Pub Date : 2026-02-06DOI: 10.1109/tase.2026.3661045
Jiangming Xu, Xiang Zhang, Jun Cheng, Ruonan Liu, Weidong Zhang
{"title":"GA-Assisted Event-Triggered Fault Detection for Networked Systems Under DoS Attacks","authors":"Jiangming Xu, Xiang Zhang, Jun Cheng, Ruonan Liu, Weidong Zhang","doi":"10.1109/tase.2026.3661045","DOIUrl":"https://doi.org/10.1109/tase.2026.3661045","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"35 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/TASE.2026.3656787
Mohamed Zaery;Syed Muhammad Amrr;Abdullah Abushokor;S. M. Suhail Hussain;Mujahed Al-Dhaifallah;Leonid Fridman;Mohammad A. Abido
This paper proposes a robust distributed secondary control strategy for AC microgrids (MGs) that ensures voltage and frequency regulation within a predefined time limit, while effectively mitigating external disturbances. The proposed composite controller integrates the predefined time convergence approach with a fixed-time integral sliding mode control (ISMC) design. The ISMC enhances disturbance rejection, while the predefined time technique guarantees that all system trajectories reach their desired values within a user-specified timeframe, independent of initial conditions. This ensures accurate regulation of distributed generators’ voltages and frequencies, along with optimal active power sharing and equalized reactive power allocation. Theoretical analysis based on Lyapunov stability confirms the convergence and robustness of the proposed scheme. Multiple simulation and hardware-in-the-loop case studies validate the superior performance of the proposed method over existing time-based controllers, achieving up to 66% lower voltage ITSE and 91% lower frequency ITAE. This confirms its fast restoration capability and strong disturbance rejection across diverse operating conditions. Note to Practitioners—With the increasing penetration of renewable energy sources such as solar and wind, ensuring fast, reliable, and decentralized control in AC MGs has become essential for maintaining stability under uncertain and fluctuating operating conditions. The proposed strategy enables engineers to explicitly define the system’s response time, irrespective of initial conditions, while ensuring precise voltage and frequency regulation and maintaining optimal active and proportional reactive power sharing among multiple generators. This capability is particularly beneficial for real-time operation in isolated MGs, renewable-dominated systems, and mission-critical energy infrastructures. Additionally, the method is designed to remain robust under external disturbances, communication delays, and system noise, without requiring complex tuning or frequent recalibration. These attributes make the proposed controller a practical and effective solution for improving responsiveness, stability, and operational reliability in modern MG applications.
{"title":"Robust Predefined-Time Frequency and Voltage Control for AC Microgrid Under Disturbances","authors":"Mohamed Zaery;Syed Muhammad Amrr;Abdullah Abushokor;S. M. Suhail Hussain;Mujahed Al-Dhaifallah;Leonid Fridman;Mohammad A. Abido","doi":"10.1109/TASE.2026.3656787","DOIUrl":"10.1109/TASE.2026.3656787","url":null,"abstract":"This paper proposes a robust distributed secondary control strategy for AC microgrids (MGs) that ensures voltage and frequency regulation within a predefined time limit, while effectively mitigating external disturbances. The proposed composite controller integrates the predefined time convergence approach with a fixed-time integral sliding mode control (ISMC) design. The ISMC enhances disturbance rejection, while the predefined time technique guarantees that all system trajectories reach their desired values within a user-specified timeframe, independent of initial conditions. This ensures accurate regulation of distributed generators’ voltages and frequencies, along with optimal active power sharing and equalized reactive power allocation. Theoretical analysis based on Lyapunov stability confirms the convergence and robustness of the proposed scheme. Multiple simulation and hardware-in-the-loop case studies validate the superior performance of the proposed method over existing time-based controllers, achieving up to 66% lower voltage ITSE and 91% lower frequency ITAE. This confirms its fast restoration capability and strong disturbance rejection across diverse operating conditions. Note to Practitioners—With the increasing penetration of renewable energy sources such as solar and wind, ensuring fast, reliable, and decentralized control in AC MGs has become essential for maintaining stability under uncertain and fluctuating operating conditions. The proposed strategy enables engineers to explicitly define the system’s response time, irrespective of initial conditions, while ensuring precise voltage and frequency regulation and maintaining optimal active and proportional reactive power sharing among multiple generators. This capability is particularly beneficial for real-time operation in isolated MGs, renewable-dominated systems, and mission-critical energy infrastructures. Additionally, the method is designed to remain robust under external disturbances, communication delays, and system noise, without requiring complex tuning or frequent recalibration. These attributes make the proposed controller a practical and effective solution for improving responsiveness, stability, and operational reliability in modern MG applications.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4198-4212"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/tase.2026.3662192
Ying Jing, Hong Zheng, Yuchuan Ji
{"title":"Prior-Guided and Gaussian Mixture-Refined Network for Industrial Anomaly Detection and Localization","authors":"Ying Jing, Hong Zheng, Yuchuan Ji","doi":"10.1109/tase.2026.3662192","DOIUrl":"https://doi.org/10.1109/tase.2026.3662192","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"27 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/tase.2026.3662003
Ming Sun, Yu Wang, Bo Yang, Li He, Hong Zhang
{"title":"Accurate and Robust UWB Localization with Incomplete Measurements based on Multi-Modal Diffusion Model","authors":"Ming Sun, Yu Wang, Bo Yang, Li He, Hong Zhang","doi":"10.1109/tase.2026.3662003","DOIUrl":"https://doi.org/10.1109/tase.2026.3662003","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/TASE.2026.3661872
Rui Liu;Cong Wu;Yifan Zhang;Haiying Song;Fei Yuan;Wei Dai;Wen Jung Li;Jun Liu
Accurate and robust cell detection in microscopic images is a fundamental yet challenging task due to diverse imaging conditions, dense cell distributions, and morphological variability. In this study, we propose FACellDet, a novel dual-branch hierarchical encoder-decoder framework that integrates adaptive feature alignment and a new supervisory signal to enhance cell detection. Specifically, a Coordinate-Attention-based Feature Alignment (CAFA) module is introduced to address spatial misalignment during multi-scale feature fusion, substantially improving cell detection precision. Furthermore, we design a Focal Attenuated Distance (FAD) map as an intermediate representation, providing highly discriminative and spatially informative cues, particularly in crowded regions. FACellDet features a dual-branch architecture, with the main branch predicting FAD maps for cell detection, while the auxiliary branch generates density maps to estimate cell counts for suppressing false detections. Extensive experiments on diverse cell types and imaging modalities from multiple public and in-house datasets demonstrate that our approach outperforms state-of-the-art methods in detection accuracy, while maintaining strong adaptability and robustness across challenging biomedical imaging scenarios. These results underscore the potential of FACellDet as an accurate and generalizable solution for automated cell detection in heterogeneous microscopic cell images, thereby facilitating reliable cell analysis to accelerate biomedical research and clinical workflows. Note to Practitioners—This work addresses the need for accurate and efficient cell detection and counting in biomedical images, where manual methods are time-consuming and error-prone, and existing automated approaches often struggle with dense or diverse cells. FACellDet offers a practical deep learning solution adaptable to various cell types and imaging conditions, improving both detection accuracy and robustness through innovative feature alignment and enhanced supervisory signals. This system can streamline laboratory workflows and support high-throughput research and clinical diagnostics. While FACellDet demonstrates strong performance across challenging datasets, its current deployment requires adequate computational resources. Future development could focus on creating lightweight versions and integrating the framework with automated imaging systems, further broadening its accessibility and impact in routine biomedical practice.
{"title":"Feature-Aligned Cell Detection for Heterogeneous Microscopic Images With Focal Attenuated Distance Transform","authors":"Rui Liu;Cong Wu;Yifan Zhang;Haiying Song;Fei Yuan;Wei Dai;Wen Jung Li;Jun Liu","doi":"10.1109/TASE.2026.3661872","DOIUrl":"10.1109/TASE.2026.3661872","url":null,"abstract":"Accurate and robust cell detection in microscopic images is a fundamental yet challenging task due to diverse imaging conditions, dense cell distributions, and morphological variability. In this study, we propose FACellDet, a novel dual-branch hierarchical encoder-decoder framework that integrates adaptive feature alignment and a new supervisory signal to enhance cell detection. Specifically, a Coordinate-Attention-based Feature Alignment (CAFA) module is introduced to address spatial misalignment during multi-scale feature fusion, substantially improving cell detection precision. Furthermore, we design a Focal Attenuated Distance (FAD) map as an intermediate representation, providing highly discriminative and spatially informative cues, particularly in crowded regions. FACellDet features a dual-branch architecture, with the main branch predicting FAD maps for cell detection, while the auxiliary branch generates density maps to estimate cell counts for suppressing false detections. Extensive experiments on diverse cell types and imaging modalities from multiple public and in-house datasets demonstrate that our approach outperforms state-of-the-art methods in detection accuracy, while maintaining strong adaptability and robustness across challenging biomedical imaging scenarios. These results underscore the potential of FACellDet as an accurate and generalizable solution for automated cell detection in heterogeneous microscopic cell images, thereby facilitating reliable cell analysis to accelerate biomedical research and clinical workflows. Note to Practitioners—This work addresses the need for accurate and efficient cell detection and counting in biomedical images, where manual methods are time-consuming and error-prone, and existing automated approaches often struggle with dense or diverse cells. FACellDet offers a practical deep learning solution adaptable to various cell types and imaging conditions, improving both detection accuracy and robustness through innovative feature alignment and enhanced supervisory signals. This system can streamline laboratory workflows and support high-throughput research and clinical diagnostics. While FACellDet demonstrates strong performance across challenging datasets, its current deployment requires adequate computational resources. Future development could focus on creating lightweight versions and integrating the framework with automated imaging systems, further broadening its accessibility and impact in routine biomedical practice.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4375-4387"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/TASE.2026.3661751
Yajuan Liu;Haoran Ma;Ziqiu Song
It is difficult to obtain an accurate mechanism model of a floating wind turbine due to the large coupling disturbance of wind and wave at sea, and the control accuracy is difficult to guarantee, so maintaining stable power output is a challenge. Therefore, an event-triggered model-free adaptive control (ET-MFAC) collective pitch angle strategy is proposed for the NREL 5MW floating offshore wind turbine. The proposed method combines input and output Model-free adaptive control (IO-MFAC) with an improved Event-triggered mechanism (ETM). The improved ETM is based on the intensified ETM, which evaluates the weighted historical state error, and innovatively introduces an adaptive adjustment factor to realize real-time adjustment of the trigger frequency according to the system operating state, and reduces the computational burden of IO-MFAC. Meanwhile, the stability of IO-MFAC is proved based on the strict contraction mapping theory, which guarantees the stability of Bounded Input Bounded output (BIBO) and the monotonic convergence of tracking error. Experimental results on the OpenFAST/Simulink simulation platform show that the ET-MFAC strategy is superior to the traditional method in terms of rated power tracking, computational load reduction and robustness, especially in the extreme coupling conditions of strong turbulence and strong sea state. Note to Practitioners—The motivation for this study stems from the practical challenges faced in controlling offshore floating wind turbines, which operate in extremely complex and uncertain environments. Traditional pitch control methods often rely on accurate system models, which are difficult to obtain and computationally expensive, or cannot dynamically adapt to the changing ocean environment. The proposed ET-MFAC strategy provides a practical alternative that does not require accurate turbine modeling and can significantly reduce the computational burden of the controller. ET-MFAC combines an event-triggered mechanism that considers multiple historical trigger signals with data-driven control, and adaptively adjusts the trigger frequency according to real-time output errors, initiating pitch angle adjustment only when necessary. A high-fidelity NREL 5MW wind turbine model is used for simulation, and the results show that the ET-MFAC has more stable control performance than the traditional variable pitch controller under the condition of wind and wave coupling. This strategy provides a promising avenue to achieve more reliable and efficient operation of floating wind turbines and reduce maintenance and operation costs.
{"title":"Blade Pitch Control for Floating Wind Turbines via Event-Triggered Model-Free Adaptive Control Strategy","authors":"Yajuan Liu;Haoran Ma;Ziqiu Song","doi":"10.1109/TASE.2026.3661751","DOIUrl":"10.1109/TASE.2026.3661751","url":null,"abstract":"It is difficult to obtain an accurate mechanism model of a floating wind turbine due to the large coupling disturbance of wind and wave at sea, and the control accuracy is difficult to guarantee, so maintaining stable power output is a challenge. Therefore, an event-triggered model-free adaptive control (ET-MFAC) collective pitch angle strategy is proposed for the NREL 5MW floating offshore wind turbine. The proposed method combines input and output Model-free adaptive control (IO-MFAC) with an improved Event-triggered mechanism (ETM). The improved ETM is based on the intensified ETM, which evaluates the weighted historical state error, and innovatively introduces an adaptive adjustment factor to realize real-time adjustment of the trigger frequency according to the system operating state, and reduces the computational burden of IO-MFAC. Meanwhile, the stability of IO-MFAC is proved based on the strict contraction mapping theory, which guarantees the stability of Bounded Input Bounded output (BIBO) and the monotonic convergence of tracking error. Experimental results on the OpenFAST/Simulink simulation platform show that the ET-MFAC strategy is superior to the traditional method in terms of rated power tracking, computational load reduction and robustness, especially in the extreme coupling conditions of strong turbulence and strong sea state. Note to Practitioners—The motivation for this study stems from the practical challenges faced in controlling offshore floating wind turbines, which operate in extremely complex and uncertain environments. Traditional pitch control methods often rely on accurate system models, which are difficult to obtain and computationally expensive, or cannot dynamically adapt to the changing ocean environment. The proposed ET-MFAC strategy provides a practical alternative that does not require accurate turbine modeling and can significantly reduce the computational burden of the controller. ET-MFAC combines an event-triggered mechanism that considers multiple historical trigger signals with data-driven control, and adaptively adjusts the trigger frequency according to real-time output errors, initiating pitch angle adjustment only when necessary. A high-fidelity NREL 5MW wind turbine model is used for simulation, and the results show that the ET-MFAC has more stable control performance than the traditional variable pitch controller under the condition of wind and wave coupling. This strategy provides a promising avenue to achieve more reliable and efficient operation of floating wind turbines and reduce maintenance and operation costs.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4365-4374"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-02-06DOI: 10.1109/TASE.2026.3661086
Yazhou Tian;Yuangong Sun;Bing Liu
Significant achievements have been made in the analysis of linear coupled differential-difference systems (CDDSs). However, the study of nonlinear CDDSs, particularly those with time-varying characteristics and exogenous inputs, presents substantial challenges. This paper proposes, for the first time, a reachable set estimation method for time-varying homogeneous nonlinear coupled differential-difference systems (HNCDDSs) with exogenous inputs. By introducing a novel state transformation and a method developed in positive systems, we establish an explicit sufficient condition that ensures all system states converge exponentially to a specified ball when the homogeneity degree of the system is less than or equal to one. Building upon this analytical framework, for the homogeneity degree of the system greater than one, we further derive a criterion guaranteeing the states converge polynomially within a bounded region. These theoretical findings not only extend but also improve existing results in the literature, which are effectively supported by two specific numerical examples. Note to Practitioners—Coupled differential-difference systems play a key role to characterize the behaviors of the dynamic systems in control field, such as electrical engineering, fluid dynamics, and multi-agent systems. It is significant to explore the reachable set estimation of nonlinear CDDSs with exogenous inputs. Moreover, since most physical systems are inherently time-varying, the reachable set estimation of time-varying HNCDDSs has become a critical issue that urgently needs to be addressed. Traditional approaches, such as the Lyapunov-Krasovskii functional method, often prove ineffective for time-varying systems, as they typically lead to either unsolvable Riccati differential equations or indefinite linear matrix inequalities (LMIs). To overcome these challenges, this study proposes a novel state transformation combined with a method developed in positive systems to estimate the reachable set of time-varying HNCDDSs with exogenous inputs, and derives more general results compared with existing conclusions.
{"title":"Reachable Set Estimation for Time-Varying Homogeneous Coupled Differential-Difference Systems With Exogenous Inputs","authors":"Yazhou Tian;Yuangong Sun;Bing Liu","doi":"10.1109/TASE.2026.3661086","DOIUrl":"10.1109/TASE.2026.3661086","url":null,"abstract":"Significant achievements have been made in the analysis of linear coupled differential-difference systems (CDDSs). However, the study of nonlinear CDDSs, particularly those with time-varying characteristics and exogenous inputs, presents substantial challenges. This paper proposes, for the first time, a reachable set estimation method for time-varying homogeneous nonlinear coupled differential-difference systems (HNCDDSs) with exogenous inputs. By introducing a novel state transformation and a method developed in positive systems, we establish an explicit sufficient condition that ensures all system states converge exponentially to a specified ball when the homogeneity degree of the system is less than or equal to one. Building upon this analytical framework, for the homogeneity degree of the system greater than one, we further derive a criterion guaranteeing the states converge polynomially within a bounded region. These theoretical findings not only extend but also improve existing results in the literature, which are effectively supported by two specific numerical examples. Note to Practitioners—Coupled differential-difference systems play a key role to characterize the behaviors of the dynamic systems in control field, such as electrical engineering, fluid dynamics, and multi-agent systems. It is significant to explore the reachable set estimation of nonlinear CDDSs with exogenous inputs. Moreover, since most physical systems are inherently time-varying, the reachable set estimation of time-varying HNCDDSs has become a critical issue that urgently needs to be addressed. Traditional approaches, such as the Lyapunov-Krasovskii functional method, often prove ineffective for time-varying systems, as they typically lead to either unsolvable Riccati differential equations or indefinite linear matrix inequalities (LMIs). To overcome these challenges, this study proposes a novel state transformation combined with a method developed in positive systems to estimate the reachable set of time-varying HNCDDSs with exogenous inputs, and derives more general results compared with existing conclusions.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4341-4349"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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