IEEE Transactions on Automation Science and Engineering最新文献

{"title":"TGSA: Text-Guided Semantic Alignment for Tiny Object Detection","authors":"Jiaping Li, Wanying Cao, Zhongtian He, Hua Yang","doi":"10.1109/tase.2026.3671786","DOIUrl":"https://doi.org/10.1109/tase.2026.3671786","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"35 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380812","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":"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}

{"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}

{"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}

{"title":"A Robust and Compliant Robotic Assembly Control Strategy for Batch Precision Assembly Task","authors":"Bin Wang;Jiwen Zhang;Song Wang;Dan Wu","doi":"10.1109/TASE.2026.3669249","DOIUrl":"10.1109/TASE.2026.3669249","url":null,"abstract":"In many high-precision industrial applications, robots are deployed to perform precision peg-in-hole assembly on large batches of manufactured pegs and holes. When the nominal design adopts a transition fit, machining errors may cause each peg–hole pair to exhibit either a clearance fit or a slight interference fit, with an unknown and continuously varying fit amount. This paper addresses robotic batch precision assembly tasks under uncertain fit types and fit amounts, and proposes a novel multi-stage framework for systematically constructing a robust assembly control strategy. The overall batch precision assembly task is first decomposed into multiple deterministic subtasks characterized by different but fixed fit amounts. For these subtasks, a force-vision fusion controller-driven reinforcement learning method combined with a multi-task reinforcement learning training framework (FVFC-MTRL) is developed to jointly learn multiple compliance control strategies. Subsequently, a multi-teacher policy distillation scheme is designed to integrate multiple trained strategies into a single student strategy, yielding a robust control strategy. Real-world experiments demonstrate that the proposed method successfully constructs a robust control strategy for high-precision assembly tasks with varying fit types and fit amounts. Moreover, the MTRL framework significantly improves training efficiency by 50%, and the final control strategy achieves superior force compliance and a higher success rate compared with several existing methods. Note to Practitioners—Unlike most existing studies that learn an assembly strategy for a specific peg–hole pair with a known clearance fit, this work addresses the challenges of control robustness and compliance in robotic batch precision assembly with uncertain fit types and fit amounts, which is closer to practical industrial production settings. Reinforcement learning is combined with a pre-designed force-vision fusion controller to enhance both training stability and control performance. Furthermore, a multi-stage framework, consisting of task decomposition, multi-task reinforcement learning, and policy distillation is developed to endow the control policy with robustness. The final constructed control strategy can be directly deployed in industrial batch precision assembly tasks.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5502-5515"},"PeriodicalIF":6.4,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350660","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":"Automated Action Generation Based on Action Field for Robotic Garment Smoothing and Alignment","authors":"Hu Cheng;Fuyuki Tokuda;Kazuhiro Kosuge","doi":"10.1109/TASE.2026.3667879","DOIUrl":"10.1109/TASE.2026.3667879","url":null,"abstract":"Garment manipulation using robotic systems is a challenging task due to the diverse shapes and deformable nature of fabric. In this paper, we propose a novel method for robotic garment smoothing and alignment that significantly improves the accuracy while reducing computational time compared to previous approaches. Our method features an action generator that directly interprets scene images and generates pixel-wise end-effector action vectors using a neural network. The network also predicts a manipulation score map that ranks potential actions, allowing the system to select the most effective action. Extensive simulation experiments demonstrate that our method achieves higher smoothing and alignment performances and faster computation time than previous approaches. Real-world experiments show that the proposed method generalizes well to different garment types and successfully flattens garments. Note to Practitioners—Vision-based robotic garment manipulation faces significant complexities due to garments’ diverse shapes and high-dimensional states, which pose challenges for both state perception and action generation. In this paper, we propose a novel deep neural network that can generate actions to smooth various garments from their RGB images. Compared to existing methods, our method generates pixel-level actions across the entire garment area, each providing a predicted manipulation score that assists in the selection of a final manipulation action. In addition, the generation requires only a single-shot network forward computation, which significantly improves efficiency. The training data consists of large-scale recorded garment state parameters and the corresponding manipulating actions in the simulator. Real-world experiments demonstrate the effectiveness and generalization capability of our model.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5884-5896"},"PeriodicalIF":6.4,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319562","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":"Reinforcement Learning-Based Distributed Secondary Frequency Control and Active Power Sharing in Islanded Microgrids With Bandwidth-Conscious Memory-Event-Triggered Mechanism","authors":"Shen Yan;Zehao Dou;Zhou Gu","doi":"10.1109/TASE.2026.3668833","DOIUrl":"10.1109/TASE.2026.3668833","url":null,"abstract":"This paper studies the reinforcement learning-based distributed secondary frequency control and active power allocation of islanded microgrids under event-triggered mechanism. First, a novel bandwidth-conscious memory-event-triggered mechanism is proposed to reduce communication and computation burdens, which introduces the mean of memory signals to smooth the measured outputs perturbed by disturbances and noises. Meanwhile, a dynamic triggering threshold depending on real-time bandwidth status is constructed to adaptively adjust the data transmission rate according to the changes in bandwidth status and system responses. Second, a new reinforcement learning-based distributed secondary controller using Q-learning algorithm is presented to dynamically regulate the frequency controller gains in response to complex system environments, which helps in achieving better frequency restoration performance. Third, the system stability is analyzed by some linear matrix inequality conditions. Then, simulation outcomes based on load variation and plug-and-play test illustrate that our strategy achieves satisfactory performance in frequency restoration and active power sharing. In addition, some comparison results show the merits of the constructed event-triggered scheme and Q-learning-based distributed secondary frequency controller. Note to Practitioners—This article is concerned with the reinforcement learning-based distributed secondary frequency control and active power allocation of islanded microgrids, addressing challenges induced by limited communication resources, dynamic bandwidth status and complex operation environment. The innovative bandwidth-conscious memory-event-triggered scheme increases the usage efficiency of communication network, and the reinforcement learning-based distributed secondary frequency controller optimizes the frequency restoration performance for islanded microgrids under load changes and random plug and unplug behaviors. The developed strategy is able to handle these realistic problems in practical systems. This strategy can provide technical support for electrical engineers to enhance the stability performance of islanded microgrids.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5450-5459"},"PeriodicalIF":6.4,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319563","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":"Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments","authors":"Yicheng Chen;Jinjie Li;Haokun Liu;Zicheng Luo;Kotaro Kaneko;Moju Zhao","doi":"10.1109/TASE.2026.3669051","DOIUrl":"10.1109/TASE.2026.3669051","url":null,"abstract":"Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high-dimensional, constraint-rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization. First, we exploit the dual nature of these robots—the root link as a rigid body for guidance and the articulated joints for flexibility—to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point-cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real-world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot to generate continuous, collision-free, and dynamically feasible trajectories directly from raw point-cloud inputs, without relying on handcrafted obstacle models. Note to Practitioners—Robots that can change their shape while moving have strong potential for tasks such as inspection, search and rescue, and exploration in spaces that are too tight for conventional drones. The key challenge is coordinating shape changes with safe and efficient navigation in cluttered environments. Our work addresses this by introducing a planning method that generates feasible trajectories considering both flight and configuration changes. For practitioners, the main benefit is improved reliability when operating in confined or unpredictable spaces: the robot can adapt its body to clear obstacles and complete maneuvering that would be impossible for rigid robots. The framework also reduces the need for manual modeling of environments by working directly with standard 3D sensor data. While these capabilities represent a meaningful advance, the current system still relies on accurate sensing and controlled test conditions. Adapting it to real-world uncertainty and longer, more complex missions remains an open direction. Future advances may enable wider deployment in industrial inspection, infrastructure maintenance, or hazardous-area exploration, as well as inspire similar solutions for other transformable robotic platforms.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5460-5477"},"PeriodicalIF":6.4,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319909","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":"Lie Algebra-Based Probabilistic Shape Estimation of Flexible Continuum Robots From Sparse Pose Measurements","authors":"Zhouhui Yan;Zhen Deng;Guotao Li;Jianwei Zhang","doi":"10.1109/TASE.2026.3668227","DOIUrl":"10.1109/TASE.2026.3668227","url":null,"abstract":"Estimating the three-dimensional (3D) shape of tendon-driven continuum robots (TDCRs) under sparse pose measurements and unknown tip forces remains a significant challenge. This paper presents a novel Lie algebra-based probabilistic framework for continuous-arclength shape reconstruction of variable-curvature TDCRs subjected to unknown tip loads. By unifying Cosserat-rod and Cosserat-string theories, a Lie algebra-coupled static model is established that explicitly represents tendon actuation as distributed wrenches along the robot’s backbone. A sparse Gaussian Process prior in <inline-formula> <tex-math>$mathfrak {se}(3)$ </tex-math></inline-formula> is derived via linearization to encode actuation-coupled shape distributions. This prior is fused with sparse, noisy pose measurements from electromagnetic sensors in a continuous-arclength batch estimation framework, enabling uncertainty-aware reconstruction along the backbone. Simulations and experiments on single-segment prototypes under diverse tip-loading conditions validate the method’s efficacy. Results demonstrate that the proposed approach achieves accurate shape estimation, with average shape (tip) errors of 1.6% (2.5%) of the total length, surpassing state-of-the-art methods. Comparable precision on helical-routing TDCR further confirms the framework’s generalizability. Note to Practitioners—This work tackles the practical problem of accurately estimating the 3D shape of continuum robots during real-world operation, such as when navigating through cluttered spaces or physically interacting with tissues. Current approaches often perform poorly under tip force conditions or rely on expensive, densely distributed sensors that are challenging to implement. We introduce a robust shape-sensing method that integrates a mechanics-based model of the robot with sparse sensor measurements to reconstruct its complete 3D shape. A key advantage of our model is its explicit consideration of both tendon actuation and external contact forces, enabling reliable shape estimation even during physical interaction. Furthermore, the framework quantifies estimation uncertainty along the robot’s entire length, supporting risk-aware control in safety-critical applications. The proposed approach significantly enhances the spatial awareness of continuum robots, improving their effectiveness in domains such as minimally invasive surgery and search-and-rescue operations.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5436-5449"},"PeriodicalIF":6.4,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319852","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":"Dual-Layer Bumpless Transfer Control for Markovian Jump Systems and Its Applications: A Weighted Coefficient Mechanism","authors":"Qingchuan Feng;Dan Ma;Dong Yang","doi":"10.1109/TASE.2026.3666949","DOIUrl":"10.1109/TASE.2026.3666949","url":null,"abstract":"This paper addresses a dual-layer bumpless transfer control (DLBTC) problem for Markovian jump systems (MJSs), aiming to achieve smooth transitions during stochastic mode switchings. First, the design of a multi-controller structure necessitates a system mode-dependent Markov chain, which enriches system dynamics and enhances robustness. However, it design inevitably increases stochastic mode switchings and aggravates bump phenomena. Second, to suppress these bumps, a dual-layer bumpless transfer control strategy is developed that constrains both input smoothness and rate continuity. Meanwhile, the traditional reference matrix mechanism is replaced by a weighted coefficient mechanism to improve adaptability across operating scenarios. Then, sufficient conditions on stochastic stabilization and DLBTC performance of MJSs are established via linear matrix inequalities. Finally, the impact of multi-controller structure and the proposed DLBTC on stochastic stabilization and transient behavior is demonstrated through an RLC circuit and a 40K engine control system as case studies. Note to Practitioners—The proposed DLBTC strategy targets critical challenges in MJSs where abrupt transient degradation arises from mode transitions. By integrating constraints on both input smoothness and rate continuity within a unified framework, the method reduces discontinuities in input and rate signals during stochastic switching. This dual-layer coordination preserves dynamic responsiveness while promoting smooth transitions, which is essential for systems with stringent precision requirements. The weighted-coefficient mechanism substitutes the rigid reference-matrix approach, enabling flexible tuning and reducing dependency on precise parameter identification. Practical applicability is shown for circuit systems and aerospace applications. Additionally, practitioners should be mindful of the trade-off between the number of controllers and system performance, as well as the trade-off among multiple performance metrics.he proposed DLBTC strategy targets critical challenges in MJSs where abrupt transient degradation arises from mode transitions. By integrating constraints on both input smoothness and rate continuity within a unified framework, the method reduces discontinuities in input and rate signals during stochastic switching. This dual-layer coordination preserves dynamic responsiveness while promoting smooth transitions, which is essential for systems with stringent precision requirements. The weighted-coefficient mechanism substitutes the rigid reference matrix approach, enabling flexible tuning and reducing dependency on precise parameter identification. Practical applicability is shown for circuit systems and aerospace applications. Additionally, practitioners should be mindful of the trade-off between the number of controllers and system performance, as well as the trade-off among multiple performance metrics.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"5413-5423"},"PeriodicalIF":6.4,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287359","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}