Shasha Yu;Yanan Qiao;Fan Yang;Wenjia Zhao;Junge Bo
The proof-of-stake (PoS) mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant's stake in the cryptocurrency network. In contrast to proof-of-work (PoW), which relies on computational power to validate transactions, PoS employs a deterministic and resource-efficient approach to elect validators. Whereas, an inherent risk of PoS is the potential for centralization among a small cohort of network participants possessing substantial stakes, jeopardizing system decentralization and posing security threats. To mitigate centralization issues within PoS, this study introduces an incentive-aligned mechanism named decentralized proof-of-stake (DePoS), wherein the second-largest stakeholder is chosen as the final validator with a higher probability. Integrated with the verifiable random function (VRF), DePoS rewards the largest stake-holder with uncertainty, thus disincentivizing stakeholders from accumulating the largest stake. Additionally, a dynamic evolutionary game model is innovatively developed to simulate the evolution of staking pools, thus facilitating the investigation of staking pool selection dynamics and equilibrium stability across PoS and DePoS systems. The findings demonstrate that DePoS generally fosters wealth decentralization by discouraging the accumulation of significant cryptocurrency holdings. Through theoretical analysis of stakeholder predilection in staking pool selection and the simulation of the evolutionary tendency in pool scale, this research demonstrates the comparative advantage in decentralization offered by DePoS over the conventional PoS.
{"title":"Dynamic Evolutionary Game-Based Staking Pool Selection Modeling and Decentralization Enhancement for Blockchain System","authors":"Shasha Yu;Yanan Qiao;Fan Yang;Wenjia Zhao;Junge Bo","doi":"10.1109/JAS.2025.125447","DOIUrl":"https://doi.org/10.1109/JAS.2025.125447","url":null,"abstract":"The proof-of-stake (PoS) mechanism is a consensus protocol within blockchain technology that determines the validation of transactions and the minting of new blocks based on the participant's stake in the cryptocurrency network. In contrast to proof-of-work (PoW), which relies on computational power to validate transactions, PoS employs a deterministic and resource-efficient approach to elect validators. Whereas, an inherent risk of PoS is the potential for centralization among a small cohort of network participants possessing substantial stakes, jeopardizing system decentralization and posing security threats. To mitigate centralization issues within PoS, this study introduces an incentive-aligned mechanism named decentralized proof-of-stake (DePoS), wherein the second-largest stakeholder is chosen as the final validator with a higher probability. Integrated with the verifiable random function (VRF), DePoS rewards the largest stake-holder with uncertainty, thus disincentivizing stakeholders from accumulating the largest stake. Additionally, a dynamic evolutionary game model is innovatively developed to simulate the evolution of staking pools, thus facilitating the investigation of staking pool selection dynamics and equilibrium stability across PoS and DePoS systems. The findings demonstrate that DePoS generally fosters wealth decentralization by discouraging the accumulation of significant cryptocurrency holdings. Through theoretical analysis of stakeholder predilection in staking pool selection and the simulation of the evolutionary tendency in pool scale, this research demonstrates the comparative advantage in decentralization offered by DePoS over the conventional PoS.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 9","pages":"1850-1865"},"PeriodicalIF":19.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335284","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}
Chaoqun Fei;Yangyang Li;Xikun Huang;Ge Zhang;Ruqian Lu
Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical method for discovering latent geometric structures, has provided important nonlinear insight for the theoretical development of unsupervised representation learning. However, due to the shallow learning mechanism of the existing methods, they can only exploit the simple geometric structure embedded in the initial data, such as the local linear structure. Traditional manifold learning methods are fairly limited in mining higher-order non-linear geometric information, which is also crucial for the development of unsupervised representation learning. To address the abovementioned limitations, this paper proposes a novel dynamic geometric structure learning model (DGSL) to explore the true latent nonlinear geometric structure. Specifically, by mathematically analysing the reconstruction loss function of manifold learning, we first provide universal geometric relational function between the curvature and the non-Euclidean metric of the initial data. Then, we leverage geometric flow to design a deeply iterative learning model to optimize this relational function. Our method can be viewed as a general-purpose algorithm for mining latent geometric structures, which can enhance the performance of geometric representation methods. Experimentally, we perform a set of representation learning tasks on several datasets. The experimental results show that our proposed method is superior to traditional methods.
{"title":"Unsupervised Dynamic Discrete Structure Learning: A Geometric Evolution Method","authors":"Chaoqun Fei;Yangyang Li;Xikun Huang;Ge Zhang;Ruqian Lu","doi":"10.1109/JAS.2025.125165","DOIUrl":"https://doi.org/10.1109/JAS.2025.125165","url":null,"abstract":"Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical method for discovering latent geometric structures, has provided important nonlinear insight for the theoretical development of unsupervised representation learning. However, due to the shallow learning mechanism of the existing methods, they can only exploit the simple geometric structure embedded in the initial data, such as the local linear structure. Traditional manifold learning methods are fairly limited in mining higher-order non-linear geometric information, which is also crucial for the development of unsupervised representation learning. To address the abovementioned limitations, this paper proposes a novel dynamic geometric structure learning model (DGSL) to explore the true latent nonlinear geometric structure. Specifically, by mathematically analysing the reconstruction loss function of manifold learning, we first provide universal geometric relational function between the curvature and the non-Euclidean metric of the initial data. Then, we leverage geometric flow to design a deeply iterative learning model to optimize this relational function. Our method can be viewed as a general-purpose algorithm for mining latent geometric structures, which can enhance the performance of geometric representation methods. Experimentally, we perform a set of representation learning tasks on several datasets. The experimental results show that our proposed method is superior to traditional methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 9","pages":"1920-1937"},"PeriodicalIF":19.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335285","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 studies the problem of designing a model-based decentralized dynamic periodic event-triggering mechanism (DDPETM) for networked control systems (NCSs) subject to packet losses and external disturbances. Firstly, the entire NCSs, comprising the triggering mechanism, packet losses and output-based controller, are unified into a hybrid dynamical framework. Secondly, by introducing dynamic triggering variables, the DDPETM is designed to conserve network resources while guaranteeing desired performance properties and tolerating the maximum allowable number of successive packet losses. Thirdly, some stability conditions are derived using the Lyapunov approach. Differing from the zero-order-hold (ZOH) case, the model-based control sufficiently exploits the model information at the controller side. Between two updates, the controller predicts the plant state based on the models and received feedback information. With the model-based control, less transmission may be expected than with ZOH. Finally, numerical examples and comparative experiments demonstrate the effectiveness of the proposed method.
{"title":"Model-Based Decentralized Dynamic Periodic Event-Triggered Control for Nonlinear Systems Subject to Packet Losses","authors":"Chengchao Li;Xudong Zhao;Wei Xing;Ning Xu;Ning Zhao","doi":"10.1109/JAS.2025.125459","DOIUrl":"https://doi.org/10.1109/JAS.2025.125459","url":null,"abstract":"This paper studies the problem of designing a model-based decentralized dynamic periodic event-triggering mechanism (DDPETM) for networked control systems (NCSs) subject to packet losses and external disturbances. Firstly, the entire NCSs, comprising the triggering mechanism, packet losses and output-based controller, are unified into a hybrid dynamical framework. Secondly, by introducing dynamic triggering variables, the DDPETM is designed to conserve network resources while guaranteeing desired performance properties and tolerating the maximum allowable number of successive packet losses. Thirdly, some stability conditions are derived using the Lyapunov approach. Differing from the zero-order-hold (ZOH) case, the model-based control sufficiently exploits the model information at the controller side. Between two updates, the controller predicts the plant state based on the models and received feedback information. With the model-based control, less transmission may be expected than with ZOH. Finally, numerical examples and comparative experiments demonstrate the effectiveness of the proposed method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 9","pages":"1908-1919"},"PeriodicalIF":19.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335343","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}
Peijun Ye;Xiao Xue;Qinghua Ni;Jing Yang;Fei-Yue Wang
Experiment is one of the necessary conditions for scientific progress. For cognitive science, neuroscience, biomedical science and other human-related disciplines, experiments involving human subjects can confirm or disprove scientific hypotheses in a controlled and systematic manner, while establishing causal relationships between studied variables. These experiments also provide both qualitative and quantitative analysis capable of statistically identifying significant patterns. Thus, solid experiments directly support testable and replicable scientific conclusions. However, limited by the budget as well as the available candidate group, current experiment design selects random subjective in an arbitrary scale, bringing a question that how they can stand for the whole studied population.
{"title":"Parallel Experiments: From The Human Participated to A Virtual-Real Hybrid Paradigm","authors":"Peijun Ye;Xiao Xue;Qinghua Ni;Jing Yang;Fei-Yue Wang","doi":"10.1109/JAS.2025.125474","DOIUrl":"https://doi.org/10.1109/JAS.2025.125474","url":null,"abstract":"Experiment is one of the necessary conditions for scientific progress. For cognitive science, neuroscience, biomedical science and other human-related disciplines, experiments involving human subjects can confirm or disprove scientific hypotheses in a controlled and systematic manner, while establishing causal relationships between studied variables. These experiments also provide both qualitative and quantitative analysis capable of statistically identifying significant patterns. Thus, solid experiments directly support testable and replicable scientific conclusions. However, limited by the budget as well as the available candidate group, current experiment design selects random subjective in an arbitrary scale, bringing a question that how they can stand for the whole studied population.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1525-1529"},"PeriodicalIF":19.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880628","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}
Dear Editor, This letter introduces a novel algorithm for privacy preservation designed to safeguard both the initial and real-time states of agents under complete distributed average consensus. It addresses a gap in existing privacy preservation approaches that predominantly focus on protecting the initial state, with limited consideration for privacy implications throughout the entire process. The algorithm ensures the privacy of both the initial and real-time states by introducing perturbations to the consensus process, allowing agents to freely define these perturbations themselves. Additionally, the perturbations defined by agents arbitrarily do not compromise the accuracy of the consensus result. One of the main results derived is that no agent has access to the real-time state of another agent.
{"title":"Average Consensus of Whole-Process Privacy Preservation","authors":"Lianghao Ji;Shaohong Tang;Xing Guo;Yan Xie","doi":"10.1109/JAS.2024.124731","DOIUrl":"https://doi.org/10.1109/JAS.2024.124731","url":null,"abstract":"Dear Editor, This letter introduces a novel algorithm for privacy preservation designed to safeguard both the initial and real-time states of agents under complete distributed average consensus. It addresses a gap in existing privacy preservation approaches that predominantly focus on protecting the initial state, with limited consideration for privacy implications throughout the entire process. The algorithm ensures the privacy of both the initial and real-time states by introducing perturbations to the consensus process, allowing agents to freely define these perturbations themselves. Additionally, the perturbations defined by agents arbitrarily do not compromise the accuracy of the consensus result. One of the main results derived is that no agent has access to the real-time state of another agent.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1727-1729"},"PeriodicalIF":19.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880626","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}
Industrial robots, as the fundamental component for intelligent manufacturing, have attracted considerable attention from both academia and industry. Since its absolute positioning accuracy can suffer from collision, wear, elastic, or inelastic deformation during its operation, a data-driven calibration (DDC) model has become a trending technique. It utilizes abundant data to decrease the difficulty in building complex system models, making it an economic and efficient approach to robot calibration. This paper conducts a comprehensive survey of the state-of-the-art DDC models with the following six-fold efforts: a) Summarizing the DDC modeling methods; b) Categorizing the latest progress of DDC optimization algorithms; c) Investigating the publicly available datasets and several typical metrics; d) Evaluating several widely adopted DDC models to demonstrate their calibration performance; e) Introducing the applications of the current DDC models; f) Discussing the progressing trend of DDC models. This paper strives to present a systematic and thorough overview of the existing DDC models from modeling to kinematic parameter optimization, thereby providing some guidance for research in this field.
{"title":"Data-Driven Calibration of Industrial Robots: A Comprehensive Survey","authors":"Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo","doi":"10.1109/JAS.2025.125237","DOIUrl":"https://doi.org/10.1109/JAS.2025.125237","url":null,"abstract":"Industrial robots, as the fundamental component for intelligent manufacturing, have attracted considerable attention from both academia and industry. Since its absolute positioning accuracy can suffer from collision, wear, elastic, or inelastic deformation during its operation, a data-driven calibration (DDC) model has become a trending technique. It utilizes abundant data to decrease the difficulty in building complex system models, making it an economic and efficient approach to robot calibration. This paper conducts a comprehensive survey of the state-of-the-art DDC models with the following six-fold efforts: a) Summarizing the DDC modeling methods; b) Categorizing the latest progress of DDC optimization algorithms; c) Investigating the publicly available datasets and several typical metrics; d) Evaluating several widely adopted DDC models to demonstrate their calibration performance; e) Introducing the applications of the current DDC models; f) Discussing the progressing trend of DDC models. This paper strives to present a systematic and thorough overview of the existing DDC models from modeling to kinematic parameter optimization, thereby providing some guidance for research in this field.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1544-1567"},"PeriodicalIF":19.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880576","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}
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}
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