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}
System identification is a data-driven modeling technique that originates from the control field. It constructs models from data to mimic the behavior of dynamic systems. However, in the network era, scenarios such as sensor malfunctions, packet loss, cyber-attacks, and big data affect the quality, integrity, and security of the data. These data issues pose significant challenges to traditional system identification methods. This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era. It explores cutting-edge methodologies to address data issues such as data loss, outliers, noise and nonlinear system identification for complex systems. To tackle the data loss, the methods based on imputation and likelihood-based inference (e.g., expectation maximization) have been employed. For outliers and noise, methods like robust regression (e.g., least median of squares, least trimmed squares) and low-rank matrix decomposition show progress in maintaining data integrity. Nonlinear system identification has advanced through kernel-based methods and neural networks, which can model complex data patterns. Finally, this paper provides valuable insights into potential directions for future research.
{"title":"System Identification in the Network Era: A Survey of Data Issues and Innovative Approaches","authors":"Qing-Guo Wang;Liang Zhang","doi":"10.1109/JAS.2024.125109","DOIUrl":"https://doi.org/10.1109/JAS.2024.125109","url":null,"abstract":"System identification is a data-driven modeling technique that originates from the control field. It constructs models from data to mimic the behavior of dynamic systems. However, in the network era, scenarios such as sensor malfunctions, packet loss, cyber-attacks, and big data affect the quality, integrity, and security of the data. These data issues pose significant challenges to traditional system identification methods. This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era. It explores cutting-edge methodologies to address data issues such as data loss, outliers, noise and nonlinear system identification for complex systems. To tackle the data loss, the methods based on imputation and likelihood-based inference (e.g., expectation maximization) have been employed. For outliers and noise, methods like robust regression (e.g., least median of squares, least trimmed squares) and low-rank matrix decomposition show progress in maintaining data integrity. Nonlinear system identification has advanced through kernel-based methods and neural networks, which can model complex data patterns. Finally, this paper provides valuable insights into potential directions for future research.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1305-1319"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536309","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 addresses the enhancement of autonomous vehicles' (AVs) behavior control systems through the application of reinforcement learning (RL) techniques. It presents a novel approach to efficient knowledge-guided self-evolutionary intelligent decision-making by integrating human intervention as prior knowledge into the RL's exploratory learning process. Specifically, we propose an innovative intervention-based reward shaping mechanism and develop a novel experience replay mechanism to augment the efficiency of leveraging guided knowledge within the framework of off-policy RL. The proposed methodology significantly enhances the performance of RL-based behavior control strategies in complex scenarios for AVs. Illustrative results indicate that, relative to existing state-of-the-art methods, our approach yields superior learning efficiency and improved autonomous driving performance.
{"title":"Efficient Knowledge-Guided Self-Evolving Intelligent Behavioral Control for Autonomous Vehicles","authors":"Qiao Peng;Kailong Liu;Jingda Wu;Amir Khajepour","doi":"10.1109/JAS.2024.124746","DOIUrl":"https://doi.org/10.1109/JAS.2024.124746","url":null,"abstract":"Dear Editor, This letter addresses the enhancement of autonomous vehicles' (AVs) behavior control systems through the application of reinforcement learning (RL) techniques. It presents a novel approach to efficient knowledge-guided self-evolutionary intelligent decision-making by integrating human intervention as prior knowledge into the RL's exploratory learning process. Specifically, we propose an innovative intervention-based reward shaping mechanism and develop a novel experience replay mechanism to augment the efficiency of leveraging guided knowledge within the framework of off-policy RL. The proposed methodology significantly enhances the performance of RL-based behavior control strategies in complex scenarios for AVs. Illustrative results indicate that, relative to existing state-of-the-art methods, our approach yields superior learning efficiency and improved autonomous driving performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1522-1524"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11062710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536526","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}
There has been significant recent research on secure control problems that arise from the open and complex real-world industrial environments. This paper focuses on addressing the issue of secure consensus control in multi-agent systems (MASs) under malicious attacks, utilizing the practical Byzantine fault tolerance (PBFT) and Raft consensus algorithm in blockchain. Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs, our approach introduces a node grouping methodology based on system topology. Additionally, we utilize the PBFT consensus algorithm for intergroup leader identity verification, effectively reducing the communication complexity of PBFT in large-scale networks. Furthermore, we enhance the Raft algorithm through cryptographic validation during followers' log replication, which enhances the security of the system. Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents. Through extensive simulations, we demonstrate robust convergence, particularly in scenarios with the relaxed topological requirements. Comparative experiments also validate the algorithm's lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced (MSR) algorithm.
{"title":"Secure Consensus Control on Multi-Agent Systems Based on Improved PBFT and Raft Blockchain Consensus Algorithms","authors":"Jing Zhu;Chengfang Lu;Juanjuan Li;Fei-Yue Wang","doi":"10.1109/JAS.2025.125300","DOIUrl":"https://doi.org/10.1109/JAS.2025.125300","url":null,"abstract":"There has been significant recent research on secure control problems that arise from the open and complex real-world industrial environments. This paper focuses on addressing the issue of secure consensus control in multi-agent systems (MASs) under malicious attacks, utilizing the practical Byzantine fault tolerance (PBFT) and Raft consensus algorithm in blockchain. Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs, our approach introduces a node grouping methodology based on system topology. Additionally, we utilize the PBFT consensus algorithm for intergroup leader identity verification, effectively reducing the communication complexity of PBFT in large-scale networks. Furthermore, we enhance the Raft algorithm through cryptographic validation during followers' log replication, which enhances the security of the system. Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents. Through extensive simulations, we demonstrate robust convergence, particularly in scenarios with the relaxed topological requirements. Comparative experiments also validate the algorithm's lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced (MSR) algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1407-1417"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536431","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, With the advances in computing and communication technologies, the cyber-physical system (CPS), has been used in lots of industrial fields, such as the urban water cycle, internet of things, and human-cyber systems [1], [2], which has to face up to malicious cyber-attacks towards cyber communication of control commands. Specifically, jamming attack is regarded as one of the most common attacks of decreasing network performance. Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user [3]. In the cyber layer, the signal game model has been utilized to describe the transmission between the attacker and defender [4]. However, most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature. Specifically, it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols. In the physical layer, the secure control [5] and estimation [6] under attack detection have been studied for CPSs. However, these methods not only rely heavily on signals injection detection, but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour [7]. Accordingly, the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.
{"title":"Robust Optimization Control for Cyber-Physical Systems Subject to Jamming Attack: A Nested Game Approach","authors":"Min Shi;Yuan Yuan","doi":"10.1109/JAS.2023.123873","DOIUrl":"https://doi.org/10.1109/JAS.2023.123873","url":null,"abstract":"Dear Editor, With the advances in computing and communication technologies, the cyber-physical system (CPS), has been used in lots of industrial fields, such as the urban water cycle, internet of things, and human-cyber systems [1], [2], which has to face up to malicious cyber-attacks towards cyber communication of control commands. Specifically, jamming attack is regarded as one of the most common attacks of decreasing network performance. Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user [3]. In the cyber layer, the signal game model has been utilized to describe the transmission between the attacker and defender [4]. However, most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature. Specifically, it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols. In the physical layer, the secure control [5] and estimation [6] under attack detection have been studied for CPSs. However, these methods not only rely heavily on signals injection detection, but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour [7]. Accordingly, the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1286-1288"},"PeriodicalIF":15.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299410","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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