Jianhui Wang;Jiarui Liu;C. L. Philip Chen;Zhi Liu;Kairui Chen
This paper proposes an observer-based prescribed-time consensus tracking control method for nonlinear multiagent systems with unknown virtual control coefficients. Existing prescribed-time distributed observers require information about leader input dynamics, which is unavailable in many practical applications. To address the above issue, this paper proposes an improved prescribed-time strategy as a foundation. Then, an auxiliary system is constructed, which removes restrictions on leader input information. With the assistance of such a system, a distributed observer is synthesized, which enables a prescribed-time observation of leader state signals. Meanwhile, by decomposing the virtual control coefficient, a prescribed-time compensation law is investigated to handle nonlinear dynamics and unknown virtual control coefficients. In addition, a prescribed-time control protocol is formulated, which drives the stabilization of the multi-agent systems and the boundedness of all signals for any initial condition. Finally, the efficacy of the proposed control method is evaluated through simulation under three distinct conditions.
{"title":"Observer-Based Practical Prescribed-Time Consensus Tracking Control for Multiagent Systems with Unknown Virtual Control Coefficients","authors":"Jianhui Wang;Jiarui Liu;C. L. Philip Chen;Zhi Liu;Kairui Chen","doi":"10.1109/JAS.2025.125480","DOIUrl":"https://doi.org/10.1109/JAS.2025.125480","url":null,"abstract":"This paper proposes an observer-based prescribed-time consensus tracking control method for nonlinear multiagent systems with unknown virtual control coefficients. Existing prescribed-time distributed observers require information about leader input dynamics, which is unavailable in many practical applications. To address the above issue, this paper proposes an improved prescribed-time strategy as a foundation. Then, an auxiliary system is constructed, which removes restrictions on leader input information. With the assistance of such a system, a distributed observer is synthesized, which enables a prescribed-time observation of leader state signals. Meanwhile, by decomposing the virtual control coefficient, a prescribed-time compensation law is investigated to handle nonlinear dynamics and unknown virtual control coefficients. In addition, a prescribed-time control protocol is formulated, which drives the stabilization of the multi-agent systems and the boundedness of all signals for any initial condition. Finally, the efficacy of the proposed control method is evaluated through simulation under three distinct conditions.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2541-2552"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861236","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 addresses a confidence fusion problem of the camera and the millimeter-wave (MMW) radar for target tracking in intelligent driving systems. The local camera and radar estimators are performed by analyzing the measurement characteristics of each sensor. The radar estimates are aligned to the camera sampling time and the Kuhn-Munkers method is used to obtain the matching relationship of local camera and radar estimates for fusion. Next, to utilize the advantage of the camera with low false detection and the radar with low miss detection performance, the mass functions are introduced to model the detection performance of the two sensors. Based on the mass functions and a D-S (Dempster-Shafer) evidence theory, the confidence fusion is performed sequentially to determine whether each target exists. Then a weighted maximum likelihood fusion estimator is designed for matched targets based on priori positing accuracy of the local camera and radar estimates. Finally, the experimental results on road vehicle tracking show that the detection range is expanded and false targets are significantly reduced by the proposed confidence fusion method.
{"title":"Target Tracking by Cameras and Millimeter-Wave Radars: A Confidence Information Fusion Method","authors":"Xiaohui Hao;Yuanqing Xia;Hongjiu Yang;Zhiqiang Zuo","doi":"10.1109/JAS.2025.125405","DOIUrl":"https://doi.org/10.1109/JAS.2025.125405","url":null,"abstract":"This paper addresses a confidence fusion problem of the camera and the millimeter-wave (MMW) radar for target tracking in intelligent driving systems. The local camera and radar estimators are performed by analyzing the measurement characteristics of each sensor. The radar estimates are aligned to the camera sampling time and the Kuhn-Munkers method is used to obtain the matching relationship of local camera and radar estimates for fusion. Next, to utilize the advantage of the camera with low false detection and the radar with low miss detection performance, the mass functions are introduced to model the detection performance of the two sensors. Based on the mass functions and a D-S (Dempster-Shafer) evidence theory, the confidence fusion is performed sequentially to determine whether each target exists. Then a weighted maximum likelihood fusion estimator is designed for matched targets based on priori positing accuracy of the local camera and radar estimates. Finally, the experimental results on road vehicle tracking show that the detection range is expanded and false targets are significantly reduced by the proposed confidence fusion method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2486-2498"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861241","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}
Yongliang Yang;Hamidreza Modares;Kyriakos G. Vamvoudakis;Frank L. Lewis
In this paper, we present a novel adaptive critic design with a finite excitation (FE) condition for adaptive optimal control of continuous-time nonlinear systems. The online recorded data is combined with instantaneous data via a hierarchical design scheme to replace the persistence of excitation condition with a FE condition. The online data is recorded in the preprocessing step and verified online, whereas the novel critic is implemented in the assembling step through appropriate algebraic calculations. On this basis, the composite adaptive critic design is developed with satisfactory convergence. The adaptive critic design can be implemented in an online fashion with the hierarchical design scheme, and the FE condition can also be online verifiable. It is shown that the composite adaptive critic design guarantees the closed-loop stability of the equilibrium point and convergence to the optimal solution. Simulations are conducted to show the efficacy of the composite adaptive critic design.
{"title":"Composite Adaptive Critic Design","authors":"Yongliang Yang;Hamidreza Modares;Kyriakos G. Vamvoudakis;Frank L. Lewis","doi":"10.1109/JAS.2025.125435","DOIUrl":"https://doi.org/10.1109/JAS.2025.125435","url":null,"abstract":"In this paper, we present a novel adaptive critic design with a finite excitation (FE) condition for adaptive optimal control of continuous-time nonlinear systems. The online recorded data is combined with instantaneous data via a hierarchical design scheme to replace the persistence of excitation condition with a FE condition. The online data is recorded in the preprocessing step and verified online, whereas the novel critic is implemented in the assembling step through appropriate algebraic calculations. On this basis, the composite adaptive critic design is developed with satisfactory convergence. The adaptive critic design can be implemented in an online fashion with the hierarchical design scheme, and the FE condition can also be online verifiable. It is shown that the composite adaptive critic design guarantees the closed-loop stability of the equilibrium point and convergence to the optimal solution. Simulations are conducted to show the efficacy of the composite adaptive critic design.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2525-2540"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861216","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}
Distributed cooperative localization is essential to operate successfully for multirobot systems, especially in scenarios where absolute information cannot be continuously obtained. With the properties of distributed processing and message passing, Gaussian belief propagation has proven to be effective for achieving accurate pose estimation, making it a promising method for future distributed cooperative localization. Unfortunately, existing Gaussian belief propagation-based distributed cooperative localization methods are sensitive to measurement outliers and measurement nonlinearity, leading to poor performance in such environments. To address these issues, a novel distributed robust belief propagation method with odometry preintegration (RBP-OP) is proposed to mitigate the effects of measurement outliers and measurement nonlinearity. Firstly, the belief of variable node is modeled as the Gaussian distribution and the probability density function of external measurement factor node is modeled as the Student's t-distribution. The message between external measurement factor node and variable node is computed by modifying the measurement noise covariance matrix adaptively, which significantly reduces the impact of outliers. Secondly, a novel wheel-speed odometry factor is derived based on the preintegration method, which enables forward-backward iteration, and then mitigates the effects of measurement nonlinearity. Finally, extensive simulations and experiments show that the proposed RBP-OP method offers superior filtering robustness and estimation accuracy compared to the existing state-of-the-art methods.
{"title":"RBP-OP: Distributed Robust Belief Propagation Method with Odometry Preintegration for Multirobot Collaborative Localization","authors":"Jianqiang Zhang;Jiajun Cheng;Hanxuan Zhang;Yulong Huang","doi":"10.1109/JAS.2025.125711","DOIUrl":"https://doi.org/10.1109/JAS.2025.125711","url":null,"abstract":"Distributed cooperative localization is essential to operate successfully for multirobot systems, especially in scenarios where absolute information cannot be continuously obtained. With the properties of distributed processing and message passing, Gaussian belief propagation has proven to be effective for achieving accurate pose estimation, making it a promising method for future distributed cooperative localization. Unfortunately, existing Gaussian belief propagation-based distributed cooperative localization methods are sensitive to measurement outliers and measurement nonlinearity, leading to poor performance in such environments. To address these issues, a novel distributed robust belief propagation method with odometry preintegration (RBP-OP) is proposed to mitigate the effects of measurement outliers and measurement nonlinearity. Firstly, the belief of variable node is modeled as the Gaussian distribution and the probability density function of external measurement factor node is modeled as the Student's t-distribution. The message between external measurement factor node and variable node is computed by modifying the measurement noise covariance matrix adaptively, which significantly reduces the impact of outliers. Secondly, a novel wheel-speed odometry factor is derived based on the preintegration method, which enables forward-backward iteration, and then mitigates the effects of measurement nonlinearity. Finally, extensive simulations and experiments show that the proposed RBP-OP method offers superior filtering robustness and estimation accuracy compared to the existing state-of-the-art methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2427-2454"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145860195","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, Sodium-ion batteries (SIBs) are characterized by low cost, excellent performance at low temperatures and good thermal stability, which are expected to replace lithium-ion batteries (LIBs) and be widely used in microgrids, energy storage power plants and other power system areas [1]. However, research on fault diagnosis for SIBs-particularly internal short circuit (ISC) faults-remains limited and primarily focuses on simple voltage and current signals, which restricts practical applications and poses potential safety risks. For this reason, this study integrates the strengths of artificial intelligence (AI) and electrochemical impedance spectroscopy (EIS) and proposes a novel data-driven learning strategy for ISC fault detection in SIBs. Specifically, an electrochemical workstation is first used to collect EIS data from SIBs with various degrees of ISC faults at different states of charge (SOC). To shorten the time for EIS extraction, the importance of each frequency feature is quantified and the impact of different frequency bands on ISC fault detection is analyzed to identify the most important frequency band as the primary fault features. Finally, to address the challenge of limited EIS data leading to poor performance in traditional machine learning methods, this study develops a data-driven learning strategy based on the Tabular prior-data fitted network (TabPFN), which achieves strong performance on small datasets due to its pretraining capabilities. The experimental results show that the proposed method achieves satisfactory results in detecting ISC fault of SIBs when only partial frequency band of EIS is used, with an accuracy of 95.4% and an area under curve (AUC) value of 0.967.
{"title":"Prior-Data Fitted Network with Impedance Spectroscopy for Smart Short Circuit Diagnosis in Sodium-Ion Batteries of Power Systems","authors":"Kailong Liu;Shiwen Zhao;Qiao Peng;Jiayue Wang;Bin Duan;Xiangjun Li;Chenghui Zhang","doi":"10.1109/JAS.2025.125606","DOIUrl":"https://doi.org/10.1109/JAS.2025.125606","url":null,"abstract":"Dear Editor, Sodium-ion batteries (SIBs) are characterized by low cost, excellent performance at low temperatures and good thermal stability, which are expected to replace lithium-ion batteries (LIBs) and be widely used in microgrids, energy storage power plants and other power system areas [1]. However, research on fault diagnosis for SIBs-particularly internal short circuit (ISC) faults-remains limited and primarily focuses on simple voltage and current signals, which restricts practical applications and poses potential safety risks. For this reason, this study integrates the strengths of artificial intelligence (AI) and electrochemical impedance spectroscopy (EIS) and proposes a novel data-driven learning strategy for ISC fault detection in SIBs. Specifically, an electrochemical workstation is first used to collect EIS data from SIBs with various degrees of ISC faults at different states of charge (SOC). To shorten the time for EIS extraction, the importance of each frequency feature is quantified and the impact of different frequency bands on ISC fault detection is analyzed to identify the most important frequency band as the primary fault features. Finally, to address the challenge of limited EIS data leading to poor performance in traditional machine learning methods, this study develops a data-driven learning strategy based on the Tabular prior-data fitted network (TabPFN), which achieves strong performance on small datasets due to its pretraining capabilities. The experimental results show that the proposed method achieves satisfactory results in detecting ISC fault of SIBs when only partial frequency band of EIS is used, with an accuracy of 95.4% and an area under curve (AUC) value of 0.967.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2630-2632"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859870","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}
This paper is concerned with the problem of distributed coordination energy management of integrated energy systems (IESs). First, an energy management model for IESs is established and formulated as a distributed constrained optimization problem. Then, an accelerated distributed event-triggered algorithm is developed to solve the problem. Compared with the existing algorithms, the developed algorithm simultaneously offers two advantages. On the one hand, the convergence speed of the algorithm is improved greatly by incorporating the second-order information. On the other hand, the algorithm is implemented with asynchronous communication by an event-triggered mechanism, effectively reducing communication interact. Furthermore, the convergence and optimality of the algorithm are analyzed rigorously based on Lyapunov method. Finally, simulation studies are provided to validate the effectiveness of the algorithm.
{"title":"Accelerated Distributed Cooperative Energy Management for Integrated Energy Systems","authors":"Lining Liu;Yulong Huang;Chao Deng","doi":"10.1109/JAS.2025.125489","DOIUrl":"https://doi.org/10.1109/JAS.2025.125489","url":null,"abstract":"This paper is concerned with the problem of distributed coordination energy management of integrated energy systems (IESs). First, an energy management model for IESs is established and formulated as a distributed constrained optimization problem. Then, an accelerated distributed event-triggered algorithm is developed to solve the problem. Compared with the existing algorithms, the developed algorithm simultaneously offers two advantages. On the one hand, the convergence speed of the algorithm is improved greatly by incorporating the second-order information. On the other hand, the algorithm is implemented with asynchronous communication by an event-triggered mechanism, effectively reducing communication interact. Furthermore, the convergence and optimality of the algorithm are analyzed rigorously based on Lyapunov method. Finally, simulation studies are provided to validate the effectiveness of the algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2589-2601"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861233","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}
Optimal output-feedback stabilization of nonlinear plants under variation of model parameters and partial observability of states is a challenging problem. Safety-critical applications face additional hurdles to preclude systems' trajectories from encountering any unsafe state. To address these challenges, this paper extends a Lyapunov-based framework introduced recently for safety and stability-guaranteed neural network (NN)-based state-feedback control synthesis. In particular, here we propose a novel sufficient condition of the stabilizability of nonlinear partially observed systems under Lipschitz-bounded output-feedback controllers (OFCs), which generalizes such a condition proposed in the earlier work assuming full observability of states. A new algorithm is proposed that employs this newly devised condition to compute a maximal Lipschitz bound of OFCs and a corresponding maximal robust-safe-region-of-stabilization, enabling a safety and stability-guaranteed training of an NN-based optimal OFC by constraining the NN's Lipschitz constant within the computed bound. The proposed method is validated using a numerical example and a single-generator-infinite-bus power systern model.
{"title":"Robust Safety and Stability of Partially Observed Nonlinear Systems with Parametric Variability","authors":"Soumyabrata Talukder;Ratnesh Kumar","doi":"10.1109/JAS.2025.125837","DOIUrl":"https://doi.org/10.1109/JAS.2025.125837","url":null,"abstract":"Optimal output-feedback stabilization of nonlinear plants under variation of model parameters and partial observability of states is a challenging problem. Safety-critical applications face additional hurdles to preclude systems' trajectories from encountering any unsafe state. To address these challenges, this paper extends a Lyapunov-based framework introduced recently for safety and stability-guaranteed neural network (NN)-based state-feedback control synthesis. In particular, here we propose a novel sufficient condition of the stabilizability of nonlinear partially observed systems under Lipschitz-bounded output-feedback controllers (OFCs), which generalizes such a condition proposed in the earlier work assuming full observability of states. A new algorithm is proposed that employs this newly devised condition to compute a maximal Lipschitz bound of OFCs and a corresponding maximal robust-safe-region-of-stabilization, enabling a safety and stability-guaranteed training of an NN-based optimal OFC by constraining the NN's Lipschitz constant within the computed bound. The proposed method is validated using a numerical example and a single-generator-infinite-bus power systern model.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2572-2588"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861224","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, As the Internet of things (IoT) and autonomous driving continue to evolve, positioning technology faces increasing demands for higher accuracy and reliability. Traditional positioning methods often struggle in complex signal environments with multipath interference and non-line-of-sight (NLOS) conditions. Reconfigurable intelligent surfaces (RIS), an innovative technology that can flexibly control signal propagation, offer new possibilities for positioning systems. To fully harness the potential of RIS, the concept of parallel intelligence becomes crucial. With parallel intelligence, artificial models run alongside real systems, enabling real-time monitoring and RIS optimization. This letter introduces a parallel intelligence-based RIS-aided positioning framework, designed to improve positioning accuracy and adaptability. The proposed approach introduces a system framework where artificial and physical systems interact for continuous RIS optimization. The technique holds significant potential in various applications, including smart transportation, autonomous driving, and industrial IoT.
{"title":"Towards Enhanced Precision Positioning with Parallel Intelligence and Reconfigurable Intelligent Surfaces","authors":"Shuangshuang Han;Fei-Yue Wang;Yuhang Liu;Guiyang Luo;Fengzhong Qu","doi":"10.1109/JAS.2024.125010","DOIUrl":"https://doi.org/10.1109/JAS.2024.125010","url":null,"abstract":"Dear Editor, As the Internet of things (IoT) and autonomous driving continue to evolve, positioning technology faces increasing demands for higher accuracy and reliability. Traditional positioning methods often struggle in complex signal environments with multipath interference and non-line-of-sight (NLOS) conditions. Reconfigurable intelligent surfaces (RIS), an innovative technology that can flexibly control signal propagation, offer new possibilities for positioning systems. To fully harness the potential of RIS, the concept of parallel intelligence becomes crucial. With parallel intelligence, artificial models run alongside real systems, enabling real-time monitoring and RIS optimization. This letter introduces a parallel intelligence-based RIS-aided positioning framework, designed to improve positioning accuracy and adaptability. The proposed approach introduces a system framework where artificial and physical systems interact for continuous RIS optimization. The technique holds significant potential in various applications, including smart transportation, autonomous driving, and industrial IoT.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2362-2364"},"PeriodicalIF":19.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830885","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}
Evolutionary multi-task optimization (EMTO) presents an efficient way to solve multiple tasks simultaneously. However, difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases. Motivated by this, a novel self-adjusting dual-mode evolutionary framework, which integrates variable classification evolution and knowledge dynamic transfer strategies, is designed to compensate for this deficiency. First, a dual-mode evolutionary framework is designed to meet the needs of evolution in different states. Then, a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes. Second, a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes. Then, the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables. Third, an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge. Then, a dynamic weighting strategy is developed for efficient utilization of knowledge. Finally, by conducting experiments and comparing the designed method with several existing algorithms, the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.
{"title":"A Novel Self-Adjusting Dual-Mode Evolutionary Framework for Multi-Task Optimization","authors":"Yingbo Xie;Junfei Qiao;Ding Wang;Manman Yuan","doi":"10.1109/JAS.2025.125273","DOIUrl":"https://doi.org/10.1109/JAS.2025.125273","url":null,"abstract":"Evolutionary multi-task optimization (EMTO) presents an efficient way to solve multiple tasks simultaneously. However, difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases. Motivated by this, a novel self-adjusting dual-mode evolutionary framework, which integrates variable classification evolution and knowledge dynamic transfer strategies, is designed to compensate for this deficiency. First, a dual-mode evolutionary framework is designed to meet the needs of evolution in different states. Then, a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes. Second, a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes. Then, the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables. Third, an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge. Then, a dynamic weighting strategy is developed for efficient utilization of knowledge. Finally, by conducting experiments and comparing the designed method with several existing algorithms, the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2239-2252"},"PeriodicalIF":19.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674730","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 investigates the grid-forming control for power converters. Recently, grid-forming control based on matching of synchronous machines was suggested by using continuous measurements. In the present letter, we suggest a digital implementation using artificial delays where the controller employs the discrete-time measurements only. The resulting closed-loop system has additional terms due to the sampling compared with the case using the continuous measurements. We suggest appropriate Lyapunov functionals to obtain linear matrix inequalities (LMIs) for finding the upper bounds on the sampling that guarantees the globally exponential stability of a shifted equilibrium. Numerical case study illustrates the efficiency of the method.
{"title":"Digital Implementation of Grid-Forming Control for Power Converters Using Artificial Delays","authors":"Jing Shi;Jin Zhang;Chen Peng;Minrui Fei","doi":"10.1109/JAS.2025.125471","DOIUrl":"https://doi.org/10.1109/JAS.2025.125471","url":null,"abstract":"Dear Editor, This letter investigates the grid-forming control for power converters. Recently, grid-forming control based on matching of synchronous machines was suggested by using continuous measurements. In the present letter, we suggest a digital implementation using artificial delays where the controller employs the discrete-time measurements only. The resulting closed-loop system has additional terms due to the sampling compared with the case using the continuous measurements. We suggest appropriate Lyapunov functionals to obtain linear matrix inequalities (LMIs) for finding the upper bounds on the sampling that guarantees the globally exponential stability of a shifted equilibrium. Numerical case study illustrates the efficiency of the method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 11","pages":"2368-2370"},"PeriodicalIF":19.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674856","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}
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