Fei Lin;Jing Yang;Dali Sun;Levente Kovács;Fei-Yue Wang
Dear Editor, The 2024 Nobel Prize in Chemistry was awarded to David Baker, Demis Hassabis, and John Jumper, recognizing their groundbreaking contributions to protein design and the prediction of complex protein structures [1]. This accomplishment advances the frontier of “Artificial Intelligence (AI) for Science”. It marks a milestone in studying complex systems, highlighting a shift in scientific exploration from traditional causal inference to a comprehensive approach centered on solving complex system problems.
{"title":"Autonomous Drug Discovery with Parallel Intelligence","authors":"Fei Lin;Jing Yang;Dali Sun;Levente Kovács;Fei-Yue Wang","doi":"10.1109/JAS.2025.125426","DOIUrl":"https://doi.org/10.1109/JAS.2025.125426","url":null,"abstract":"Dear Editor, The 2024 Nobel Prize in Chemistry was awarded to David Baker, Demis Hassabis, and John Jumper, recognizing their groundbreaking contributions to protein design and the prediction of complex protein structures [1]. This accomplishment advances the frontier of “Artificial Intelligence (AI) for Science”. It marks a milestone in studying complex systems, highlighting a shift in scientific exploration from traditional causal inference to a comprehensive approach centered on solving complex system problems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1742-1744"},"PeriodicalIF":19.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131624","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880575","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}
Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.
{"title":"Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey","authors":"Qiong Li;Xiaotong Liu;Xuecai Hu;Md Atiqur Rahman Ahad;Min Ren;Li Yao;Yongzhen Huang","doi":"10.1109/JAS.2025.125393","DOIUrl":"https://doi.org/10.1109/JAS.2025.125393","url":null,"abstract":"Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1320-1349"},"PeriodicalIF":15.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536529","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 proposes a novel Nash bargaining solution-based multi-objective model predictive control (MPC) scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot. Considering the elastic interaction force model, a mechanical trade-off always exists between the interaction force and position, which means that neither force nor path following can satisfy their desired demands completely. Based on this consideration, two irreconcilable control specifications, the force object function and the position track object function, are proposed, and a new multi-objective MPC scheme is then designed. At each sampling interval, the control action is chosen automatically among the set of Pareto optimal solutions with the Nash bargaining solution from the cooperative game theory. Furthermore, we set state and control constraints to consider physical limitations. The proposed controller's efficacy is demonstrated through simulations on a constrained interactive robot.
{"title":"Nash Bargaining Solution-Based Multi-Objective Model Predictive Control for Constrained Interactive Robots","authors":"Minglei Zhu;Jun Qi","doi":"10.1109/JAS.2024.124398","DOIUrl":"https://doi.org/10.1109/JAS.2024.124398","url":null,"abstract":"Dear Editor, This letter proposes a novel Nash bargaining solution-based multi-objective model predictive control (MPC) scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot. Considering the elastic interaction force model, a mechanical trade-off always exists between the interaction force and position, which means that neither force nor path following can satisfy their desired demands completely. Based on this consideration, two irreconcilable control specifications, the force object function and the position track object function, are proposed, and a new multi-objective MPC scheme is then designed. At each sampling interval, the control action is chosen automatically among the set of Pareto optimal solutions with the Nash bargaining solution from the cooperative game theory. Furthermore, we set state and control constraints to consider physical limitations. The proposed controller's efficacy is demonstrated through simulations on a constrained interactive robot.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1516-1518"},"PeriodicalIF":15.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a novel hybrid event-triggered control (ETC) method is developed based on the online action-critic technique, which aims at tackling the optimal regulation problem of discrete-time nonlinear systems. In order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain the initial admissible control policy by using an offline iterative method under the time-triggered control framework. Subsequently, a general triggering condition is designed based on the uniform ultimate boundedness of the controlled system. In order to determine a constant interval which can ensure the system stability, another triggering condition is introduced and the asymptotic stability of the closed-loop system satisfying this condition is analyzed from the perspective of the input-to-state stability. The designed online hybrid ETC method not only further improves control efficiency, but also avoids the continuous judgment of the corresponding triggering condition. In addition, the event-based control law can approach the optimal control input within a finite approximation error. Finally, two experimental examples with physical background are conducted to indicate the present results.
{"title":"Hybrid Event-Triggered Control with Stability Analysis","authors":"Ding Wang;Lingzhi Hu;Junfei Qiao","doi":"10.1109/JAS.2024.125067","DOIUrl":"https://doi.org/10.1109/JAS.2024.125067","url":null,"abstract":"In this paper, a novel hybrid event-triggered control (ETC) method is developed based on the online action-critic technique, which aims at tackling the optimal regulation problem of discrete-time nonlinear systems. In order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain the initial admissible control policy by using an offline iterative method under the time-triggered control framework. Subsequently, a general triggering condition is designed based on the uniform ultimate boundedness of the controlled system. In order to determine a constant interval which can ensure the system stability, another triggering condition is introduced and the asymptotic stability of the closed-loop system satisfying this condition is analyzed from the perspective of the input-to-state stability. The designed online hybrid ETC method not only further improves control efficiency, but also avoids the continuous judgment of the corresponding triggering condition. In addition, the event-based control law can approach the optimal control input within a finite approximation error. Finally, two experimental examples with physical background are conducted to indicate the present results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1464-1474"},"PeriodicalIF":15.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536560","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 presents a new secure hierarchical control strategy for steering tracking of in-wheel motor driven (IWMD) electric vehicle (EV) subject to limited network resources, hybrid cyber-attacks, model nonlinearities, actuator redundancy and airflow disturbance. A hierarchical control architecture is proposed specifically for solving the problems of nonlinear system modeling and actuator redundancy. By utilizing the advantages of fully actuated system (FAS) approach, a nonlinear virtual controller against airflow disturbance is constructed in upper layer system and an event-triggered nonlinear distributed controller is proposed in lower layer system under stochastic hybrid cyber-attacks. A case study of overtaking task is carried out to validate the FAS-based hierarchical control strategy.
{"title":"Hierarchical Secure Steering Control of In-Wheel Motor Driven Electric Vehicle Under Cyber-Physical Constraints","authors":"Zifan Gao;Dawei Zhang;Shuqian Zhu","doi":"10.1109/JAS.2023.124092","DOIUrl":"https://doi.org/10.1109/JAS.2023.124092","url":null,"abstract":"Dear Editor, This letter presents a new secure hierarchical control strategy for steering tracking of in-wheel motor driven (IWMD) electric vehicle (EV) subject to limited network resources, hybrid cyber-attacks, model nonlinearities, actuator redundancy and airflow disturbance. A hierarchical control architecture is proposed specifically for solving the problems of nonlinear system modeling and actuator redundancy. By utilizing the advantages of fully actuated system (FAS) approach, a nonlinear virtual controller against airflow disturbance is constructed in upper layer system and an event-triggered nonlinear distributed controller is proposed in lower layer system under stochastic hybrid cyber-attacks. A case study of overtaking task is carried out to validate the FAS-based hierarchical control strategy.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1504-1506"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536591","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}
The problem of high-performance tracking control for the lower-triangular systems with unknown sign-switching virtual control coefficients as well as unmatched disturbances is investigated in this paper. Instead of the online estimation algorithm, the sliding mode method and the Nussbaum gain technique, a group of orientation functions are employed to handle the unknown sign-switching virtual control coefficients. The control law is combined with the orientation functions and the barrier functions lumped in a recursive manner. It achieves output tracking with the preassigned rate, overshoot, and accuracy. In contrast with the existing solutions, it is effective for the nearly model-free case, with the requirement for information of neither the system nonlinearities nor their bounding functions of the plant, nor the bounds of the disturbances. In addition, our controller exhibits significant simplicity, without parameter identification, disturbance estimation, function approximation, derivative calculation, dynamic surfaces, or command filtering. Two simulation examples are conducted to substantiate the efficacy and advantages of our approach.
{"title":"Prescribed Performance Control of Nonlinear Systems With Unknown Sign-Switching Virtual Control Coefficients","authors":"Jin-Zi Yang;Jin-Xi Zhang;Tianyou Chai","doi":"10.1109/JAS.2025.125135","DOIUrl":"https://doi.org/10.1109/JAS.2025.125135","url":null,"abstract":"The problem of high-performance tracking control for the lower-triangular systems with unknown sign-switching virtual control coefficients as well as unmatched disturbances is investigated in this paper. Instead of the online estimation algorithm, the sliding mode method and the Nussbaum gain technique, a group of orientation functions are employed to handle the unknown sign-switching virtual control coefficients. The control law is combined with the orientation functions and the barrier functions lumped in a recursive manner. It achieves output tracking with the preassigned rate, overshoot, and accuracy. In contrast with the existing solutions, it is effective for the nearly model-free case, with the requirement for information of neither the system nonlinearities nor their bounding functions of the plant, nor the bounds of the disturbances. In addition, our controller exhibits significant simplicity, without parameter identification, disturbance estimation, function approximation, derivative calculation, dynamic surfaces, or command filtering. Two simulation examples are conducted to substantiate the efficacy and advantages of our approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1381-1390"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536553","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}
De-Yu Zhou;Xiao Xue;Qun Ma;Chao Guo;Li-Zhen Cui;Yong-Lin Tian;Jing Yang;Fei-Yue Wang
Computational experiments method is an essential tool for analyzing, designing, managing, and integrating complex systems. However, a significant challenge arises in constructing agents with human-like characteristics to form an AI society. Agent modeling typically encompasses four levels: 1) The autonomy features of agents, e.g., perception, behavior, and decision-making; 2) The evolutionary features of agents, e.g., bounded rationality, heterogeneity, and learning evolution; 3) The social features of agents, e.g., interaction, cooperation, and competition; 4) The emergent features of agents, e.g., gaming with environments or regulatory strategies. Traditional modeling techniques primarily derive from ABMs (Agent-based Models) and incorporate various emerging technologies (e.g., machine learning, big data, and social networks), which can enhance modeling capabilities, while amplifying the complexity [1].
{"title":"Federated Experiments: Generative Causal Inference Powered by LLM-Based Agents Simulation and RAG-Based Domain Docking","authors":"De-Yu Zhou;Xiao Xue;Qun Ma;Chao Guo;Li-Zhen Cui;Yong-Lin Tian;Jing Yang;Fei-Yue Wang","doi":"10.1109/JAS.2024.124671","DOIUrl":"https://doi.org/10.1109/JAS.2024.124671","url":null,"abstract":"Computational experiments method is an essential tool for analyzing, designing, managing, and integrating complex systems. However, a significant challenge arises in constructing agents with human-like characteristics to form an AI society. Agent modeling typically encompasses four levels: 1) The autonomy features of agents, e.g., perception, behavior, and decision-making; 2) The evolutionary features of agents, e.g., bounded rationality, heterogeneity, and learning evolution; 3) The social features of agents, e.g., interaction, cooperation, and competition; 4) The emergent features of agents, e.g., gaming with environments or regulatory strategies. Traditional modeling techniques primarily derive from ABMs (Agent-based Models) and incorporate various emerging technologies (e.g., machine learning, big data, and social networks), which can enhance modeling capabilities, while amplifying the complexity [1].","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1301-1304"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536434","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}
Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.
{"title":"A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority","authors":"Liang Xin;Zhi-Qiang Long","doi":"10.1109/JAS.2024.124683","DOIUrl":"https://doi.org/10.1109/JAS.2024.124683","url":null,"abstract":"Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1368-1380"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536562","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 optimal denial-of-service (DoS) attack scheduling targeting state estimation in cyber-Physical systems (CPSs) with the two-hop multi-channel network. CPSs are designed to achieve efficient, secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world. As a key component of the CPSs, the wireless network is vulnerable to various malicious attacks due to its openness [1]. DoS attack is one of the most common attacks, characterized of simple execution and significant destructiveness [2]. To mitigate the economic losses and environmental damage caused by DoS attacks, it is crucial to model and investigate data transmissions in CPSs.
{"title":"DoS Attack Schedules for Remote State Estimation in CPSs with Two-Hop Relay Networks Under Round-Robin Protocol","authors":"Shuo Zhang;Lei Miao;Xudong Zhao","doi":"10.1109/JAS.2024.124755","DOIUrl":"https://doi.org/10.1109/JAS.2024.124755","url":null,"abstract":"Dear Editor, This letter investigates the optimal denial-of-service (DoS) attack scheduling targeting state estimation in cyber-Physical systems (CPSs) with the two-hop multi-channel network. CPSs are designed to achieve efficient, secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world. As a key component of the CPSs, the wireless network is vulnerable to various malicious attacks due to its openness [1]. DoS attack is one of the most common attacks, characterized of simple execution and significant destructiveness [2]. To mitigate the economic losses and environmental damage caused by DoS attacks, it is crucial to model and investigate data transmissions in CPSs.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1513-1515"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536432","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}
To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system (IES) that supports the power Internet of Things (IoT), this paper proposes a data-driven cooperative method for automatic generation control (AGC). The method consists of adaptive fractional-order proportional-integral (FOPI) controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient (EIE-MATD3) algorithm. The FOPI controllers are designed for each area based on the performance-based frequency regulation market mechanism. The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution. The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy. An experiment on the four-area China Southern Grid (CSG) real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.
{"title":"A Robust Large-Scale Multiagent Deep Reinforcement Learning Method for Coordinated Automatic Generation Control of Integrated Energy Systems in a Performance-Based Frequency Regulation Market","authors":"Jiawen Li;Tao Zhou","doi":"10.1109/JAS.2024.124482","DOIUrl":"https://doi.org/10.1109/JAS.2024.124482","url":null,"abstract":"To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system (IES) that supports the power Internet of Things (IoT), this paper proposes a data-driven cooperative method for automatic generation control (AGC). The method consists of adaptive fractional-order proportional-integral (FOPI) controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient (EIE-MATD3) algorithm. The FOPI controllers are designed for each area based on the performance-based frequency regulation market mechanism. The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution. The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy. An experiment on the four-area China Southern Grid (CSG) real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1475-1488"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536436","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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