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 investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems(MASs) with input saturation. According to the coupling of input saturation and denial of service(DoS) attacks, imp...This paper investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems(MASs) with input saturation. According to the coupling of input saturation and denial of service(DoS) attacks, impulsive control for MASs becomes extremely challenging. Considering general DoS attacks,this paper provides the sufficient conditions for the almost sure consensus of the MASs with input saturation, where the error system can achieve almost sure local exponential stability.Through linear matrix inequalities(LMIs), the relation between the trajectory boundary and DoS attacks is characterized, and the trajectory boundary is estimated. Furthermore, an optimization method of the domain of attraction is proposed to maximize the size. And a non-conservative and practical boundary is proposed to characterize the effect of DoS attacks on MASs. Finally, considering a multi-agent system with typical Chua's circuit dynamic model, an example is provided to illustrate the theorems' correctness.展开更多
Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networ...Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.展开更多
The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criter...The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an alternative.In this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios.Consequently,an MCDM approach is essential to address these emerging challenges.While the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the ambiguity.The results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of 0.1946.In contrast,the Hop count is identified as the least significant criterion,with a weighted score of 0.1090.The remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring 0.1472.In terms of ranking,SvN-FDOSM has two approaches:individual and group.Both the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under F-DoSA.The sensitivity analysis provides a high stability in ranking among all 10 scenarios.This approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Dear Editor,This letter deals with the stabilization of a resilient model predictive control(MPC)algorithm with a dynamic event-triggered mechanism subject to Denial-of-Service(Do S)attacks.Different from previous wor...Dear Editor,This letter deals with the stabilization of a resilient model predictive control(MPC)algorithm with a dynamic event-triggered mechanism subject to Denial-of-Service(Do S)attacks.Different from previous works,this letter is based on the designed threshold function to dynamically trigger and gives the upper bound conditions for intersampling intervals with attack and attack-free scenarios to converge.展开更多
Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited late...Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited latency.DDoS attacks pose significant risks to entrepreneurial businesses,preventing legitimate customers from accessing their websites.These attacks require intelligent analytics before processing service requests.Distributed denial of service(DDoS)attacks exploit vulnerabilities in IoT devices by launchingmulti-point distributed attacks.These attacks generate massive traffic that overwhelms the victim’s network,disrupting normal operations.The consequences of distributed denial of service(DDoS)attacks are typically more severe in software-defined networks(SDNs)than in traditional networks.The centralised architecture of these networks can exacerbate existing vulnerabilities,as these weaknesses may not be effectively addressed in this model.The preliminary objective for detecting and mitigating distributed denial of service(DDoS)attacks in software-defined networks(SDN)is to monitor traffic patterns and identify anomalies that indicate distributed denial of service(DDoS)attacks.It implements measures to counter the effects ofDDoS attacks,and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats.The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks.The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
针对网络化电力系统在分布式拒绝服务(Distributed Denial of Service,DDoS)攻击与控制器增益偏差并存导致的频率调节退化问题,提出一种将非脆弱比例积分(Non-fragile Proportional Integral,NFPI)与Q-learning自适应采样结合的采样数...针对网络化电力系统在分布式拒绝服务(Distributed Denial of Service,DDoS)攻击与控制器增益偏差并存导致的频率调节退化问题,提出一种将非脆弱比例积分(Non-fragile Proportional Integral,NFPI)与Q-learning自适应采样结合的采样数据负荷频率控制(Load Frequency Control,LFC)框架。采用伯努利随机门控刻画由DDoS引发的指令丢失与阻塞,构建含状态、采样保持与能量积分项的Lyapunov-Krasovskii泛函,并配合线性矩阵不等式(Linear Matrix Inequalities,LMIs)给出H∞稳定性判据与控制器综合。同时以频差和攻击指示等构成学习状态,将候选采样间隔离散为动作空间并设计兼顾误差与通信代价的回报函数以在线优化采样策略。仿真表明,在较高攻击概率下该方案仍能实现频率快速且近乎无过冲的收敛,并在保持可比稳态精度的前提下显著减少采样与控制更新,从而在增益不确定与对抗性丢包并存时兼顾鲁棒性与通信效率,适于现代网络化电力系统部署。展开更多
针对车联网中拒绝服务(denial of service,DoS)攻击难以防范且现有监督学习方法无法有效检测零日攻击的问题,提出了一种混合DoS攻击入侵检测系统.首先,对数据集进行预处理,提高数据的质量;其次,利用特征选择滤除冗余特征,旨在获得代表...针对车联网中拒绝服务(denial of service,DoS)攻击难以防范且现有监督学习方法无法有效检测零日攻击的问题,提出了一种混合DoS攻击入侵检测系统.首先,对数据集进行预处理,提高数据的质量;其次,利用特征选择滤除冗余特征,旨在获得代表性更强的特征;再次,采用集成学习方法将5种基于树结构的监督分类器堆叠集成用于检测已知DoS攻击;最后,提出了一种无监督异常检测方法,将卷积去噪自动编码器与注意力机制相结合来建立正常行为模型,用于检测堆叠集成模型漏报的未知DoS攻击.实验结果表明,对于已知DoS攻击检测,所提系统在Car-Hacking数据集和CICIDS2017数据集上的检测准确率分别为100%和99.967%;对于未知DoS攻击检测,所提系统在上述两个数据集上的检测准确率分别为100%和83.953%,并且在两个数据集上的平均测试时间分别为0.072 ms和0.157 ms,验证了所提系统的有效性和可行性.展开更多
Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumpti...Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.展开更多
Dear Editor,This letter is concerned with distributed resilient platoon control of multiple vehicles subject to denial-of-service(DoS)attacks.In order to accommodate the effects of DoS attacks,a fully resilient distri...Dear Editor,This letter is concerned with distributed resilient platoon control of multiple vehicles subject to denial-of-service(DoS)attacks.In order to accommodate the effects of DoS attacks,a fully resilient distributed control strategy is presented by designing an adaptive control gain,where any global information of communication topology is no longer required.Then,the conditions of time duration rates and frequency of DoS attacks on stability of vehicular platoons can be characterized.Finally,a numerical simulation example is given to validate the obtained results.展开更多
This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-d...This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.展开更多
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(62373302,62333009)
文摘This paper investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems(MASs) with input saturation. According to the coupling of input saturation and denial of service(DoS) attacks, impulsive control for MASs becomes extremely challenging. Considering general DoS attacks,this paper provides the sufficient conditions for the almost sure consensus of the MASs with input saturation, where the error system can achieve almost sure local exponential stability.Through linear matrix inequalities(LMIs), the relation between the trajectory boundary and DoS attacks is characterized, and the trajectory boundary is estimated. Furthermore, an optimization method of the domain of attraction is proposed to maximize the size. And a non-conservative and practical boundary is proposed to characterize the effect of DoS attacks on MASs. Finally, considering a multi-agent system with typical Chua's circuit dynamic model, an example is provided to illustrate the theorems' correctness.
基金supported by the National Natural Science Foundation of China(61433003,60904003,11602019).
文摘Dear Editor,This letter studies the stabilization control issue of cyber-physical systems with time-varying delays and aperiodic denial-of-service(DoS)attacks.To address the calculation overload issue caused by networked predictive control(NPC)approach,an event-based NPC method is proposed.Within the proposed method,the negative effects of time-varying delays and DoS attacks on system performance are compensated.Then,sufficient and necessary conditions are derived to ensure the stability of the closed-loop system.In the end,simulation results are provided to demonstrate the validity of presented method.
文摘The implementation of Countermeasure Techniques(CTs)in the context of Network-On-Chip(NoC)based Multiprocessor System-On-Chip(MPSoC)routers against the Flooding Denial-of-Service Attack(F-DoSA)falls under Multi-Criteria Decision-Making(MCDM)due to the three main concerns,called:traffic variations,multiple evaluation criteria-based traffic features,and prioritization NoC routers as an alternative.In this study,we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios.Consequently,an MCDM approach is essential to address these emerging challenges.While the recent MCDM approach has some issues,such as uncertainty,this study utilizes Fuzzy-Weighted Zero-Inconsistency(FWZIC)to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method(FDOSM)for ranking the routers with fuzzy Single-valued Neutrosophic under names(SvN-FWZIC and SvN-FDOSM)to overcome the ambiguity.The results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria,with a weighted score of 0.1946.In contrast,the Hop count is identified as the least significant criterion,with a weighted score of 0.1090.The remaining criteria fall within a range of intermediate importance,with enqueue time scoring 0.1845,packet count decremented and traversal index scoring 0.1262,packet count incremented scoring 0.1124,and packet count index scoring 0.1472.In terms of ranking,SvN-FDOSM has two approaches:individual and group.Both the individual and group ranking processes show that(Router 4)is the most effective router,while(Router 3)is the lowest router under F-DoSA.The sensitivity analysis provides a high stability in ranking among all 10 scenarios.This approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
文摘Dear Editor,This letter deals with the stabilization of a resilient model predictive control(MPC)algorithm with a dynamic event-triggered mechanism subject to Denial-of-Service(Do S)attacks.Different from previous works,this letter is based on the designed threshold function to dynamically trigger and gives the upper bound conditions for intersampling intervals with attack and attack-free scenarios to converge.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited latency.DDoS attacks pose significant risks to entrepreneurial businesses,preventing legitimate customers from accessing their websites.These attacks require intelligent analytics before processing service requests.Distributed denial of service(DDoS)attacks exploit vulnerabilities in IoT devices by launchingmulti-point distributed attacks.These attacks generate massive traffic that overwhelms the victim’s network,disrupting normal operations.The consequences of distributed denial of service(DDoS)attacks are typically more severe in software-defined networks(SDNs)than in traditional networks.The centralised architecture of these networks can exacerbate existing vulnerabilities,as these weaknesses may not be effectively addressed in this model.The preliminary objective for detecting and mitigating distributed denial of service(DDoS)attacks in software-defined networks(SDN)is to monitor traffic patterns and identify anomalies that indicate distributed denial of service(DDoS)attacks.It implements measures to counter the effects ofDDoS attacks,and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats.The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks.The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
文摘针对网络化电力系统在分布式拒绝服务(Distributed Denial of Service,DDoS)攻击与控制器增益偏差并存导致的频率调节退化问题,提出一种将非脆弱比例积分(Non-fragile Proportional Integral,NFPI)与Q-learning自适应采样结合的采样数据负荷频率控制(Load Frequency Control,LFC)框架。采用伯努利随机门控刻画由DDoS引发的指令丢失与阻塞,构建含状态、采样保持与能量积分项的Lyapunov-Krasovskii泛函,并配合线性矩阵不等式(Linear Matrix Inequalities,LMIs)给出H∞稳定性判据与控制器综合。同时以频差和攻击指示等构成学习状态,将候选采样间隔离散为动作空间并设计兼顾误差与通信代价的回报函数以在线优化采样策略。仿真表明,在较高攻击概率下该方案仍能实现频率快速且近乎无过冲的收敛,并在保持可比稳态精度的前提下显著减少采样与控制更新,从而在增益不确定与对抗性丢包并存时兼顾鲁棒性与通信效率,适于现代网络化电力系统部署。
文摘针对车联网中拒绝服务(denial of service,DoS)攻击难以防范且现有监督学习方法无法有效检测零日攻击的问题,提出了一种混合DoS攻击入侵检测系统.首先,对数据集进行预处理,提高数据的质量;其次,利用特征选择滤除冗余特征,旨在获得代表性更强的特征;再次,采用集成学习方法将5种基于树结构的监督分类器堆叠集成用于检测已知DoS攻击;最后,提出了一种无监督异常检测方法,将卷积去噪自动编码器与注意力机制相结合来建立正常行为模型,用于检测堆叠集成模型漏报的未知DoS攻击.实验结果表明,对于已知DoS攻击检测,所提系统在Car-Hacking数据集和CICIDS2017数据集上的检测准确率分别为100%和99.967%;对于未知DoS攻击检测,所提系统在上述两个数据集上的检测准确率分别为100%和83.953%,并且在两个数据集上的平均测试时间分别为0.072 ms和0.157 ms,验证了所提系统的有效性和可行性.
基金supported in part by Australian Research Council Discovery Early Career Researcher Award(DE210100273)。
文摘Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.
基金supported by the National Natural Science Foundation of China(NSFC)(62073171,62173181)the Natural Science Foundation of Jiangsu Province(BK20200744,BK20180455)+3 种基金Jiangsu Specially-Appointed Professor(RK043 STP19001)1311 Talent Plan of Nanjing University of Posts and Telecommunications,the Fundamental Research Funds for the Central Universities(30920032203)the Natural Science Foundation of Tianjin(20JCQNJC00390)Graduate Scientific Research Innovation Project of Tianjin(2021YJSO2S31).
文摘Dear Editor,This letter is concerned with distributed resilient platoon control of multiple vehicles subject to denial-of-service(DoS)attacks.In order to accommodate the effects of DoS attacks,a fully resilient distributed control strategy is presented by designing an adaptive control gain,where any global information of communication topology is no longer required.Then,the conditions of time duration rates and frequency of DoS attacks on stability of vehicular platoons can be characterized.Finally,a numerical simulation example is given to validate the obtained results.
基金supported by the National Natural Science Foundation of China(61773056)the Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(USTB)(BK19AE018)+2 种基金the Fundamental Research Funds for the Central Universities of USTB(FRF-TP-20-09B,230201606500061,FRF-DF-20-35,FRF-BD-19-002A)supported by Zhejiang Natural Science Foundation(LD21F030001)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and Information and Communications Technology)(NRF-2020R1A2C1005449)。
文摘This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.
