Pub Date : 2024-11-27DOI: 10.1109/TSMC.2024.3496564
Jiacheng Zhang;Jingjing Wang;Honggui Han;Ying Hou
Due to its demonstrated efficacy, optimal control has extensive application in nonlinear systems. However, in the optimal control process, the time delays in the optimization objectives makes the optimization problem difficult to solve. To improve the optimal control performance, a coevolution-based robust optimal control (C-TDROC) method is designed. First, a data-driven estimation strategy is proposed to approximate the optimal objectives of nonlinear systems. Then, the approximation errors caused by time delays are described as uncertain representations of system states. Second, a coevolution-based robust optimization (CRO) algorithm is developed to solve the optimal set points of system states. This algorithm generates two coevolutionary particle swarms in robust time delay intervals to improve the robustness of optimal set points. Third, an adaptive time delay controller is proposed for tracking the optimal set points. Then, the Lyapunov-Krasovskii functionals are employed to ensure the stability of C-TDROC. The experiments on a time-delay nonlinear system and a time-delay biochemical reaction process are carried out to prove the availability of C-TDROC.
{"title":"Coevolution-Based Robust Optimal Control for Nonlinear System With Time-Delay Optimal Objectives","authors":"Jiacheng Zhang;Jingjing Wang;Honggui Han;Ying Hou","doi":"10.1109/TSMC.2024.3496564","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3496564","url":null,"abstract":"Due to its demonstrated efficacy, optimal control has extensive application in nonlinear systems. However, in the optimal control process, the time delays in the optimization objectives makes the optimization problem difficult to solve. To improve the optimal control performance, a coevolution-based robust optimal control (C-TDROC) method is designed. First, a data-driven estimation strategy is proposed to approximate the optimal objectives of nonlinear systems. Then, the approximation errors caused by time delays are described as uncertain representations of system states. Second, a coevolution-based robust optimization (CRO) algorithm is developed to solve the optimal set points of system states. This algorithm generates two coevolutionary particle swarms in robust time delay intervals to improve the robustness of optimal set points. Third, an adaptive time delay controller is proposed for tracking the optimal set points. Then, the Lyapunov-Krasovskii functionals are employed to ensure the stability of C-TDROC. The experiments on a time-delay nonlinear system and a time-delay biochemical reaction process are carried out to prove the availability of C-TDROC.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1126-1136"},"PeriodicalIF":8.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TSMC.2024.3495821
Peng Zhang;Mou Chen;Zixuan Zheng
This article studies two robust adaptive dynamic programming (ADP) approaches for uncertain discrete-time (DT) nonlinear systems. Since the uncertainty is implicit in the traditional Hamilton-Jacobi–Bellman (HJB) equation, it is difficult to deal with the uncertainty. In this article, the Taylor series approximation technique is utilized to convert the traditional HJB equation into an explicit form of the uncertainty. In virtue of the first-order Taylor series approximation technique, a robust first-order approximate HJB equation is established. To further improve the approximation accuracy, a robust second-order approximate HJB equation is exploited by using the Hessian matrix of the value function. It is shown that the second-order approximate HJB equation could be extended to the uncertain DT linear systems. Aiming at obtaining the solutions of the two robust approximate HJB equations, we propose two corresponding policy iteration (PI) algorithms. More importantly, the convergence and optimality of the designed PI algorithms are clarified. Finally, a numerical case is conducted to test the validity of the designed robust DT PI ADP approaches.
研究了不确定离散时间非线性系统的两种鲁棒自适应动态规划方法。由于不确定性在传统的Hamilton-Jacobi-Bellman （HJB）方程中是隐式的，使得不确定性难以处理。本文利用泰勒级数逼近技术将传统的HJB方程转化为不确定性的显式形式。利用一阶泰勒级数逼近技术，建立了鲁棒的一阶近似HJB方程。为了进一步提高近似精度，利用值函数的Hessian矩阵建立了鲁棒二阶近似HJB方程。结果表明，二阶近似HJB方程可以推广到不确定DT线性系统。为了得到这两个鲁棒近似HJB方程的解，我们提出了两种相应的策略迭代算法。更重要的是，阐明了所设计PI算法的收敛性和最优性。最后，通过一个算例验证了所设计的稳健DT PI ADP方法的有效性。
{"title":"Robust Adaptive Dynamic Programming Control for Uncertain Discrete-Time Nonlinear Systems","authors":"Peng Zhang;Mou Chen;Zixuan Zheng","doi":"10.1109/TSMC.2024.3495821","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3495821","url":null,"abstract":"This article studies two robust adaptive dynamic programming (ADP) approaches for uncertain discrete-time (DT) nonlinear systems. Since the uncertainty is implicit in the traditional Hamilton-Jacobi–Bellman (HJB) equation, it is difficult to deal with the uncertainty. In this article, the Taylor series approximation technique is utilized to convert the traditional HJB equation into an explicit form of the uncertainty. In virtue of the first-order Taylor series approximation technique, a robust first-order approximate HJB equation is established. To further improve the approximation accuracy, a robust second-order approximate HJB equation is exploited by using the Hessian matrix of the value function. It is shown that the second-order approximate HJB equation could be extended to the uncertain DT linear systems. Aiming at obtaining the solutions of the two robust approximate HJB equations, we propose two corresponding policy iteration (PI) algorithms. More importantly, the convergence and optimality of the designed PI algorithms are clarified. Finally, a numerical case is conducted to test the validity of the designed robust DT PI ADP approaches.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1151-1162"},"PeriodicalIF":8.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/TSMC.2024.3493965
Mouquan Shen;Xianming Wang;Song Zhu;Tingwen Huang;Qing-Guo Wang
This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.
{"title":"Event-Triggered Data-Driven Control of Nonlinear Systems via Q-Learning","authors":"Mouquan Shen;Xianming Wang;Song Zhu;Tingwen Huang;Qing-Guo Wang","doi":"10.1109/TSMC.2024.3493965","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3493965","url":null,"abstract":"This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1069-1077"},"PeriodicalIF":8.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1109/TSMC.2024.3496332
Ye Liu;Yuanrong Tian;Yunlong Mi;Hui Liu;Jianqiang Wang;Witold Pedrycz
Unsupervised anomaly detection (AD) methods based on deep learning have attracted great attention in unlabeled data mining. The performance of these AD methods usually depends on the representation ability of normal patterns and the quality of training data. However, most deep unsupervised AD methods do not capture the distribution characteristics and the diversity of normal patterns effectively. In the meantime, they ignore the interference of abnormal samples on the model in training data with anomaly contamination. To tackle these issues, this article proposes a method named landmark block-embedded aggregation autoencoder (LBAA) for AD. LBAA constructs a filter and an aggregation autoencoder by introducing a novel normal feature learning approach to improve data quality and adjust its distribution differences from anomalies. In the normal feature learning, we define a landmark block to represent distribution of a normal class and an adaptive selection mechanism of landmark blocks’ number to obtain diverse normal features. On the basis, the filter is constructed to filter distinct anomalies and improve the quality of the contaminated training data. Then, a weighted objective function is proposed to train the aggregation autoencoder. The function can reduce the interference of anomalies and realize the aggregation of normal samples to increase the feature differences between normal and abnormal samples. Next, the trained aggregation autoencoder calculates the anomaly score of each sample by summing the reconstruction error and its median sparseness to the landmark blocks. Finally, we report on a comprehensive experiment on multiple datasets. The obtained results validate the effectiveness and robustness of LBAA.
{"title":"Landmark Block-Embedded Aggregation Autoencoder for Anomaly Detection","authors":"Ye Liu;Yuanrong Tian;Yunlong Mi;Hui Liu;Jianqiang Wang;Witold Pedrycz","doi":"10.1109/TSMC.2024.3496332","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3496332","url":null,"abstract":"Unsupervised anomaly detection (AD) methods based on deep learning have attracted great attention in unlabeled data mining. The performance of these AD methods usually depends on the representation ability of normal patterns and the quality of training data. However, most deep unsupervised AD methods do not capture the distribution characteristics and the diversity of normal patterns effectively. In the meantime, they ignore the interference of abnormal samples on the model in training data with anomaly contamination. To tackle these issues, this article proposes a method named landmark block-embedded aggregation autoencoder (LBAA) for AD. LBAA constructs a filter and an aggregation autoencoder by introducing a novel normal feature learning approach to improve data quality and adjust its distribution differences from anomalies. In the normal feature learning, we define a landmark block to represent distribution of a normal class and an adaptive selection mechanism of landmark blocks’ number to obtain diverse normal features. On the basis, the filter is constructed to filter distinct anomalies and improve the quality of the contaminated training data. Then, a weighted objective function is proposed to train the aggregation autoencoder. The function can reduce the interference of anomalies and realize the aggregation of normal samples to increase the feature differences between normal and abnormal samples. Next, the trained aggregation autoencoder calculates the anomaly score of each sample by summing the reconstruction error and its median sparseness to the landmark blocks. Finally, we report on a comprehensive experiment on multiple datasets. The obtained results validate the effectiveness and robustness of LBAA.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1004-1019"},"PeriodicalIF":8.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1109/TSMC.2024.3493859
Jun-Lan Wang;Xiao-Jian Li
This article studies the resilient control issue of the Markovian jump cyber-physical systems (CPSs) under stealthy integrity attacks. In order to enhance the security of the Markovian jumping CPSs, an encryption scheme based on complex dynamical networks (CDNs) is presented. Note that the existing encryption mechanism based on single-node chaotic systems cannot detect attacks when part of the encrypted information is eavesdropped by the attacker. However, the method presented here plays the advantage of complexity of CDNs in the encryption link and can still effectively identify attacks. In addition, the communication delay of data in the communication network is considered. It is shown that compared with the existing results, the encryption scheme proposed in this article does not involve the control link when realizing CDNs synchronization, thus widening the allowable range of delay. Furthermore, the synchronization of drive-response complex chaotic networks guarantees the Markovian jumping CPSs nominal performance without attacks and the stochastic input-to-state stability with attacks. In the end, two examples are given to describe that the proposed security architecture can detect attacks both in theory and simulation.
{"title":"Resilient Control of Stochastic Cyber-Physical Systems Against Stealthy Attacks: Complex Dynamical Networks Encryption Strategy","authors":"Jun-Lan Wang;Xiao-Jian Li","doi":"10.1109/TSMC.2024.3493859","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3493859","url":null,"abstract":"This article studies the resilient control issue of the Markovian jump cyber-physical systems (CPSs) under stealthy integrity attacks. In order to enhance the security of the Markovian jumping CPSs, an encryption scheme based on complex dynamical networks (CDNs) is presented. Note that the existing encryption mechanism based on single-node chaotic systems cannot detect attacks when part of the encrypted information is eavesdropped by the attacker. However, the method presented here plays the advantage of complexity of CDNs in the encryption link and can still effectively identify attacks. In addition, the communication delay of data in the communication network is considered. It is shown that compared with the existing results, the encryption scheme proposed in this article does not involve the control link when realizing CDNs synchronization, thus widening the allowable range of delay. Furthermore, the synchronization of drive-response complex chaotic networks guarantees the Markovian jumping CPSs nominal performance without attacks and the stochastic input-to-state stability with attacks. In the end, two examples are given to describe that the proposed security architecture can detect attacks both in theory and simulation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1078-1091"},"PeriodicalIF":8.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/TSMC.2024.3496694
Yang Liu;Xiaoqi Chen;Xi Wang;Zhen Su;Shiqi Fan;Zhen Wang
A great number of studies have demonstrated that many complex systems could benefit a lot from complex networks, through either a direct modeling on which dynamics among agents could be investigated in a global view or an indirect representation by the aid of that the leading factors could be captured more clearly. Hence, in the context of networks, this article copes with the continuous network dismantling problem which aims to find the key node set whose removal would break down a given network more thoroughly and thus is more capable of suppressing virus or misinformation. To achieve this goal effectively and efficiently, we propose the external-degree and internal-size component suppression (EDIS) framework based on the network percolation, where we constrain the search space by a well-designed local goal function and candidate selection approach such that EDIS could obtain better results than the-state-of-the-art in networks of millions of nodes in seconds. We also contribute two strategies with time complexity ${mathcal {O}}(mlog _{vartheta } m)$ and space complexity ${mathcal {O}}(m)$ , of networks of m edges, under such framework by well studying the evolving characteristics of the associated connected components as nodes are occupied, where $vartheta gt 1$ is a hyperparameter. Our results on 12 empirical networks from various domains demonstrate that the proposed method has far better performance than the-state-of-the-art over both effectiveness and computing time. Our study could play important roles in many real-world scenarios, such as the containment of misinformation or epidemics, the distribution of resources or vaccine, the decision of which group of individuals set to quarantine, or the detection of the resilience of a network-based system under intentional attacks.
{"title":"Efficient Continuous Network Dismantling","authors":"Yang Liu;Xiaoqi Chen;Xi Wang;Zhen Su;Shiqi Fan;Zhen Wang","doi":"10.1109/TSMC.2024.3496694","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3496694","url":null,"abstract":"A great number of studies have demonstrated that many complex systems could benefit a lot from complex networks, through either a direct modeling on which dynamics among agents could be investigated in a global view or an indirect representation by the aid of that the leading factors could be captured more clearly. Hence, in the context of networks, this article copes with the continuous network dismantling problem which aims to find the key node set whose removal would break down a given network more thoroughly and thus is more capable of suppressing virus or misinformation. To achieve this goal effectively and efficiently, we propose the external-degree and internal-size component suppression (EDIS) framework based on the network percolation, where we constrain the search space by a well-designed local goal function and candidate selection approach such that EDIS could obtain better results than the-state-of-the-art in networks of millions of nodes in seconds. We also contribute two strategies with time complexity <inline-formula> <tex-math>${mathcal {O}}(mlog _{vartheta } m)$ </tex-math></inline-formula> and space complexity <inline-formula> <tex-math>${mathcal {O}}(m)$ </tex-math></inline-formula>, of networks of m edges, under such framework by well studying the evolving characteristics of the associated connected components as nodes are occupied, where <inline-formula> <tex-math>$vartheta gt 1$ </tex-math></inline-formula> is a hyperparameter. Our results on 12 empirical networks from various domains demonstrate that the proposed method has far better performance than the-state-of-the-art over both effectiveness and computing time. Our study could play important roles in many real-world scenarios, such as the containment of misinformation or epidemics, the distribution of resources or vaccine, the decision of which group of individuals set to quarantine, or the detection of the resilience of a network-based system under intentional attacks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"976-989"},"PeriodicalIF":8.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/TSMC.2024.3487257
Xiaowei Wang;Baoshan Zhang;Shouyan Chen;Limin Wang;Zhijia Zhao;Zhijie Liu;Keum-Shik Hong
With the burgeoning growth of the maritime economy, marine risers have emerged as reliable and convenient conduits for the transport of oil and natural gas. However, these risers are vulnerable to vibrational disturbances, which can adversely impact system performance and induce fatigue damage. Therefore, effective vibration control strategies are required to address this issue. This study introduces an innovative adaptive quantized fault-tolerant control strategy designed to attenuate vibrations in a three-dimensional (3-D) riser-vessel system against the effects of actuator faults, unknown control direction, and external disturbances. Different from previous findings, the suggested controller can directly counteract the nonlinear component stemming from actuator faults and handle the nonlinear decomposition inherent to the quantizer, without the necessity for upper-limit estimation. Furthermore, to tackle the input saturation, control laws are formulated using the hyperbolic tangent operator. Finally, the proposed controller’s effectiveness and robustness are validated through thorough Lyapunov analysis and numerical simulations, affirming the system’s uniformly bounded stability.
{"title":"Adaptive Quantized Fault-Tolerant Control for a Riser-Vessel System With Unknown Control Direction and Input Saturation","authors":"Xiaowei Wang;Baoshan Zhang;Shouyan Chen;Limin Wang;Zhijia Zhao;Zhijie Liu;Keum-Shik Hong","doi":"10.1109/TSMC.2024.3487257","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3487257","url":null,"abstract":"With the burgeoning growth of the maritime economy, marine risers have emerged as reliable and convenient conduits for the transport of oil and natural gas. However, these risers are vulnerable to vibrational disturbances, which can adversely impact system performance and induce fatigue damage. Therefore, effective vibration control strategies are required to address this issue. This study introduces an innovative adaptive quantized fault-tolerant control strategy designed to attenuate vibrations in a three-dimensional (3-D) riser-vessel system against the effects of actuator faults, unknown control direction, and external disturbances. Different from previous findings, the suggested controller can directly counteract the nonlinear component stemming from actuator faults and handle the nonlinear decomposition inherent to the quantizer, without the necessity for upper-limit estimation. Furthermore, to tackle the input saturation, control laws are formulated using the hyperbolic tangent operator. Finally, the proposed controller’s effectiveness and robustness are validated through thorough Lyapunov analysis and numerical simulations, affirming the system’s uniformly bounded stability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"886-897"},"PeriodicalIF":8.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/TSMC.2024.3504793
{"title":"2023-2024 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 54","authors":"","doi":"10.1109/TSMC.2024.3504793","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3504793","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7911-8038"},"PeriodicalIF":8.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766861","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/TSMC.2024.3488779
Taotao Hu;Qiankun Song;Xiaojun Zhang;Kaibo Shi
This article investigates the security consensus problem for fractional-order multiagent systems (FOMASs) with cyber attacks via the hybrid event-triggered and impulsive control strategy. First, based on the destructive characteristics of network attacks on communication channels, a method for separating partial network structures is proposed. Differing from other studies of multiagent systems, it considers here that the communication topological network consists of strong channel and weak channel networks, and its connectivity is not required. Then, to overcome bandwidth constraints, a new hybrid event-triggered and impulsive security consensus control approach is first designed, which only needs to detect the sampled data at instants of cyber attacks and impulse. Furthermore, applying the fractional Lyapunov stability analysis methods, several sufficient conditions are proposed to achieve the exponential consensus for FOMASs with cyber attacks. Meanwhile, some parameters in security consensus control protocol are acquired. Finally, from the perspective of numerical simulation visualization, the rationality and correctness of the proposed consensus security control method are verified.
{"title":"Hybrid Event-Triggered and Impulsive Security Consensus Control Strategy for Fractional-Order Multiagent Systems With Cyber Attacks","authors":"Taotao Hu;Qiankun Song;Xiaojun Zhang;Kaibo Shi","doi":"10.1109/TSMC.2024.3488779","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3488779","url":null,"abstract":"This article investigates the security consensus problem for fractional-order multiagent systems (FOMASs) with cyber attacks via the hybrid event-triggered and impulsive control strategy. First, based on the destructive characteristics of network attacks on communication channels, a method for separating partial network structures is proposed. Differing from other studies of multiagent systems, it considers here that the communication topological network consists of strong channel and weak channel networks, and its connectivity is not required. Then, to overcome bandwidth constraints, a new hybrid event-triggered and impulsive security consensus control approach is first designed, which only needs to detect the sampled data at instants of cyber attacks and impulse. Furthermore, applying the fractional Lyapunov stability analysis methods, several sufficient conditions are proposed to achieve the exponential consensus for FOMASs with cyber attacks. Meanwhile, some parameters in security consensus control protocol are acquired. Finally, from the perspective of numerical simulation visualization, the rationality and correctness of the proposed consensus security control method are verified.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"830-842"},"PeriodicalIF":8.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1109/TSMC.2024.3492324
Yisen Huang;Jian Li;Weibing Li;Xue Zhang;Yichong Sun;Ke Xie;Yingbai Hu;Philip Wai Yan Chiu;Zheng Li
In minimally invasive surgery (MIS), the field of view (FOV) control is crucial. Autonomous endoscope robots have been developed to facilitate MIS procedures by enabling autonomous surgical target tracking, thus reducing the workload on surgeons. However, existing visual servoing-based target tracking methods for autonomous endoscopes often overlook the insecurity stemming from restricted workspace conditions. Instances, such as collisions between the endoscope robot’s tip and the patient’s chest or abdominal wall pose risks to patient tissue, while extensive motion of the endoscope shaft may damage incision port tissue. Addressing these security concerns, this article proposes a novel approach called virtual fixture-based restricted workspace constraint (RWSC) to reconstruct the endoscope robot’s movement range. A quadratic programming (QP) optimization framework is employed to govern the robot’s motion, ensuring autonomous target tracking while adhering to RWSCs. To solve the QP problem, we propose an adaptive zeroing neural network (ZNN) featuring a newly designed activation function (AF). This AF enhances the ZNN with predefined-time convergence and noise rejection capabilities, making it especially suitable for time-sensitive and noise-prone surgical applications. Theoretical analysis and experimental results demonstrate that our adaptive ZNN achieves shorter convergence times than existing neural dynamic-based QP solvers. Physical validations show the efficacy of the proposed RWSCs in limiting the workspace of the endoscope robot, while the FOV control strategy enables autonomous target tracking of flexible endoscopes under diverse constraints and objectives.
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