Pub Date : 2025-01-31DOI: 10.1109/TCYB.2025.3530761
Wenhai Qi;Xiaochun Teng;Ju H. Park;Jinde Cao;Huaicheng Yan;Jun Cheng
A dynamic event-triggered load frequency control (LFC) is studied for interconnected multiarea power systems (IMAPSs) with stochastic semi-Markov parameters from the perspective of finite-time interval. To facilitate the sudden changes, the underlying semi-Markov process (SMP) is adopted to characterize the random behavior of IMAPSs. A dynamic event-triggered protocol (DETP) is developed to modulate the transmission frequency while maintaining predefined system performance. Owing to complicated grid environment, the hidden semi-Markov model (HSMM) is proposed to solve the asynchronization between the system mode and the controller mode, which forms a new asynchronous mechanism to better understand the behavior pattern of the system. The novelty of this article is to construct a suitable asynchronous control strategy to solve the mismatch between the system mode and the controller mode under the framework of IMAPSs. Different from static event-triggered protocol (ETP), the DETP is proposed, in which the threshold parameters can be dynamically adjusted to reduce the waste of communication resources and achieve dynamic performance in limited time. According to the stochastic system theory and the finite-time theory, by constructing a modular dependent random Lyapunov function, sufficient conditions are obtained to ensure the finite-time boundedness of the corresponding system with $H_{infty }$ performance. Finally, the efficiency is demonstrated through three-area power systems.
{"title":"Dynamic Protocol-Based Control for Hidden Stochastic Jump Multiarea Power Systems in Finite-Time Interval","authors":"Wenhai Qi;Xiaochun Teng;Ju H. Park;Jinde Cao;Huaicheng Yan;Jun Cheng","doi":"10.1109/TCYB.2025.3530761","DOIUrl":"10.1109/TCYB.2025.3530761","url":null,"abstract":"A dynamic event-triggered load frequency control (LFC) is studied for interconnected multiarea power systems (IMAPSs) with stochastic semi-Markov parameters from the perspective of finite-time interval. To facilitate the sudden changes, the underlying semi-Markov process (SMP) is adopted to characterize the random behavior of IMAPSs. A dynamic event-triggered protocol (DETP) is developed to modulate the transmission frequency while maintaining predefined system performance. Owing to complicated grid environment, the hidden semi-Markov model (HSMM) is proposed to solve the asynchronization between the system mode and the controller mode, which forms a new asynchronous mechanism to better understand the behavior pattern of the system. The novelty of this article is to construct a suitable asynchronous control strategy to solve the mismatch between the system mode and the controller mode under the framework of IMAPSs. Different from static event-triggered protocol (ETP), the DETP is proposed, in which the threshold parameters can be dynamically adjusted to reduce the waste of communication resources and achieve dynamic performance in limited time. According to the stochastic system theory and the finite-time theory, by constructing a modular dependent random Lyapunov function, sufficient conditions are obtained to ensure the finite-time boundedness of the corresponding system with <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> performance. Finally, the efficiency is demonstrated through three-area power systems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1486-1496"},"PeriodicalIF":9.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071344","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 : 2025-01-29DOI: 10.1109/TCYB.2025.3529867
Jihang Sui;Ben Niu;Yongsheng Ou;Xudong Zhao;Ding Wang
This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.
{"title":"Event-Triggered Adaptive Finite-Time Control for a Robotic Manipulator System With Global Prescribed Performance and Asymptotic Tracking","authors":"Jihang Sui;Ben Niu;Yongsheng Ou;Xudong Zhao;Ding Wang","doi":"10.1109/TCYB.2025.3529867","DOIUrl":"10.1109/TCYB.2025.3529867","url":null,"abstract":"This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1045-1055"},"PeriodicalIF":9.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056530","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 : 2025-01-28DOI: 10.1109/TCYB.2025.3528200
Han-Yu Wu;Qingshan Liu;Ju H. Park
This article studies the secure leader-follower consensus of nonlinear multiagent systems under multimode Denial-of-Service attacks. According to the topology characteristics after attacks, three kinds of different attack modes are introduced. Moreover, the common zero-topology attack is encompassed within the multimode attacks considered in this article. Due to the difficulty of directly obtaining the state of the system in some circumstances, a state observer is designed utilizing localized output information of the MASs to estimate the state. By applying suitable observer-based controller and Lyapunov method, some sufficient conditions are presented to ensure the observer-based secure consensus of the MASs under multimode DoS attacks. Furthermore, the obtained results can be extended to linear MASs under DoS attacks. Finally, two practical examples on power system are provided to demonstrate the validity of the derived theoretical results.
{"title":"Observer-Based Secure Consensus for Multiagent Systems Under Multimode DoS Attacks With Application to Power System","authors":"Han-Yu Wu;Qingshan Liu;Ju H. Park","doi":"10.1109/TCYB.2025.3528200","DOIUrl":"10.1109/TCYB.2025.3528200","url":null,"abstract":"This article studies the secure leader-follower consensus of nonlinear multiagent systems under multimode Denial-of-Service attacks. According to the topology characteristics after attacks, three kinds of different attack modes are introduced. Moreover, the common zero-topology attack is encompassed within the multimode attacks considered in this article. Due to the difficulty of directly obtaining the state of the system in some circumstances, a state observer is designed utilizing localized output information of the MASs to estimate the state. By applying suitable observer-based controller and Lyapunov method, some sufficient conditions are presented to ensure the observer-based secure consensus of the MASs under multimode DoS attacks. Furthermore, the obtained results can be extended to linear MASs under DoS attacks. Finally, two practical examples on power system are provided to demonstrate the validity of the derived theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1032-1044"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055000","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 : 2025-01-28DOI: 10.1109/TCYB.2025.3525652
Yue Zhang;Sheng Wei;Zheng Wang;Honghai Liu
Muscle force and morphology information offer complementary perspectives for gesture recognition and its applications. Surface Electromyography (sEMG) provides force and electrophysiological information associated with muscles, while A-mode ultrasound (AUS) reveals muscle morphological information. By leveraging these two modalities, more comprehensive muscle motor unit information relevant to gesture recognition can be obtained. In this article, we introduce the adaptive weight classification (AWC) module and its enhanced version with hierarchical classifiers, adaptive weight hierarchical soft voting (AWHSV), to integrate AUS and sEMG into a fused modality. This approach dynamically adjusts the weights of individual and fused features, compensating for lost details during fusion, leading to a richer information representation and significantly improving algorithm robustness in gesture recognition. The experimental results demonstrate that the proposed method achieves recognition rates that are 0.66%, 2.36%, and 1.30% higher than those of its counterparts using sEMG, AUS, and sEMG-AUS, respectively. Moreover, the method outperforms state-of-the-art approaches, confirming its effectiveness in gesture recognition across both single and multiple modalities. This work demonstrates the advantages of the proposed AWHSV method, providing broader application scenarios for gesture recognition.
{"title":"Dual-Modal Gesture Recognition Using Adaptive Weight Hierarchical Soft Voting Mechanism","authors":"Yue Zhang;Sheng Wei;Zheng Wang;Honghai Liu","doi":"10.1109/TCYB.2025.3525652","DOIUrl":"10.1109/TCYB.2025.3525652","url":null,"abstract":"Muscle force and morphology information offer complementary perspectives for gesture recognition and its applications. Surface Electromyography (sEMG) provides force and electrophysiological information associated with muscles, while A-mode ultrasound (AUS) reveals muscle morphological information. By leveraging these two modalities, more comprehensive muscle motor unit information relevant to gesture recognition can be obtained. In this article, we introduce the adaptive weight classification (AWC) module and its enhanced version with hierarchical classifiers, adaptive weight hierarchical soft voting (AWHSV), to integrate AUS and sEMG into a fused modality. This approach dynamically adjusts the weights of individual and fused features, compensating for lost details during fusion, leading to a richer information representation and significantly improving algorithm robustness in gesture recognition. The experimental results demonstrate that the proposed method achieves recognition rates that are 0.66%, 2.36%, and 1.30% higher than those of its counterparts using sEMG, AUS, and sEMG-AUS, respectively. Moreover, the method outperforms state-of-the-art approaches, confirming its effectiveness in gesture recognition across both single and multiple modalities. This work demonstrates the advantages of the proposed AWHSV method, providing broader application scenarios for gesture recognition.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1497-1508"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055062","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 : 2025-01-24DOI: 10.1109/TCYB.2024.3524827
Junhao Chen;Chunhui Zhao;Pengyu Song;Min Xie
Industrial data often consist of continuous variables (CVs) and binary variables (BVs), both of which provide crucial information about process operating conditions. Due to the coupling between industrial systems or equipment, these hybrid variables are usually high-dimensional and highly correlated. However, existing methods generally model hybrid variables directly in the observation space and assume independence between the variables to overcome the curse of dimensionality. Thus, they are ineffective at capturing dependencies among hybrid variables, and the effectiveness of process monitoring will be compromised. To overcome the limitations, this study proposes to seek a unified subspace for hybrid variables using the probabilistic latent variable (LV) model. By introducing a low-dimensional continuous LV, the proposed method can avoid the curse of dimensionality while capturing the dependencies between hybrid variables. Nevertheless, the inference of LV is analytically intractable and thus time-consuming due to the heterogeneity of CVs and BVs. To accelerate offline learning and online inference procedures, this study originally derives an analytical Gaussian distribution to approximate the true posterior distribution of the LV, based on which an efficient expectation-maximization algorithm is developed for parameter estimation. The Gaussian approximation is simultaneously optimized with the latest parameters to achieve a high approximation accuracy. The LV is then estimated by the posterior mean of the Gaussian approximation. By mapping the heterogeneous variables into a unified subspace, the proposed method defines three monitoring statistics, which are physically interpretable and thoroughly evaluate the probability of hybrid variables being normal. The effectiveness of the proposed method in detecting anomalies in CVs and BVs is shown through a numerically simulated case and a real industrial case.
{"title":"Unified Low-Dimensional Subspace Analysis of Continuous and Binary Variables for Industrial Process Monitoring","authors":"Junhao Chen;Chunhui Zhao;Pengyu Song;Min Xie","doi":"10.1109/TCYB.2024.3524827","DOIUrl":"10.1109/TCYB.2024.3524827","url":null,"abstract":"Industrial data often consist of continuous variables (CVs) and binary variables (BVs), both of which provide crucial information about process operating conditions. Due to the coupling between industrial systems or equipment, these hybrid variables are usually high-dimensional and highly correlated. However, existing methods generally model hybrid variables directly in the observation space and assume independence between the variables to overcome the curse of dimensionality. Thus, they are ineffective at capturing dependencies among hybrid variables, and the effectiveness of process monitoring will be compromised. To overcome the limitations, this study proposes to seek a unified subspace for hybrid variables using the probabilistic latent variable (LV) model. By introducing a low-dimensional continuous LV, the proposed method can avoid the curse of dimensionality while capturing the dependencies between hybrid variables. Nevertheless, the inference of LV is analytically intractable and thus time-consuming due to the heterogeneity of CVs and BVs. To accelerate offline learning and online inference procedures, this study originally derives an analytical Gaussian distribution to approximate the true posterior distribution of the LV, based on which an efficient expectation-maximization algorithm is developed for parameter estimation. The Gaussian approximation is simultaneously optimized with the latest parameters to achieve a high approximation accuracy. The LV is then estimated by the posterior mean of the Gaussian approximation. By mapping the heterogeneous variables into a unified subspace, the proposed method defines three monitoring statistics, which are physically interpretable and thoroughly evaluate the probability of hybrid variables being normal. The effectiveness of the proposed method in detecting anomalies in CVs and BVs is shown through a numerically simulated case and a real industrial case.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1135-1146"},"PeriodicalIF":9.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030788","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}
Real-time accurate visual object tracking (VOT) for quadrupedal robots is a great challenge when the scale or aspect ratio of moving objects vary. To overcome this challenge, existing methods apply anchor-based schemes that search a handcrafted space to locate moving objects. However, their performances are limited given complicated environments, especially when the speed of quadrupedal robots is relatively high. In this work, a newly designed VOT algorithm for a quadrupedal robot based on a Siamese network is introduced. First, a one-stage detector for locating moving objects is designed and applied. Then, position information of moving objects is fed into a newly designed Siamese adaptive network to estimate their scale and aspect ratio. For regressing bounding boxes of a target object, a box adaptive head with an asymmetric convolution (ACM) layer is newly proposed. The proposed approach is successfully used on a quadrupedal robot, which can accurately track a specific moving object in real-world complicated scenes.
{"title":"Siamese Adaptive Network-Based Accurate and Robust Visual Object Tracking Algorithm for Quadrupedal Robots","authors":"Zhengcai Cao;Junnian Li;Shibo Shao;Dong Zhang;MengChu Zhou","doi":"10.1109/TCYB.2025.3527342","DOIUrl":"10.1109/TCYB.2025.3527342","url":null,"abstract":"Real-time accurate visual object tracking (VOT) for quadrupedal robots is a great challenge when the scale or aspect ratio of moving objects vary. To overcome this challenge, existing methods apply anchor-based schemes that search a handcrafted space to locate moving objects. However, their performances are limited given complicated environments, especially when the speed of quadrupedal robots is relatively high. In this work, a newly designed VOT algorithm for a quadrupedal robot based on a Siamese network is introduced. First, a one-stage detector for locating moving objects is designed and applied. Then, position information of moving objects is fed into a newly designed Siamese adaptive network to estimate their scale and aspect ratio. For regressing bounding boxes of a target object, a box adaptive head with an asymmetric convolution (ACM) layer is newly proposed. The proposed approach is successfully used on a quadrupedal robot, which can accurately track a specific moving object in real-world complicated scenes.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1264-1276"},"PeriodicalIF":9.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030790","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 : 2025-01-24DOI: 10.1109/TCYB.2024.3525413
Fangyu Zhang;Jun Wang
Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.
{"title":"Index Tracking via Sparse Bayesian Regression and Collaborative Neurodynamic Optimization","authors":"Fangyu Zhang;Jun Wang","doi":"10.1109/TCYB.2024.3525413","DOIUrl":"10.1109/TCYB.2024.3525413","url":null,"abstract":"Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1238-1249"},"PeriodicalIF":9.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030815","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 : 2025-01-24DOI: 10.1109/TCYB.2024.3523965
Haichuan Yang;Ziquan Yu;Minrui Fu;Youmin Zhang
In this article, a resilient consensus control method is proposed for nonlinear multiple unmanned aerial vehicles (UAVs) with input saturation and Denial of Service (DoS) attacks. First, an input saturation constraint based on the UAV dynamic model is investigated in this article, and an adaptive compensating term is developed to handle the input saturation. The DoS attacks considered in this article can interrupt all the communication transmissions of the attacked UAV from neighbors so that the victim is not able to receive any information from neighboring UAVs during DoS attacks. To deal with such a difficult problem, a fixed-time security constraint estimator (FTSCE) is proposed to ensure the stability and security of UAVs during the DoS attacks. Moreover, the unknown state is estimated to reduce the amount of the transferred information. Based on the proposed FTSCE, the relative position and velocity of UAV states are used to design the resilient consensus controller against the DoS attacks. By using the proposed controller, the system stability can be guaranteed according to the Lyapunov stability analysis. Finally, the numerical simulation is conducted to verify the effectiveness of the proposed resilient consensus control method.
{"title":"Resilient Consensus Control for Multiple UAVs With Input Saturation Under DoS Attacks","authors":"Haichuan Yang;Ziquan Yu;Minrui Fu;Youmin Zhang","doi":"10.1109/TCYB.2024.3523965","DOIUrl":"10.1109/TCYB.2024.3523965","url":null,"abstract":"In this article, a resilient consensus control method is proposed for nonlinear multiple unmanned aerial vehicles (UAVs) with input saturation and Denial of Service (DoS) attacks. First, an input saturation constraint based on the UAV dynamic model is investigated in this article, and an adaptive compensating term is developed to handle the input saturation. The DoS attacks considered in this article can interrupt all the communication transmissions of the attacked UAV from neighbors so that the victim is not able to receive any information from neighboring UAVs during DoS attacks. To deal with such a difficult problem, a fixed-time security constraint estimator (FTSCE) is proposed to ensure the stability and security of UAVs during the DoS attacks. Moreover, the unknown state is estimated to reduce the amount of the transferred information. Based on the proposed FTSCE, the relative position and velocity of UAV states are used to design the resilient consensus controller against the DoS attacks. By using the proposed controller, the system stability can be guaranteed according to the Lyapunov stability analysis. Finally, the numerical simulation is conducted to verify the effectiveness of the proposed resilient consensus control method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1159-1171"},"PeriodicalIF":9.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030816","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 : 2025-01-23DOI: 10.1109/TCYB.2025.3526686
Kang-Di Lu;Le Zhou;Zheng-Guang Wu
State estimation of cyber-physical power systems (CPPSs) is of great significance for power system optimization, control, and security analysis. Additionally, fractional differential calculus is based on differentiation and integration of arbitrary fractional order, which can more accurately describe the physical phenomenon model than the traditional integer calculus. Thus, this article proposes a novel fractional-order extended Kalman filter (FOEKF) based on the evolutionary algorithm and deep ensemble learning techniques for the state estimation problem of CPPSs from the fractional-order theory perspective. First, the power system is modeled as a fractional version to describe the physical phenomenon better according to the fractional differential calculus theory. Then, considering the difficulties in determining fractional orders in the fractional-order power system, a deep ensemble learning-based approach is used to design the fitness function and a genetic algorithm is developed to determine these parameters by optimizing the designed objective function. Furthermore, to solve the difficulties in estimating for fractional-order power system by integral extended Kalman filter (EKF), the evolutionary FOEKF (EFOEKF) is presented as the estimator for the designed fractional-order power system. Finally, to improve the performance of EFOEKF under bad datum scenarios caused by cyber-attacks or sudden loads, an enhanced EFOEKF method is developed by using an adapted exponential weighting function. The numerical simulation results show that the proposed EFOEKF is better than EKF and FOEKF on four different IEEE bus systems in terms of the mean absolute error.
{"title":"Evolutionary Fractional-Order Extended Kalman Filter of Cyber-Physical Power Systems","authors":"Kang-Di Lu;Le Zhou;Zheng-Guang Wu","doi":"10.1109/TCYB.2025.3526686","DOIUrl":"10.1109/TCYB.2025.3526686","url":null,"abstract":"State estimation of cyber-physical power systems (CPPSs) is of great significance for power system optimization, control, and security analysis. Additionally, fractional differential calculus is based on differentiation and integration of arbitrary fractional order, which can more accurately describe the physical phenomenon model than the traditional integer calculus. Thus, this article proposes a novel fractional-order extended Kalman filter (FOEKF) based on the evolutionary algorithm and deep ensemble learning techniques for the state estimation problem of CPPSs from the fractional-order theory perspective. First, the power system is modeled as a fractional version to describe the physical phenomenon better according to the fractional differential calculus theory. Then, considering the difficulties in determining fractional orders in the fractional-order power system, a deep ensemble learning-based approach is used to design the fitness function and a genetic algorithm is developed to determine these parameters by optimizing the designed objective function. Furthermore, to solve the difficulties in estimating for fractional-order power system by integral extended Kalman filter (EKF), the evolutionary FOEKF (EFOEKF) is presented as the estimator for the designed fractional-order power system. Finally, to improve the performance of EFOEKF under bad datum scenarios caused by cyber-attacks or sudden loads, an enhanced EFOEKF method is developed by using an adapted exponential weighting function. The numerical simulation results show that the proposed EFOEKF is better than EKF and FOEKF on four different IEEE bus systems in terms of the mean absolute error.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1395-1408"},"PeriodicalIF":9.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026361","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 article first presents a dual flexible prescribed performance control (DFPPC) approach of input saturated high-order nonlinear systems (IS-HONSs). Compared to the existing PPC approaches of IS-HONSs, under which the performance constraint boundaries (PCBs) are usually fixed and bounded, resulting in a restriction of the initial error in the algorithm implementation; in addition, the coupling relationship between performance constraints and input saturation is usually ignored, resulting in the methods are very fragile when input saturation occurs. By designing the novel tensile model-based PCBs that depend on output and input constraints, the proposed DFPPC method provides sufficient resilience for both the initial conditions and the input saturation, so that the proposed DFPPC method can not only be suitable for multiple types of initial errors by adjusting the parameters, including $e_{1}(0)in (underline {mathcal {B}}(0),bar {mathcal {B}}(0))$ , $e_{1}(0)in (-infty,bar {mathcal {B}}(0))$ , $e_{1}(0)in (underline {mathcal {B}}(0),+infty)$ and $e_{1}(0)in (-infty,infty)$ , where $underline {mathcal {B}}(0)$ , and $bar {mathcal {B}}(0)$ denote the initial PCBs; but also can achieve a good balance between input saturation and performance constraints, i.e., when the control input reaches or exceeds the saturation threshold, the PCBs can adaptively extend to avoid the singularity, and when the control input returns to the saturation threshold range, the PCBs are then adaptively restored to the original PCBs. The results show that the proposed DFPPC algorithm guarantees semi-global boundedness for all closed-loop signals, while ensuring that the system output accurately tracks the desired signal, and it consistently maintains the tracking error within the PCBs. The developed algorithm is illustrated by means of simulation instances.
{"title":"Dual Flexible Prescribed Performance Control of Input Saturated High-Order Nonlinear Systems","authors":"Yangang Yao;Xu Liang;Yu Kang;Yunbo Zhao;Jieqing Tan;Lichuan Gu","doi":"10.1109/TCYB.2024.3524242","DOIUrl":"10.1109/TCYB.2024.3524242","url":null,"abstract":"This article first presents a dual flexible prescribed performance control (DFPPC) approach of input saturated high-order nonlinear systems (IS-HONSs). Compared to the existing PPC approaches of IS-HONSs, under which the performance constraint boundaries (PCBs) are usually fixed and bounded, resulting in a restriction of the initial error in the algorithm implementation; in addition, the coupling relationship between performance constraints and input saturation is usually ignored, resulting in the methods are very fragile when input saturation occurs. By designing the novel tensile model-based PCBs that depend on output and input constraints, the proposed DFPPC method provides sufficient resilience for both the initial conditions and the input saturation, so that the proposed DFPPC method can not only be suitable for multiple types of initial errors by adjusting the parameters, including <inline-formula> <tex-math>$e_{1}(0)in (underline {mathcal {B}}(0),bar {mathcal {B}}(0))$ </tex-math></inline-formula>, <inline-formula> <tex-math>$e_{1}(0)in (-infty,bar {mathcal {B}}(0))$ </tex-math></inline-formula>, <inline-formula> <tex-math>$e_{1}(0)in (underline {mathcal {B}}(0),+infty)$ </tex-math></inline-formula> and <inline-formula> <tex-math>$e_{1}(0)in (-infty,infty)$ </tex-math></inline-formula>, where <inline-formula> <tex-math>$underline {mathcal {B}}(0)$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$bar {mathcal {B}}(0)$ </tex-math></inline-formula> denote the initial PCBs; but also can achieve a good balance between input saturation and performance constraints, i.e., when the control input reaches or exceeds the saturation threshold, the PCBs can adaptively extend to avoid the singularity, and when the control input returns to the saturation threshold range, the PCBs are then adaptively restored to the original PCBs. The results show that the proposed DFPPC algorithm guarantees semi-global boundedness for all closed-loop signals, while ensuring that the system output accurately tracks the desired signal, and it consistently maintains the tracking error within the PCBs. The developed algorithm is illustrated by means of simulation instances.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1147-1158"},"PeriodicalIF":9.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026359","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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