Ieee-Caa Journal of Automatica Sinica最新文献

Parallel Experiments: From The Human Participated to A Virtual-Real Hybrid Paradigm 平行实验：从人参与到虚实混合范式

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-08-20 DOI: 10.1109/JAS.2025.125474

Peijun Ye;Xiao Xue;Qinghua Ni;Jing Yang;Fei-Yue Wang

引用次数: 0

Average Consensus of Whole-Process Privacy Preservation 全程隐私保护的平均共识

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-08-20 DOI: 10.1109/JAS.2024.124731

Lianghao Ji;Shaohong Tang;Xing Guo;Yan Xie

引用次数: 0

Data-Driven Calibration of Industrial Robots: A Comprehensive Survey 数据驱动的工业机器人标定：综合调查

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-08-20 DOI: 10.1109/JAS.2025.125237

Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo

{"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}

引用次数: 0

Autonomous Drug Discovery with Parallel Intelligence 并行智能的自主药物发现

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-08-20 DOI: 10.1109/JAS.2025.125426

Fei Lin;Jing Yang;Dali Sun;Levente Kovács;Fei-Yue Wang

引用次数: 0

Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey 基于机器学习的抑郁症预测：通过各种数据模式：一项调查

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2025.125393

Qiong Li;Xiaotong Liu;Xuecai Hu;Md Atiqur Rahman Ahad;Min Ren;Li Yao;Yongzhen Huang

{"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}

引用次数: 0

Nash Bargaining Solution-Based Multi-Objective Model Predictive Control for Constrained Interactive Robots 基于Nash议价解的约束交互机器人多目标模型预测控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2024.124398

Minglei Zhu;Jun Qi

引用次数: 0

Hybrid Event-Triggered Control with Stability Analysis 混合事件触发控制与稳定性分析

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2024.125067

Ding Wang;Lingzhi Hu;Junfei Qiao

{"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}

引用次数: 0

Hierarchical Secure Steering Control of In-Wheel Motor Driven Electric Vehicle Under Cyber-Physical Constraints 网络物理约束下轮毂电机驱动电动汽车分层安全转向控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2023.124092

Zifan Gao;Dawei Zhang;Shuqian Zhu

引用次数: 0

Prescribed Performance Control of Nonlinear Systems With Unknown Sign-Switching Virtual Control Coefficients 未知符号切换虚拟控制系数非线性系统的规定性能控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2025.125135

Jin-Zi Yang;Jin-Xi Zhang;Tianyou Chai

{"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}

引用次数: 0

Federated Experiments: Generative Causal Inference Powered by LLM-Based Agents Simulation and RAG-Based Domain Docking 联邦实验：基于llm的agent仿真和基于rag的领域对接驱动的生成因果推理

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.124671

De-Yu Zhou;Xiao Xue;Qun Ma;Chao Guo;Li-Zhen Cui;Yong-Lin Tian;Jing Yang;Fei-Yue Wang

引用次数: 0