A continuous⁃time distributed optimization was researched for second⁃order heterogeneous multi⁃agent systems.The aim of this study is to keep the velocities of all agents the same and make the velocities converge to t...A continuous⁃time distributed optimization was researched for second⁃order heterogeneous multi⁃agent systems.The aim of this study is to keep the velocities of all agents the same and make the velocities converge to the optimal value to minimize the sum of local cost functions.First,an effective distributed controller which only uses local information was designed.Then,the stability and optimization of the systems were verified.Finally,a simulation case was used to illustrate the analytical results.展开更多
Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective fun...Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.展开更多
Dear Editor,An adaptive consensus control algorithm for uncertain multi-agent systems(MAS),capable of guaranteeing unified prescribed performance,is presented in this letter.Unlike many existing prescribed performance...Dear Editor,An adaptive consensus control algorithm for uncertain multi-agent systems(MAS),capable of guaranteeing unified prescribed performance,is presented in this letter.Unlike many existing prescribed performance related works,the developed control exhibits some features.Firstly,a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader’s signal within a predetermined time,but also the control design for each agent is independent with its neighbors.展开更多
Dear Editor,This letter investigates a multi-agent path planning problem in a road network with the requirement of avoiding collisions among all agents in the partitioned environment.We first abstract the agents to a ...Dear Editor,This letter investigates a multi-agent path planning problem in a road network with the requirement of avoiding collisions among all agents in the partitioned environment.We first abstract the agents to a set of transition systems,and construct a team transition system from these individual systems.A mechanism is designed for the team transition system to detect all collisions within the synthesized run.展开更多
The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to...The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.展开更多
Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus o...Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.展开更多
Low-temperature plasma is a green and high-efficiency technology for chemical warfare agent(CWA)decontamination.However,traditional plasma devices suffer from the problems of highpower composition and large power-supp...Low-temperature plasma is a green and high-efficiency technology for chemical warfare agent(CWA)decontamination.However,traditional plasma devices suffer from the problems of highpower composition and large power-supply size,which limit their practical applications.In this paper,a self-driven microplasma decontamination system,induced by a dielectric-dielectric rotary triboelectric nanogenerator(dd-r TENG),was innovatively proposed for the decontamination of CWA simulants.The microplasma was characterized via electrical measurements,optical emission spectra and ozone concentration detection.With an output voltage of-3460 V,the dd-r TENG can successfully excite microplasma in air.Reactive species,such as OH,O(1D),Hαand O3were detected.With input average power of 0.116 W,the decontamination rate of 2-chloroethyl ethyl sulfide reached 100%within 3 min of plasma treatment,while the decontamination rates of malathion and dimethyl methylphosphonate reached(65.92±1.65)%and(60.88±1.92)%after 7 min of plasma treatment,respectively.In addition,the decontamination rates gradually decreased with the increase in the simulant concentrations.Typical products were identified and analyzed.This study demonstrates the broad spectrum and feasibility of the dd-r TENG-microplasma for CWA elimination,which provides significant guidance for their practical applications in the future.展开更多
Dear Editor,The optimal formation control design problem is studied for a class of second-order multi-agent systems(MASs) with obstacle avoidance.Based on the actor-critic framework, an optimized formation controller ...Dear Editor,The optimal formation control design problem is studied for a class of second-order multi-agent systems(MASs) with obstacle avoidance.Based on the actor-critic framework, an optimized formation controller is proposed by constructing a novel performance index function. Furthermore, the stability of MAS is proved by constructing the Lyapunov function. The simulation results are provided to depict the effectiveness of the proposed strategies.展开更多
Dear Editor,This letter investigates the target enclosing control problem of multi-agent systems.A signed graph-based control strategy is presented,where the agents are steered to enclose the dynamic target from both ...Dear Editor,This letter investigates the target enclosing control problem of multi-agent systems.A signed graph-based control strategy is presented,where the agents are steered to enclose the dynamic target from both sides as they move.This is inspired by the phenomenon that signed networks exhibit bipartite clustering if the underlying graph is structurally balanced,so that the agents may naturally enclose the zero point from opposite sides(+and.)if proper controllers are applied.By adopting a distributed observer to estimate the information of dynamic target。展开更多
Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intr...Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.61573199 and 61571441)。
文摘A continuous⁃time distributed optimization was researched for second⁃order heterogeneous multi⁃agent systems.The aim of this study is to keep the velocities of all agents the same and make the velocities converge to the optimal value to minimize the sum of local cost functions.First,an effective distributed controller which only uses local information was designed.Then,the stability and optimization of the systems were verified.Finally,a simulation case was used to illustrate the analytical results.
基金supported in part by the National Natural Science Foundation of China(62373231,61973201)the Fundamental Research Program of Shanxi Province(202203021211297)Shanxi Scholarship Council of China(2023-002)。
文摘Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.
基金supported in part by the National Key Research and Development Program of China(2022YFB4701400/4701401)the National Natural Science Foundation of China(61991400,61991403,62250710167,61860206008,61933012,62273064)+1 种基金the Chongqing Outstanding Young Talents Support Program(cstc2024ycjh-bgzxm0085)CAAI-Huawei MindSpore Open Fund。
文摘Dear Editor,An adaptive consensus control algorithm for uncertain multi-agent systems(MAS),capable of guaranteeing unified prescribed performance,is presented in this letter.Unlike many existing prescribed performance related works,the developed control exhibits some features.Firstly,a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader’s signal within a predetermined time,but also the control design for each agent is independent with its neighbors.
基金supported in part by the National Key R&D Program of China(2021ZD0112600)the National Natural Science Foundation of China(61873219,62103344,62173283)+1 种基金the Natural Science Foundation of Fujian Province of China(2021J01051,2021J05015)Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM)(HRTP-[2022]-25)。
文摘Dear Editor,This letter investigates a multi-agent path planning problem in a road network with the requirement of avoiding collisions among all agents in the partitioned environment.We first abstract the agents to a set of transition systems,and construct a team transition system from these individual systems.A mechanism is designed for the team transition system to detect all collisions within the synthesized run.
基金the Project of National Natural Science Foundation of China(Grant No.62106283)the Project of National Natural Science Foundation of China(Grant No.72001214)to provide fund for conducting experimentsthe Project of Natural Science Foundation of Shaanxi Province(Grant No.2020JQ-484)。
文摘The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.
基金supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA))supported by the National Natural Science Foundation of China under Grant No. 61971264the National Natural Science Foundation of China/Research Grants Council Collaborative Research Scheme under Grant No. 62261160390
文摘Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.
基金supported by National Natural Science Foundation of China(No.51877205)Fundamental Research Funds for the Central Universities(No.buct201906)+1 种基金the State Key Laboratory of NBC Protection for Civilian(No.SKLNBC2021-0X)Beijing Nova Program(No.2022015)。
文摘Low-temperature plasma is a green and high-efficiency technology for chemical warfare agent(CWA)decontamination.However,traditional plasma devices suffer from the problems of highpower composition and large power-supply size,which limit their practical applications.In this paper,a self-driven microplasma decontamination system,induced by a dielectric-dielectric rotary triboelectric nanogenerator(dd-r TENG),was innovatively proposed for the decontamination of CWA simulants.The microplasma was characterized via electrical measurements,optical emission spectra and ozone concentration detection.With an output voltage of-3460 V,the dd-r TENG can successfully excite microplasma in air.Reactive species,such as OH,O(1D),Hαand O3were detected.With input average power of 0.116 W,the decontamination rate of 2-chloroethyl ethyl sulfide reached 100%within 3 min of plasma treatment,while the decontamination rates of malathion and dimethyl methylphosphonate reached(65.92±1.65)%and(60.88±1.92)%after 7 min of plasma treatment,respectively.In addition,the decontamination rates gradually decreased with the increase in the simulant concentrations.Typical products were identified and analyzed.This study demonstrates the broad spectrum and feasibility of the dd-r TENG-microplasma for CWA elimination,which provides significant guidance for their practical applications in the future.
基金supported by the National Natural Science Foundation of China(61822307)。
文摘Dear Editor,The optimal formation control design problem is studied for a class of second-order multi-agent systems(MASs) with obstacle avoidance.Based on the actor-critic framework, an optimized formation controller is proposed by constructing a novel performance index function. Furthermore, the stability of MAS is proved by constructing the Lyapunov function. The simulation results are provided to depict the effectiveness of the proposed strategies.
基金supported by the Science&Technology Department of Sichuan Province(2022NSFSC0037,2022JDR0107,2020YJ0044,2021YFG0131)the Fundamental Research Funds for the Central Universities(ZYGX2020J020)the National Numerical Wind Tunnel Project,China(NNW2021ZT6-A26)。
文摘Dear Editor,This letter investigates the target enclosing control problem of multi-agent systems.A signed graph-based control strategy is presented,where the agents are steered to enclose the dynamic target from both sides as they move.This is inspired by the phenomenon that signed networks exhibit bipartite clustering if the underlying graph is structurally balanced,so that the agents may naturally enclose the zero point from opposite sides(+and.)if proper controllers are applied.By adopting a distributed observer to estimate the information of dynamic target。
文摘Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.