This paper studies an indefinite mean-field game with Markov jump parameters, where all agents' diffusion terms depend on control variables and both state and control average terms $(x^{(N)}, u^{(N)})$ are considered. One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs, allowing for zero or even negative values. By virtue of mean-field methods and decomposition techniques, we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system. These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition. The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition (RC) and the decomposition technique. Furthermore, we demonstrate that the resulting decentralized strategies achieve an $epsilon$-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function. Finally, our theoretical results are applied to resolve a class of mean-variance portfolio selection problems. We provide corresponding numerical simulation results and economic explanations.
{"title":"Indefinite Linear-Quadratic Mean-Field Game of Regime-Switching System","authors":"Tian Chen;Kai Du;Zhen Wu","doi":"10.1109/JAS.2025.125456","DOIUrl":"https://doi.org/10.1109/JAS.2025.125456","url":null,"abstract":"This paper studies an indefinite mean-field game with Markov jump parameters, where all agents' diffusion terms depend on control variables and both state and control average terms <tex>$(x^{(N)}, u^{(N)})$</tex> are considered. One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs, allowing for zero or even negative values. By virtue of mean-field methods and decomposition techniques, we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system. These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition. The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition (RC) and the decomposition technique. Furthermore, we demonstrate that the resulting decentralized strategies achieve an <tex>$epsilon$</tex>-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function. Finally, our theoretical results are applied to resolve a class of mean-variance portfolio selection problems. We provide corresponding numerical simulation results and economic explanations.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"83-97"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082251","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}
An attack-resilient distributed Nash equilibrium (NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks, i.e., false data injection (FDI) attacks. Different from many existing distributed NE seeking works, it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks. An attack-resilient NE seeking algorithm that is distributed (i.e., independent of global information on the graph's algebraic connectivity, Lipschitz and monotone constants of pseudo-gradients, or number of players), is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically. Another key characteristic is that FDI attacks are allowed to be unknown and unbounded. By exploiting nonsmooth analysis and stability theory, the global asymptotic convergence of the developed algorithm to the NE is ensured. Moreover, we extend this design to further consider the attack-resilient NE seeking of double-integrator players. Lastly, numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.
{"title":"Attack-Resilient Distributed Nash Equilibrium Seeking for Networked Games Under Unbounded FDI Attacks: Theory and Experiment","authors":"Zhi Feng;Zhexin Shi;Xiwang Dong;Guoqiang Hu;Jinhu Lv","doi":"10.1109/JAS.2025.125486","DOIUrl":"https://doi.org/10.1109/JAS.2025.125486","url":null,"abstract":"An attack-resilient distributed Nash equilibrium (NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks, i.e., false data injection (FDI) attacks. Different from many existing distributed NE seeking works, it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks. An attack-resilient NE seeking algorithm that is distributed (i.e., independent of global information on the graph's algebraic connectivity, Lipschitz and monotone constants of pseudo-gradients, or number of players), is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically. Another key characteristic is that FDI attacks are allowed to be unknown and unbounded. By exploiting nonsmooth analysis and stability theory, the global asymptotic convergence of the developed algorithm to the NE is ensured. Moreover, we extend this design to further consider the attack-resilient NE seeking of double-integrator players. Lastly, numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"98-109"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082316","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}
In this paper, a security defense issue is investigated for networked control systems susceptible to stochastic denial of service (DoS) attacks by using the sliding mode control method. To utilize network communication resources more effectively, a novel adaptive event-triggered (AET) mechanism is introduced, whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states. Differing from existing event-triggered (ET) mechanisms, the proposed one demonstrates exceptional relevance and flexibility. It is closely related to attack probability, and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack. To leverage attacker information more effectively, a switching-like sliding mode security controller is designed, which can autonomously select different controller gains based on the sliding function representing the attack situation. Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface. Compared with existing time-invariant control strategies within the triggered interval, more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy. Finally, a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.
{"title":"Switching-Like Sliding Mode Security Control Against DoS Attacks: A Novel Attack-Related Adaptive Event-Triggered Scheme","authors":"Jiancun Wu;Zhiru Cao;Engang Tian;Chen Peng","doi":"10.1109/JAS.2025.125189","DOIUrl":"https://doi.org/10.1109/JAS.2025.125189","url":null,"abstract":"In this paper, a security defense issue is investigated for networked control systems susceptible to stochastic denial of service (DoS) attacks by using the sliding mode control method. To utilize network communication resources more effectively, a novel adaptive event-triggered (AET) mechanism is introduced, whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states. Differing from existing event-triggered (ET) mechanisms, the proposed one demonstrates exceptional relevance and flexibility. It is closely related to attack probability, and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack. To leverage attacker information more effectively, a switching-like sliding mode security controller is designed, which can autonomously select different controller gains based on the sliding function representing the attack situation. Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface. Compared with existing time-invariant control strategies within the triggered interval, more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy. Finally, a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"137-148"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082245","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}
This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period, despite unknown actuation characteristics and potential fading powering faults. By performing deliberately designed coordinate transformations on the tracking error, the complex and demanding problem of “reaching specified precision within a given time” is transformed into a bounded control problem, facilitating the development of the control scheme. To enhance practicality, the design incorporates smooth function fitting and dynamic surface control techniques. Additionally, the proposed control algorithm is robust to faults, effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention. Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.
{"title":"Fault-Tolerant Control Achieving Prescribed Tracking Accuracy Within Given Time for Euler-Lagrange Systems Under Unknown Actuation Characteristics and Fading Powering Faults","authors":"Jie Su;Yongduan Song","doi":"10.1109/JAS.2025.125453","DOIUrl":"https://doi.org/10.1109/JAS.2025.125453","url":null,"abstract":"This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period, despite unknown actuation characteristics and potential fading powering faults. By performing deliberately designed coordinate transformations on the tracking error, the complex and demanding problem of “reaching specified precision within a given time” is transformed into a bounded control problem, facilitating the development of the control scheme. To enhance practicality, the design incorporates smooth function fitting and dynamic surface control techniques. Additionally, the proposed control algorithm is robust to faults, effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention. Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"72-82"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082250","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}
The exponential growth of video content has driven significant advancements in video summarization techniques in recent years. Breakthroughs in deep learning have been particularly transformative, enabling more effective detection of key information and creating new possibilities for video synopsis. To summarize recent progress and accelerate research in this field, this paper provides a comprehensive review of deep learning-based video summarization methods developed over the past decade. We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization. Subsequently, we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems, offering researchers a clear roadmap of the field's evelution. Unlike previous review works, we first classify research papers based on the structural hierarchy of the video (from frame-level to shot-level to video-level), then further categorize them according to the summary backbone model (feature extraction and spatiotemporal modeling). This approach provides a more systematic and hierarchical organization of the documents. Following this comprehensive review, we summarize the benchmark datasets and evaluation metrics commonly employed in the field. Finally, we analyze persistent challenges and propose insightful directions for future research, providing a forward-looking perspective on video summarization technologies. This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.
{"title":"Deep Learning for Video Summarization: Systematic Review, Challenges and Opportunities","authors":"Qinghao Yu;Zidong Wang;Guoliang Wei;Hui Yu","doi":"10.1109/JAS.2025.125864","DOIUrl":"https://doi.org/10.1109/JAS.2025.125864","url":null,"abstract":"The exponential growth of video content has driven significant advancements in video summarization techniques in recent years. Breakthroughs in deep learning have been particularly transformative, enabling more effective detection of key information and creating new possibilities for video synopsis. To summarize recent progress and accelerate research in this field, this paper provides a comprehensive review of deep learning-based video summarization methods developed over the past decade. We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization. Subsequently, we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems, offering researchers a clear roadmap of the field's evelution. Unlike previous review works, we first classify research papers based on the structural hierarchy of the video (from frame-level to shot-level to video-level), then further categorize them according to the summary backbone model (feature extraction and spatiotemporal modeling). This approach provides a more systematic and hierarchical organization of the documents. Following this comprehensive review, we summarize the benchmark datasets and evaluation metrics commonly employed in the field. Finally, we analyze persistent challenges and propose insightful directions for future research, providing a forward-looking perspective on video summarization technologies. This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"21-42"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082194","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}
Dear Editor, This letter studies the problem of stealthy attacks targeting stochastic event-based estimation, alongside proposing measures for their mitigation. A general attack framework is introduced, and the corresponding stealthiness condition is analyzed. To enhance system security, we advocate for a single-dimensional encryption method, showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.
{"title":"Single-Dimensional Encryption Against Stealthy Attacks on Stochastic Event-Based Estimation","authors":"Jun Shang;Di Zhao;Hanwen Zhang;Dawei Shi","doi":"10.1109/JAS.2025.125381","DOIUrl":"https://doi.org/10.1109/JAS.2025.125381","url":null,"abstract":"Dear Editor, This letter studies the problem of stealthy attacks targeting stochastic event-based estimation, alongside proposing measures for their mitigation. A general attack framework is introduced, and the corresponding stealthiness condition is analyzed. To enhance system security, we advocate for a single-dimensional encryption method, showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"233-235"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11369925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082153","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}
Zhong-Hua Pang;Tong Mu;Yi Yu;Haibin Guo;Guo-Ping Liu;Qing-Long Han
Networked predictive control (NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems (NCSs), such as network-induced delays, packet dropouts, and packet disorders. Despite significant advancements, the increasing complexity and dynamism of network environments, along with the growing complexity of systems, pose new challenges for NPC. These challenges include difficulties in system modeling, cyber attacks, component faults, limited network bandwidth, and the necessity for distributed collaboration. This survey aims to provide a comprehensive review of NPC strategies. It begins with a summary of the primary challenges faced by NCSs, followed by an introduction to the control structure and core concepts of NPC. The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control, fault-tolerant control, distributed coordinated control, and event-triggered control. Moreover, it reviews notable works that have implemented these schemes. Finally, the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.
{"title":"Networked Predictive Control: A Survey","authors":"Zhong-Hua Pang;Tong Mu;Yi Yu;Haibin Guo;Guo-Ping Liu;Qing-Long Han","doi":"10.1109/JAS.2025.125234","DOIUrl":"https://doi.org/10.1109/JAS.2025.125234","url":null,"abstract":"Networked predictive control (NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems (NCSs), such as network-induced delays, packet dropouts, and packet disorders. Despite significant advancements, the increasing complexity and dynamism of network environments, along with the growing complexity of systems, pose new challenges for NPC. These challenges include difficulties in system modeling, cyber attacks, component faults, limited network bandwidth, and the necessity for distributed collaboration. This survey aims to provide a comprehensive review of NPC strategies. It begins with a summary of the primary challenges faced by NCSs, followed by an introduction to the control structure and core concepts of NPC. The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control, fault-tolerant control, distributed coordinated control, and event-triggered control. Moreover, it reviews notable works that have implemented these schemes. Finally, the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"3-20"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082207","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}
Hoang Minh Pham;Anh Dong Le;Pablo Malvido-Fresnillo;Saigopal Vasudevan;José L. Martínez Lastra
Dear Editor, This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products, a critical step toward automating food production lines. Accurate segmentation is essential for addressing challenges such as occlusions, indistinct edges, and stacked configurations, which demand large, diverse datasets. To meet these demands, we propose two complementary approaches: a semi-automatic annotation interface using tools like the segment anything model (SAM) and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models. These methods reduce reliance on real meat, mitigate food waste, and improve scalability. Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and, when combined with real data, further boosts accuracy, paving the way for more efficient automation in the food industry.
{"title":"Efficient Dataset Generation for Stacked Meat Products Instance Segmentation in Food Automation","authors":"Hoang Minh Pham;Anh Dong Le;Pablo Malvido-Fresnillo;Saigopal Vasudevan;José L. Martínez Lastra","doi":"10.1109/JAS.2025.125798","DOIUrl":"https://doi.org/10.1109/JAS.2025.125798","url":null,"abstract":"Dear Editor, This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products, a critical step toward automating food production lines. Accurate segmentation is essential for addressing challenges such as occlusions, indistinct edges, and stacked configurations, which demand large, diverse datasets. To meet these demands, we propose two complementary approaches: a semi-automatic annotation interface using tools like the segment anything model (SAM) and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models. These methods reduce reliance on real meat, mitigate food waste, and improve scalability. Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and, when combined with real data, further boosts accuracy, paving the way for more efficient automation in the food industry.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"224-226"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11369910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082160","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}
In this paper, we propose a learning algorithm termed linear multistep adaptive moment (LMAdam) to enhance the adaptive moment (Adam) algorithm for machine learning. Considering Adam as a single-step discretization of its continuous counterpart, we develop the LMAdam algorithm based on a linear multistep discretization scheme. We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms. We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.
{"title":"LMAdam: Enhancing Adam via Linear Multistep Discretization","authors":"Liangming Chen;Longbang Wang;Long Jin;Jun Wang","doi":"10.1109/JAS.2025.125834","DOIUrl":"https://doi.org/10.1109/JAS.2025.125834","url":null,"abstract":"In this paper, we propose a learning algorithm termed linear multistep adaptive moment (LMAdam) to enhance the adaptive moment (Adam) algorithm for machine learning. Considering Adam as a single-step discretization of its continuous counterpart, we develop the LMAdam algorithm based on a linear multistep discretization scheme. We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms. We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"161-169"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082299","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}
This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems. The motivation mainly comes from the following two challenges: the undesired singularity problem arising from infinite control gains at the prescribed-time instant, the effective tradeoff between the control amplitude and the triggering duration. The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold. Utilizing the designed control strategy, the solutions' existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions. Better than existing prescribed-time methods, our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth, and converge to the origin exponentially, but also ensures that the controller continuously works on the whole-time horizon. Two illustrative examples are given to show the effectiveness of the devised scheme.
{"title":"Global Adaptive Event-Triggered Designated-Time Stabilization of Uncertain Nonlinear Systems","authors":"Jiao-Jiao Li;Zong-Yao Sun;Changyun Wen;Chih-Chiang Chen","doi":"10.1109/JAS.2025.125558","DOIUrl":"https://doi.org/10.1109/JAS.2025.125558","url":null,"abstract":"This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems. The motivation mainly comes from the following two challenges: the undesired singularity problem arising from infinite control gains at the prescribed-time instant, the effective tradeoff between the control amplitude and the triggering duration. The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold. Utilizing the designed control strategy, the solutions' existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions. Better than existing prescribed-time methods, our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth, and converge to the origin exponentially, but also ensures that the controller continuously works on the whole-time horizon. Two illustrative examples are given to show the effectiveness of the devised scheme.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 1","pages":"110-122"},"PeriodicalIF":19.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082281","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}
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