IEEE Transactions on Automation Science and Engineering最新文献_第4页

A Self-Organizing Federated Reinforcement Learning Framework for Cross-Scenario Collaborative AVP Trajectory Planning 跨场景协同AVP轨迹规划的自组织联邦强化学习框架

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-10-05 DOI: 10.1109/tase.2026.3740234

Xinhao Liu, Xiansheng Guo, Gordon Owusu Boateng, Haonan Si, Yu Cao, Yinong Liu, Bocheng Qian, Yu Wang, Kehan Chen, Lei Huang, Nirwan Ansari

引用次数: 0

Neural-Adaptive Fault-Tolerant Control for Resilient Robot Manipulation Under Actuator Failures 执行器故障下弹性机器人操作的神经自适应容错控制

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-10-01 DOI: 10.1109/tase.2026.3738776

Tawei Chou, Fengshuo Bai, Yuwan Liu, Yufeng Li, Yaodong Yang, Xiaoshan Ma

引用次数: 0

Enhancing Vehicle Trajectory Prediction Under Sensor Degradation Through Masked Autoencoder Pretraining 通过蒙面自编码器预训练增强传感器退化下的车辆轨迹预测

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-09-29 DOI: 10.1109/tase.2026.3738234

Penglong Li, You Ren, Yanran Liu, Jintao Wan, Shangke Li, Hongfei Liu

引用次数: 0

Executable Multi-Agent Path Finding with Practical Hardware Constraints 具有实际硬件约束的可执行多代理寻径

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-09-11 DOI: 10.1109/tase.2026.3733001

Changhyun Chung, Young Jae Jang

引用次数: 0

Hybrid CNN and Transformer Encoder Model for Localization of Forced Oscillations in Modern Power Systems 现代电力系统强迫振荡局部化的混合CNN和变压器编码器模型

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-08-24 DOI: 10.1109/tase.2026.3726932

Priya Singh, Abhineet Prakash, Aritra Roy, S. K. Parida

引用次数: 0

A Lightweight Digital Twin-Based Control Framework for Power Maximization in Waked Wind Farm 风电场功率最大化的轻量化双数字控制框架

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-03-30 DOI: 10.1109/tase.2026.3672940

Pengda Wang, Jiani Mao, Sheng Huang, Jinxin Xiao

引用次数: 0

TGSA: Text-Guided Semantic Alignment for Tiny Object Detection TGSA：用于微小目标检测的文本引导语义对齐

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-03-09 DOI: 10.1109/tase.2026.3671786

Jiaping Li, Wanying Cao, Zhongtian He, Hua Yang

引用次数: 0

Contraction-Relaxation Behavior Inspired Fractional-Order Control for Physical Human–Robot Interaction With Optimized Order 基于收缩-松弛行为的分数阶人机交互优化控制

IF 6.4 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-03-05 DOI: 10.1109/TASE.2026.3669621

Junhui Liu;Zhiqiang Ma;Hanlin Dong;Panfeng Huang

{"title":"Contraction-Relaxation Behavior Inspired Fractional-Order Control for Physical Human–Robot Interaction With Optimized Order","authors":"Junhui Liu;Zhiqiang Ma;Hanlin Dong;Panfeng Huang","doi":"10.1109/TASE.2026.3669621","DOIUrl":"10.1109/TASE.2026.3669621","url":null,"abstract":"This paper proposes a fractional-order control method for the physical human–robot interaction (pHRI) inspired by the dynamics of muscle contraction-relaxation behavior, which is integrated into a fractional-order sliding mode with variable order and parameters, to achieve fast transient response while guaranteeing high-precision steady-state tracking performance. A sensorless force observer based on fractional calculus is developed to estimate the operator’s behavior, enabling a composite control system that guarantees ultimate boundedness of the closed-loop signals. The deep reinforcement learning-based order and parameter optimization mechanism is synthesized into the unified architecture of the composite control system. The effectiveness of the proposed method is validated through numerical simulations and comparative studies, which demonstrate significant improvements in convergence speed on the sliding manifold while maintaining steady-state precision. Experimental results further confirm the feasibility and practical applicability of the framework in a cylindrical docking scenario via visual-reality fusion approach, highlighting its potential for future semi-autonomous human-in-loop missions. Note to Practitioners—This paper was motivated by the problem that the pHRI exhibits significantly rigid motion characteristics, which lack human-like compliance, since the robotic controller design has not taken the laws of human interactive behavior into account. Existing approaches to stabilizing the pHRI generally isolate the robotic controller from the operator, and aim to track a reference trajectory generated from an impedance or admittance structure with the operator’s force injected. Hence, the compliant performance of pHRI is dominated by the dynamic response and steady-state performance of the control method, which are challenging to balance with a fixed set of control parameters. This paper suggests a new fractional-order sliding manifold comprising a fractional element and a nonlinear spring to express viscoelastic and elastic movement of the muscle, capturing the time-dependent and dynamic characteristics of contraction-relaxation behavior. The order and control parameters of the proposed composite control system with sensorless force estimation are integrated into a Markov decision process to generate an optimization strategy that accounts for long-horizon compliant performance of pHRI via deep reinforcement learning. In this paper, we mathematically characterize the mechanics of contraction-relaxation behavior for synthesizing the fractional-order control method with order optimization. We then show its effectiveness in simulations. The experimental results suggest that the proposed method is feasible for conducting space haptic operation with the order and parameter strategy.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5897-5913"},"PeriodicalIF":6.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Real-Time Deep Reinforcement Learning Policy Controller for Magnetically Levitated Robots 磁悬浮机器人实时深度强化学习策略控制器

IF 6.4 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-03-05 DOI: 10.1109/TASE.2026.3671162

Yang Wang;Yanze Li;Sebastian Viancha;Mir Behrad Khamesee

{"title":"Real-Time Deep Reinforcement Learning Policy Controller for Magnetically Levitated Robots","authors":"Yang Wang;Yanze Li;Sebastian Viancha;Mir Behrad Khamesee","doi":"10.1109/TASE.2026.3671162","DOIUrl":"10.1109/TASE.2026.3671162","url":null,"abstract":"Magnetic levitation (Maglev) technology is widely adopted for transportation, photolithography, clean room, and material-handling applications, owing to its vibration- and friction-free, untethered motion. The magnetic movers are actuated using the stator coil array, which functions as mobile maglev robots (MLRs) with precision position and orientation motion control. Therefore, this article studies the MLRs for intelligent manufacturing, such as material handling and collaboration with manufacturing components, by integrating them with deep reinforcement learning (DRL) policy controllers. Linear DRL policies are developed and compared between Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) for the horizontal motion control, which takes <inline-formula> <tex-math>$mathrm {455~mu text {s} }$ </tex-math></inline-formula> to compute the actuating forces for real-time operations. During policy training, random magnitude horizontal step responses are ticked every 500 ms to explore and exploit the dynamic capacity of the MLRs. The PPO policies are finalized based on the discovered deterministic nature of the maglev environment. Furthermore, the symmetry of the MLR configuration is utilized for policy training, with a threshold boundary included to enable MLRs to translate smoothly across the horizontal operating range. The trained policies exhibit aggressive oscillations and persistent offsets relative to References, respectively. To alleviate the fitting problem, distance-based soft state machines are introduced to integrate the DRL policy with the basic controller. The state machine parameters are empirically tuned using various location step responses. This feature enables the dynamic responses of the DRL policy to be combined with the precision responses at references. The soft state machines are also trained as auxiliary policy actions and evaluated using full operating-range trajectory tracking and long-stroke step responses alongside the policy and the basic controller. The DRL policy controllers outperform the basic controllers, with improved rise and settling times in step responses, while showing enhanced performance in tracking trajectories. Finally, the policy robustness is empirically evaluated. Note to Practitioners—This article is first motivated by the need for dynamic responses in long-stroke magnetically levitated (maglev) robots in manufacturing operations. The reported basic controllers are fine-tuned locally and implemented in the large horizontal operating space. Utilizing the patterns of the maglev robots (MLRs), a controller that provides dynamic responses based on the operating conditions is desired. Then, a secondary motivation for deep reinforcement learning (DRL) in smart manufacturing systems (SMSs) is investigated to obtain a policy controller using artificial intelligence. In addition to dynamic performance, achieving precise control with a DRL policy is challenging owing to the policy-fitting problem durin","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5929-5939"},"PeriodicalIF":6.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Noncooperative Game-Based Probability Assignment Formation Control for Networked Uncrewed Surface Vehicles Under Stochastic Packet Losses 随机丢包情况下基于非合作博弈的网络无人水面车辆概率分配编队控制

IF 6.4 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2026-03-02 DOI: 10.1109/TASE.2026.3669734

Bing Huang;Hua Peng;Choon Ki Ahn

{"title":"Noncooperative Game-Based Probability Assignment Formation Control for Networked Uncrewed Surface Vehicles Under Stochastic Packet Losses","authors":"Bing Huang;Hua Peng;Choon Ki Ahn","doi":"10.1109/TASE.2026.3669734","DOIUrl":"10.1109/TASE.2026.3669734","url":null,"abstract":"Enhanced formation flexibility stringently commands on the connection stability of networked systems, while vehicle-equipped facilities limit interaction coherence. Either a prolonged communication range or a malicious attack leaves the networked system suffering from disconnections. Functioned as randomly occurring transmission packet dropouts, we consider the noncooperative game played by networked uncrewed surface vehicles (NUSVs), where game theory enables NUSVs’ active mission assessment and thereby enhances flexibility. For loss rejection, a probability assignment event-triggered mechanism (PAETM) is explored under two cases, i.e., loss occurrence obeying a Bernoulli distribution and irregular occurrence. To begin with, PAETM is first proposed for the former. Assigned with specifically designed weights, historically triggered packets are employed to manage individual broadcast timing. By virtue of PAETM, neither explicit nor implicit loss acknowledgment is required, thus meriting acknowledgment-free. In this sequel, irregular loss occurrence, which is more generic, is considered by decomposing it into a piece-wise regular manner. Theoretical analysis and semi-physical simulations are performed to show the effectiveness and superiority of the proposed algorithm. Note to Practitioners—Formation control completed by NUSVs significantly contributes to marine applications. For great environment adaptability, NUSVs prefer to form the desired configuration in a flexible manner, including saved energy and reduced convergence duration. Nevertheless, most formation protocols are designed through the tracking manner, where individual character and autonomous evaluation are neglected. As a solution, the noncooperative game played by NUSVs endows each vehicle with self-adjustable capability. Furthermore, vehicle-equipped communication facilities face unexpected interaction failures when suffering from potential electromagnetic interference and terrain blocking, especially for a flexibility-enhanced system. To prevent such damage, a probability-assigned mechanism is developed in this paper. Thus, both regularly and irregularly occurring inter-vehicle packet dropouts are formulated and tackled in an acknowledgment-free manner.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5914-5928"},"PeriodicalIF":6.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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