IEEE Transactions on Automation Science and Engineering最新文献

{"title":"Linear-Parameterization-Based Model Free Adaptive Predictive Control","authors":"Juanping Zhu, Qiuyan Wei, Xian Yu, Zhongsheng Hou","doi":"10.1109/tase.2025.3613545","DOIUrl":"https://doi.org/10.1109/tase.2025.3613545","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"15 2 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127726","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}

{"title":"Adaptive Resilient Event-Triggered Control for T-S Fuzzy Multi-Area Power System With Pumped Storage Hydropower Under Deception Attacks","authors":"Congzhe Mu;Lin Gan;Tao Deng;Zhenzhen Zhang;Yaonan Shan;Hao Chen;Shouming Zhong","doi":"10.1109/TASE.2025.3613303","DOIUrl":"10.1109/TASE.2025.3613303","url":null,"abstract":"This paper investigates the load frequency control problem for nonlinear multi-area power system that incorporates pumped storage hydropower. First, a unified Takagi-Sugeno fuzzy model is developed to represent the nonlinear dynamics of power exchange progress in pumped storage hydropower, including uncertainties. Subsequently, a fuzzy logic algorithm is applied by incorporating both system error and its derivative to dynamically adjust the trigger threshold, an adaptive resilient event-triggered mechanism is proposed to reduce communication burden while improving control performance. Furthermore, a Lyapunov functional is constructed to ensure that the system is stable and strictly <inline-formula> <tex-math>$left ({{mathcal {D}_{1},mathcal {D}_{2},mathcal {D}_{3}}}right)-lambda -dissipative$ </tex-math></inline-formula>. Finally, a three-area power system is used to show the effectiveness of the proposed mechanism. Note to Practitioners—With the increasing application of pumped storage technology in power system, the problem of frequency regulation is investigated in this paper. Takagi-Sugeno fuzzy model is employed due to its linear substructure that enables direct application of Lyapunov-based methods for stability verification. Meanwhile, an adaptive resilient event-triggered control mechanism with fuzzy logic rules is proposed to balance data transmission requirements with control performance. Qualitative and quantitative analyses verify the proposed control strategy.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21748-21759"},"PeriodicalIF":6.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127725","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}

{"title":"MMFSeg: Multi-structure Multi-feature Fusion for Segmentation of Road Potholes","authors":"Zhen Feng, Yanning Guo, Rui Fan, Yuxiang Sun","doi":"10.1109/tase.2025.3613629","DOIUrl":"https://doi.org/10.1109/tase.2025.3613629","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"56 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127722","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}

{"title":"Safe Trajectory Generation for Nonholonomic Multi-Robot Systems: A Compensation-Based MPC Approach","authors":"Zhixu Du;Hao Zhang;Peiyu Cui;Zhuping Wang;Huaicheng Yan","doi":"10.1109/TASE.2025.3613403","DOIUrl":"10.1109/TASE.2025.3613403","url":null,"abstract":"This article investigates the collaborative trajectory generation problem for nonholonomic multi-robot systems using a compensation-based nonlinear model predictive control (MPC), where the system is subject to unknown uncertainties. A dynamic uncertainty estimator is designed for each robot to estimate the discrepancy between the predictive model and the actual system, enabling adaptive model corrections that enhance the robustness and adaptability of the MPC. By incorporating control Barrier function constraints, physical constraints, and stability constraints, the nonlinear MPC generates feasible, collision-free trajectories. These trajectories are further optimized using piecewise Bézier curves, yielding smoother and more efficient paths. Additionally, a dynamic safety-stability gain is introduced, allowing the MPC to adaptively balance safety and stability based on the system state and obstacle positions. The theoretical results are validated through simulations and experiments, demonstrating the effectiveness of the proposed approach. Note to Practitioners—The motivation of this article is to address the collaborative trajectory generation problem for multi-robot systems in environments with unknown uncertainties. Existing nonlinear MPC methods have two major limitations: 1) some schemes struggle to handle dynamic model uncertainties effectively, and 2) there may be adverse interactions between safety and stability components. To overcome these limitations, we propose a compensation-based nonlinear MPC framework that incorporates a dynamic uncertainty estimator for each robot. The estimator continuously measures the discrepancy between the predictive model and the actual system, adaptively updating the model to improve control accuracy. By incorporating control Barrier functions, physical, and stability constraints, the method generates feasible, collision-free trajectories. These trajectories are optimized using piecewise Bézier curves for smoother, more efficient paths. A dynamic safety-stability balancer adjusts constraints based on the system’s state and detected obstacles, relaxing stability when safety is critical and prioritizing progress when less urgent, thereby ensuring both safety and efficiency.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21831-21842"},"PeriodicalIF":6.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127724","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}

{"title":"Second-Order Sliding Mode Control of Flying-Wing Aircraft Based on Feedforward Neural Networks","authors":"Yuecheng Song;Zhenbao Liu;Junwei Han;Jinbiao Yuan;Wen Zhao;Qingqing Dang","doi":"10.1109/TASE.2025.3613383","DOIUrl":"10.1109/TASE.2025.3613383","url":null,"abstract":"The flying-wing aircraft control problem is a major concern. In this paper, a new control strategy is introduced. First, a Feedforward neural network (FNN) modeling is introduced. Then, a second-order sliding mode control is applied, with the parameters generated from Deep Deterministic Policy Gradient (DDPG) reinforcement learning. To study the disturbance rejection performance, wind disturbance is applied to the aircraft using a deep neural network as an disturbance observer for different types of winds. Finally, All three simulations: Simulink, Software In The Loop, and Hardware In the Loop are applied to show the effectiveness of the proposed strategy. The simulation results show that the proposed method demonstrates good robustness in various conditions. Note to Practitioners—This paper is motivated by the traditional linearized flying-wing aircraft controller with the effectiveness of the FNN and reinforcement learning on UAV applications. The controller can be designed for various situations without changing the parameters by modeling the aircraft through the FNNs. The theoretical framework proposed in this paper combines the multiple-input multiple-output (MIMO) sliding mode strategy with the reinforcement learning with higher accuracy modeling. This method reduces the settling time, overshoot and steady error. More realistic effects should be considered for deployment into real aircraft. The parameters of the neural networks should also be adjusted in real applications.The FNN networks and DDPG have low computational footprints and are readily deployable on embedded systems like Pixhawk. The DNN observer may require model compression for the smallest processors. While the current framework requires per-aircraft training to achieve optimal performance, this process is conducted offline. The resulting controller gains are then fixed for reliable real-time operation, providing a clear pathway for implementation on specific UAV platforms.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21811-21830"},"PeriodicalIF":6.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127721","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}

{"title":"Optimizing Technician Staffing and Scheduling in Medical Procedure Services Using Two-Stage Stochastic Integer Programming","authors":"Mirui Zhang, Narges Shahraki, Feifan Wang","doi":"10.1109/tase.2025.3612390","DOIUrl":"https://doi.org/10.1109/tase.2025.3612390","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"59 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116525","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}

{"title":"Adaptive Prescribed-Time Target Capture Control for Marine Vehicles With Game-Based Navigation Risk Field","authors":"Shengjia Chu, Ning Li","doi":"10.1109/tase.2025.3612430","DOIUrl":"https://doi.org/10.1109/tase.2025.3612430","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"28 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116527","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}

{"title":"ADP: Adaptive Diffusion Policy Energizes Robots Thinking in Both Learning and Practice","authors":"Dechao Chen;Zhengwen Chen;Xiangyan Zheng;Weiling Xu;Chencong Ma;Chentao Mao","doi":"10.1109/TASE.2025.3612396","DOIUrl":"10.1109/TASE.2025.3612396","url":null,"abstract":"Adaptive control policies for robots often require balancing generalization from large offline datasets with efficient adaptation to specific deployment conditions. In this paper, we propose Adaptive Diffusion Policy (ADP), a two-stage framework that integrates temporal-aware diffusion models with parameter-efficient LoRA adaptation. First, in the learning stage, ADP imitates and generates actions based on image and video signals from a meager amount of expert demonstrations, considering both spatial and temporal information. This component contrasts with most existing works, which focus solely on spatial information. Second, in the practice stage, ADP incorporates a low-rank adaptation module into the policy, subsequently training it using residual reinforcement learning with minimal environment interaction. Experiments conducted on Meta-World benchmark demonstrate the efficiency of each ADP component and the superiority of ADP over representative baseline methods. Note to Practitioners—This work introduces Adaptive Diffusion Policy (ADP), a two-stage visuomotor framework that first learns from just a few image-and-video demonstrations by modeling both spatial and temporal cues, then rapidly refines its behavior via a lightweight low-rank adapter and residual reinforcement learning. The ADP enables swift skill acquisition on new tasks with minimal expert data and limited environment trials, making it ideal for industrial or household robots where extensive data collection is impractical. To apply ADP, collect a small set of demonstration clips, train the diffusion-based policy offline, and deploy the adapter online for in situ fine-tuning. The proposed Meta-World results show the ADP’s consistent gains over standard imitation and residual RL baselines, which is very easy for practitioners in multiple real-world robot scenarios.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21585-21594"},"PeriodicalIF":6.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127729","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}

{"title":"Joint Optimization of Order Sequencing and Temporary Rack Shelving for Separated Bin-Picking Systems","authors":"Meimei Zheng;Zhenqi Xu;Edward Huang;Tangbin Xia;Kan Wu","doi":"10.1109/TASE.2025.3613008","DOIUrl":"10.1109/TASE.2025.3613008","url":null,"abstract":"As the adoption of warehouse automation continues its upward trajectory, “parts-to-picker” order picking systems, epitomized by the Robotic Mobile Fulfillment System (RMFS), are increasingly deployed across diverse enterprises. Considering that current automation systems are economically unfriendly to the traditional warehouse transformation, and other shortcomings such as low space utilization, a separated bin-picking system has been developed and adopted in companies. The key improvement compared to RMFS is the adoption of a novel Automated Guided Vehicle (AGV) equipped with temporary racks, which can pick material bins from traditional racks through a grabbing device without redesigning and alterations of the fixed racks. However, research on related decision-making and optimization is still lacking. To fill this gap, we explore the joint optimization of order sequencing and temporary rack shelving and formulate a mixed integer programming model, taking into account the SKU-based (Stock Keeping Unit) workload balancing among multiple picking stations. To solve the model, we propose an interactive order driven heuristic combined neighborhood search algorithm. Based on the real data from an auto-parts distribution center, the case study is conducted to demonstrate the effectiveness of the proposed method. The proposed method can reduce the number of rack movements by 15.39% and improve the utilization rate of temporary racks by 27.49% compared to the two-step method typically used in practice. Sensitivity analysis is also performed to provide valuable managerial insights for the operational improvement of the separated bin-picking system. Note to Practitioners—This paper is inspired by an emerging “parts-to-picker” order picking system, which can more effectively reduce retrofit costs of warehouses and improve efficiency compared to currently used Automated Storage and Retrieval System (AS/RS) and Robotic Mobile Fulfillment System (RMFS). However, although the system has been applied in practice, there lacks research on related decision-making and optimization at the operational level. In this paper, we investigate the joint optimization problem of order sequencing and temporary rack shelving, considering the SKU-based workload balancing among multiple picking stations. A mixed integer programming model is formulated. Then, to deal with large-scale cases, an interactive order driven heuristic combined neighborhood search algorithm is proposed to improve the computation efficiency. Practitioners can implement the proposed method and algorithm for routine order processing of automated warehouses that use separated bin-picking systems. Based on the sensitivity analysis, we also present some managerial insights, which can provide guidance for practitioners when implementing the operational improvement of the system. For instance, practitioners could prioritize increasing the capacity of temporary racks over that of picking stations if they would lik","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21728-21747"},"PeriodicalIF":6.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116531","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}

{"title":"Koopman-Based Fractional Predefined-Time Control for Wearable Exoskeleton System","authors":"Xianlei Zhang;Yan Zhang;Qing Hu;Zongyu Han;Yixin Yang;Xilong Yu","doi":"10.1109/TASE.2025.3612902","DOIUrl":"10.1109/TASE.2025.3612902","url":null,"abstract":"In this paper, an innovative fractional predefined-time control scheme based on the Koopman operator is proposed for the wearable exoskeleton system (WES). Leveraging the nonlinear mapping of the Koopman operator and the prior information of the exoskeleton system, a prior Koopman model (PIKOM) is devised. As a data-driven model, the PIKOM can be constructed through measuring input/output (I/O) data, avoiding complex dynamic modeling. Besides, the integer-order PIKOM is broadened into a fractional-order form, and the tanh function is adopted to handle the problem of saturation constraint. The fractional predefined-time controller is meticulously designed to enhance the tracking performance of the WES. The tracking error converges to a narrow range near the origin within a predefined time. The experimental results verify the effectiveness and superiority of the proposed approach. Compared with state-of-the-art (SOTA) methods (Schemes 1-4), the proposed method achieves improvements of 12.2%, 18.2%, 8.8%, and 20.7%, respectively. Note to Practitioners—In recent years, the trajectory tracking of the WES has garnered significant and escalating research interests. Owing to the complicated structure of the WES, dynamic model-based control methods generally fail to realize satisfactory control performance. A PIKOM that only embraces I/O data is constructed to avoid this problem. Meanwhile, the PIKOM is converted into a fractional form because of the outstanding advantages of fractional-order control (FOC). With the rapid development of automatic control technology, there is a growing demand for the convergence rate of the WES. The asymptotic FOC method is no longer applicable. In this paper, a PIKOM-based fractional predefined-time control is presented to achieve trajectory tracking. In addition to inheriting the merits of fixed-time control strategies, this method exhibits a characteristic that the upper bound of convergence time is solely determined by a single parameter. The control performance of the proposed scheme is demonstrated by experimental results.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21595-21608"},"PeriodicalIF":6.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116529","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}