IEEE Transactions on Systems Man Cybernetics-Systems最新文献

{"title":"Iterative Algorithms for Set Stabilization of Probabilistic Boolean Control Networks and Applications in State-Based Games","authors":"Wen Liu;Jun-e Feng;Renato De Leone;Shihua Fu;Jianwei Xia","doi":"10.1109/TSMC.2025.3580175","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3580175","url":null,"abstract":"A probabilistic Boolean control network (PBCN) is a binary discrete-time system that utilizes probability values. An important application of PBCN is in gene regulatory network (GRN), providing a more flexible model, and lay a foundation for the study of gene interactions in complex environments. In this research, the set stabilization with probability 1 of PBCNs is investigated. Utilizing semi-tensor product (STP) of matrices, an iterative algorithm for calculating the largest control invariant subset of PBCNs is established and a necessary and sufficient condition for set stabilization with probability 1 is provided. Moreover, we design an algorithm for the stabilization controller to achieve stabilization of the system in the shortest time. As an application, the results of PBCNs are used to solve the stabilization problems at recurrent state equilibriums (RSEs) of state-based games. Examples are given to verify the feasibility of the proposed method and results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7048-7059"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100335","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}

{"title":"Supervisor Design of Unbounded Petri Nets for Discrete Event Systems","authors":"Yanan Zhang;Yufeng Chen;Ahmed M. El-Sherbeeny;Zhiwu Li","doi":"10.1109/TSMC.2025.3580434","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3580434","url":null,"abstract":"A deadlock prevention policy for discrete event systems (DESs) modeled as unbounded Petri nets (UPNs) is developed in this article. A reachability tree, an essential instrument, shows the evolution of UPNs is fundamental for the description and analysis of several characteristics, including liveness and reversibility. Deadlocks are an undesirable situation within the realm of DESs, particularly those represented by Petri nets. This article introduces a method implemented through integer linear programming for UPNs. We explore the modified reachability trees of UPNs from a new lens by defining two classes of circuits, and exploit an algorithm to engineer a supervisor that ensures liveness and maximum permissiveness. The proposed approach can supervise and control all deadlock nodes in a reachability tree such that the closed-loop unbounded net is live. Finally, several typical cases are shown to demonstrate the reported methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6611-6620"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100343","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}

{"title":"Asynchronous Consensus Evolution Mechanism for Large Group Emergency Decision Making: Risk Mitigation Strategy Selection Under Uncertainty","authors":"Ya-Jing Zhou;Mi Zhou;Jian Wu;Witold Pedrycz;Xin-Bao Liu","doi":"10.1109/TSMC.2025.3580657","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3580657","url":null,"abstract":"Supply chain disruptions pose substantial risks to the system-on-chip supply chain (SoCSC) within the electric vehicle (EV) industry, potentially resulting in production delays and financial losses. This study proposes a novel asynchronous consensus evolution mechanism (ACEM) designed to enhance large group emergency decision-making (LGEDM) under uncertainty, with specific application to the EV SoCSC. Unlike traditional synchronous approaches, ACEM enables decision makers (DMs) to contribute asynchronously, reducing wait times and accelerating consensus formation. The mechanism integrates uncertain scenario analysis with an optimization framework that dynamically allocates decision steps with relative weights, ensuring adaptability to complex and dynamic environments. We further develop a time-aware adaptive clustering (TAAC) algorithm to segment DMs based on decision quality and response speed, enhancing both the speed and the accuracy of consensus building. Simulation results indicate that ACEM significantly reduces decision latency and improves consensus efficiency under uncertain disruption scenarios. This work provides a robust framework for agile decision-making, enabling manufacturers to enhance SoCSC resilience in uncertain disruptions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6753-6766"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100381","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}

{"title":"Equilibrium Torque Control-Based Lower-Limb Exoskeleton Assistance With Memory-Enhanced Gait Prediction and Real-Time Learning","authors":"Wenlong Li;Yiming Fei;Hao Su;Qi Li;Yanan Li","doi":"10.1109/TSMC.2025.3580690","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3580690","url":null,"abstract":"The control of lower-limb exoskeletons plays a crucial role in determining the effectiveness of walking assistance, but how to generate a reference signal still poses a significant challenge. Many existing approaches involve offline training and classifiers or depend on prefabricated models, lacking the adaptability needed to support diverse users and real-time scenarios with varying gait cycles. Meanwhile, balancing intervention on human limbs between compliance and assistance during learning is still an open problem. To address these issues, this article proposes a real-time learning method for walking assistance without classifiers, automatically adapting to alterations in motion patterns. The control law, based on adaptive admittance control and the equilibrium state, ensures stable assistance during learning with intuitive parameter tuning and allows for switching of gait during assistance. Utilizing selective memory recursive least squares in a neural network enables rapid learning and precise prediction of human users’ motion intention, without pretraining. Experimental results demonstrate that our approach achieves a prediction error within 6° after half a minute of learning with a prediction ahead time of 120 ms, outperforming classic approaches. The assistance performance is consistent despite varied control parameters, indicating a certain level of robustness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7060-7074"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100495","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}

{"title":"When Process Control Meets Big Data: Data-Driven Cloud-Edge Collaborative Predictive Control Method for Multiple Operating Conditions Processes","authors":"Keke Huang;Yanwei Tang;Zui Tao;Dehao Wu;Chunhua Yang;Weihua Gui","doi":"10.1109/TSMC.2025.3582880","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3582880","url":null,"abstract":"Complex industrial processes often run under varying operating conditions. Learning-based control methods are difficult to adapt to these unknown variations. Therefore, it is necessary to update the model and control strategy adaptively. However, in traditional control frameworks, due to the limitation of computational and storage resources of edge devices, control strategies are difficult to update once deployed, which leads to model mismatch after operating condition change and seriously reduces the control performance. To solve this problem, this article proposes a novel cloud-edge collaborative control method. Specifically, a cloud-assisted parallel subspace identification method is proposed, which fully utilizes the powerful computational capability of the distributed cluster in the cloud to achieve fast and accurate model identification. Then, an explicit control strategy is proposed, which solves the control law as a piece-wise affine function offline. The process model and explicit control law are sent down to the edge, enabling fast and precise control under limited resource constraints. An operating condition change detection method based on the process model is proposed, and the edge detects the emergence of new operating conditions by the prediction error. Meanwhile, to fully excite new operating condition characteristics, a joint control and excitation signal generator (JCESG) is designed. JCESG ensures accurate identification of new operating condition model under limited data, which in turn greatly shortens the operation condition switching process and ensures fast modeling and precise control in new operating conditions. Notably, considering that the proposed method can adaptively realize model identification and control law update, it is capable of adapting to the continuous change of operating conditions, and the sufficient excitation of JCESG greatly reduces the data volume requirement for model update, which further ensures that the method adapts to the full range of operating conditions. Finally, extensive experiments verified the superiority of the proposed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6829-6841"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100454","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}

{"title":"Stabilization of Hybrid Neutral Stochastic Delay Systems With Aperiodically Intermittent Control and Delay Feedback","authors":"Fangzhe Wan;Feiqi Deng;Xueyan Zhao","doi":"10.1109/TSMC.2025.3580625","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3580625","url":null,"abstract":"This article addresses the stabilization of neutral stochastic delay systems (NSDSs) employing aperiodically intermittent controllers (APIC) based on delay feedback and asynchronous switching. To tackle issues arising from the neutral term, we introduce a special auxiliary system (AS) that is not a neutral system, and is distinct from existing literature [41]. Utilizing the Lyapunov-Krasovskii functional approach and the iterative method, the stability criterion for the AS is given, which consists of the bound of three delay functions and the duty-cycle. If the stability criterion is satisfied, the AS will achieve mean-square exponentially stability, offering a viable APIC design scheme for non-NSDSs. Additionally, employing the equivalence technique (ET), this article obtains an additional bound for the system delay function, denoted by <inline-formula> <tex-math>$tau ^{*}$ </tex-math></inline-formula>. When the system delay function <inline-formula> <tex-math>$tau (t)lt tau ^{*}$ </tex-math></inline-formula>, we demonstrate that the NSDS with intermittent feedback is mean-square exponentially stable if the non-neutral AS is stable. This method is called as AS method based on non-neutral type (ASMbNT). With one comparison, this article reveals that the ASMbNT proposed in this article not only addresses the problem considered in [41], but also yields improved results. Lastly, to demonstrate the effectiveness and validity of the proposed approach, a numerical example is presented.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7169-7183"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100334","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}

{"title":"Event-Based Adaptive Optimal Fault-Tolerant Consensus Control for Uncertain Nonlinear Multiagent Systems With Actuator Failures","authors":"Yuanbo Su;Qihe Shan;Hongjing Liang;Tieshan Li;Huaguang Zhang","doi":"10.1109/TSMC.2025.3582815","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3582815","url":null,"abstract":"This article addresses the event-based adaptive optimal fault-tolerant consensus control problem for a class of nonlinear multiagent systems (MASs) under a directed graph. It can be solved that both control gains and actuator failure parameters are unknown in considered MASs, which enhances the practicability of optimal consensus control. First, a reinforcement learning-based distributed optimal control law is designed by constructing the identifier-critic-actor learning networks. Furthermore, by utilizing the distributed optimal control law as an auxiliary variable, an adaptive fault-tolerant controller is proposed to effectively compensate for actuator failures. Meanwhile, a co-design scheme is proposed for the construction of an event-triggered control input with the fault-tolerant property. It is proven that the designed control input ensures the boundedness of closed-loop systems through rigorous stability analysis. Finally, the effectiveness of the developed approach can be illustrated via simulations of numerical nonlinear MASs and a group of autonomous underwater vehicles.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7273-7287"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100316","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}

{"title":"DOMAIN: Mildly Conservative Model-Based Offline Reinforcement Learning","authors":"Xiao-Yin Liu;Xiao-Hu Zhou;Mei-Jiang Gui;Guo-Tao Li;Xiao-Liang Xie;Shi-Qi Liu;Shuang-Yi Wang;Qi-Chao Zhang;Biao Luo;Zeng-Guang Hou","doi":"10.1109/TSMC.2025.3578666","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3578666","url":null,"abstract":"Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. To address the above issues, this article proposes a mildly conservative model-based offline RL algorithm (DOMAIN) without estimating model uncertainty, and designs the adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this article, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms, and has the guarantee of safety policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms and the average performance has improved by 1.8% on the D4RL benchmark.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7142-7155"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100504","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}

{"title":"The Spacecraft Early Analysis Model: An MBSE Framework for Early Analysis of Spacecraft Behavior","authors":"Joe Gregory;Lucy Berthoud;Theo Tryfonas;Ludovic Faure;Antonio Prezzavento","doi":"10.1109/TSMC.2025.3581567","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3581567","url":null,"abstract":"Model-based systems engineering (MBSE) represents a move away from the traditional approach to systems engineering. MBSE has the potential to promote consistency, communication, clarity and maintainability within systems engineering projects. MBSE also has the potential to address one of the well-known issues of the systems engineering process—the late discovery of errors or design faults. In this article, the development of the “Spacecraft early analysis model” (SEAM) is detailed and the current version is presented. The SEAM is a model-based framework developed by the authors to define, execute and analyze spacecraft structure and behavior during preliminary design. The SEAM comprises multiple modules (Project, Requirements, Mission, System, Operations) that enable the definition of the spacecraft and its supporting systems, and enables each to be updated independently. The SEAM incorporates a novel behavioral pattern that structures the modes and functions of the spacecraft using state machines and activities. Using this pattern, the behavior itself is not prescribed, as is common in similar model-based representations of spacecraft. The user defines individual functions and modes, and the user then simulates the behavior of the spacecraft in response to a concept of operations (ConOps). In this article, the need for the SEAM, the development approach, the SEAM composition and the limitations of the SEAM are presented and discussed.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"6658-6670"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100348","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}

{"title":"Data and Prior-Driven Low-Light Enhancement Boosting the Visibility of Imaging Systems","authors":"Huaian Chen;Tianle Liu;Ben Wang;Zhixiang Wei;Yi Jin;Enhong Chen","doi":"10.1109/TSMC.2025.3579759","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3579759","url":null,"abstract":"Imaging systems working under poor lighting conditions often suffer from poor visibility, compromising the reliability of vision-based systems. To address this problem, recent studies have developed data-driven low-light image enhancement (LIE) techniques to improve visibility. However, these LIE methods typically require large amounts of training samples, and the learned representation may not be valid for real-world scenes due to data discrepancies. In this work, we propose DP-LIE, an unsupervised LIE method driven by both data and priors. Unlike the existing methods that learn unexplainable high-dimensional features for end-to-end mapping, DP-LIE focuses on learning prior-guided parametric maps with definite meanings, enabling the low-light images to be brightened from an interpretable prior-based perspective. To this end, we design a simple yet effective prior-guided network-assisted LIE formulation, which elaborately bonds the data-driven representations with the traditional priors. The embedded priors narrow the solution space of the LIE model, allowing it to be efficiently trained with fewer samples. Notably, even trained with just a single low-light input image, the proposed method (denoted as DP-LIE-S) achieves comparable performances with existing unsupervised LIE methods. Moreover, experiments demonstrate that the proposed DP-LIE method exhibits excellent generalization performance across diverse imaging devices and promises better detection results for vision-based detection systems in nighttime scenes.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7085-7099"},"PeriodicalIF":8.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100354","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}