation results are also provided to show the validity of the derived results.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under project number NBU-FFR-2026-2441-02.
文摘This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
基金supported by the National Key Research and Development Program of China under grant 2021YFA0716600。
文摘In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.
基金supported in part by the National Natural Science Foundation of China(grants 62203073 and 62573068)the Natural Science Foundation of Chongqing,China(grant CSTB2022NSCQMSX0577)。
文摘Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms.
文摘With the advent of sixth-generation mobile communications(6G),space-air-ground integrated networks have become mainstream.This paper focuses on collaborative scheduling for mobile edge computing(MEC)under a three-tier heterogeneous architecture composed of mobile devices,unmanned aerial vehicles(UAVs),and macro base stations(BSs).This scenario typically faces fast channel fading,dynamic computational loads,and energy constraints,whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings.To address this issue,we formulate a multi-agent Markov decision process(MDP)for an air-ground-fused MEC system,unify link selection,bandwidth/power allocation,and task offloading into a continuous action space and propose a joint scheduling strategy that is based on an improved MATD3 algorithm.The improvements include Alternating Layer Normalization(ALN)in the actor to suppress gradient variance,Residual Orthogonalization(RO)in the critic to reduce the correlation between the twin Q-value estimates,and a dynamic-temperature reward to enable adaptive trade-offs during training.On a multi-user,dual-link simulation platform,we conduct ablation and baseline comparisons.The results reveal that the proposed method has better convergence and stability.Compared with MADDPG,TD3,and DSAC,our algorithm achieves more robust performance across key metrics.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金supported by the National Natural Science Foundation of China under Grants 61962023,61562029 and 62466019.
文摘This paper presents an adaptive multi-agent coordination(AMAC)strategy suitable for complex scenarios,which only requires information exchange between neighbouring robots.Unlike traditional multi-agent coordination methods that are solved by neural dynamics,the proposed strategy displays greater flexibility,adaptability and scalability.Furthermore,the proposed AMAC strategy is reconstructed as a time-varying complex-valued matrix equation.By introducing a dynamic error function,a fixed-time convergent zeroing neural network(FTCZNN)model is designed for the online solution of the AMAC strategy,with its convergence time upper bound derived theoretically.Finally,the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments.Numerical results indicate that this method can reduce the formation error to the order of 10^(-6)within 1.8 s.
文摘This paper addresses the synchronization of follower agents’state vectors with that of a leader in high-order nonlinear multi-agent systems.The proposed low-complexity control scheme employs high-gain observers to estimate higher-order synchronization errors,enabling the controller to rely solely on relative output measurements.This approach significantly reduces the dependence on full-state information,which is often infeasible or costly in practical engineering applications.An output feedback control strategy is developed to overcome these limitations while ensuring robust and effective synchronization.Simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the theoretical findings.
基金supported by the National Natural Science Foundation of China Youth Fund(No.62101579)。
文摘Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
