IEEE Transactions on Industrial Cyber-Physical Systems最新文献

{"title":"IEEE Transactions on Industrial Cyber-Physical Systems Publication Information","authors":"","doi":"10.1109/TICPS.2025.3590506","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3590506","url":null,"abstract":"","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11083976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contact-Based Digital Twins Modeling for Reinforcement Learning of Robotic Disassembly Operations","authors":"M. Qu;D. T. Pham;F. Lan;Z. Wu;Y. Zang;Y. Zhang;Y. Wang","doi":"10.1109/TICPS.2025.3589351","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3589351","url":null,"abstract":"Reinforcement learning (RL) holds great potential for robotic skill acquisition, but its practical deployment in industrial disassembly tasks is challenged by low sample efficiency and safety concerns in contact-intensive environments. This article presents a cyber-physical approach that enhances RL through simulation-to-reality (sim-to-real) skill transfer using a Digital Twin (DT). The DT models the physical environment and is calibrated via the Bees Algorithm, a metaheuristic optimisation method, to reduce the reality gap by minimising discrepancies between simulated and real-world responses. That enables more accurate simulation of contact dynamics without requiring manual parameter tuning or expert modelling. The method is validated on a representative task: removing a bolt from a door-chain groove, simulating the challenges of force-sensitive disassembly operations. Results demonstrate that the DT-assisted sim-to-real transfer improves learning efficiency, offering a scalable framework for deploying RL in cyber-physical systems for intelligent disassembly and circular manufacturing.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"497-506"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems","authors":"Jinwei Hu;Zezhi Tang;Xin Jin;Benyuan Zhang;Yi Dong;Xiaowei Huang","doi":"10.1109/TICPS.2025.3586988","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3586988","url":null,"abstract":"This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO’s ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"472-484"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Digital Twin-Empowered Online Control and Optimization for Cyber-Physical Systems","authors":"Yushuai Li;Tianyi Li;Jiachen Xu;Sabita Maharjan;Torben Bach Pedersen;Tingwen Huang;Yan Zhang","doi":"10.1109/TICPS.2025.3586992","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3586992","url":null,"abstract":"Digital twin (DT) technologies have the potential to revolutionize the online control and optimization methods for cyber-physical systems. However, the lack of the high-fidelity DT models that can accurately simulate the operation of the physical world is a key obstacle to their development. To address this issue, this paper proposes a federated DT (FedDT) architecture and modeling method to create a DT that can mimic the intrinsic dynamics and operational mechanisms of the physical world. Specifically, the FedDT architecture encompasses the internal components for specifying the assignment of functionalities and the external structure to leverage the collaboration of individual DTs. This model provides a digital representation of an online decision-making process for universal control and optimization problems, while considering DTs’ collaboration to enrich their capabilities. Then, we design a federated self-learning algorithm to complete the DT modeling. By using the modeled FedDT, we are able to make purposeful planning and decisions in the digital space that are equivalent to those in the real world, without requiring knowledge of the dynamics of entities and environments. This enables achieving predictive evolution, precise estimation, and reliable decision for online control and optimization. The proposed FedDT offers practical advantages for engineering systems that require predictive and reliable decision-making with strong lookahead capabilities. It is particularly well-suited for applications such as autonomous driving, frequency control in smart grids, and real-time process control in Industrial Internet of Things (IIoT), where accurately modeling physical system dynamics is highly challenging. In real-world deployments, engineers only need to define the observations, actions, and reward signals. The proposed method then autonomously learns the underlying system dynamics and derives informed, data-driven optimization and control strategies. Finally, we demonstrate the effectiveness of our proposed FedDT model by applying it for the autonomous driving use case.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"485-496"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-Space Driven Digital Twin for Condition Monitoring and Predictive Health Assessment in Grid-Integrated Power Converter System","authors":"Arun Kumar;Nishant Kumar","doi":"10.1109/TICPS.2025.3586823","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3586823","url":null,"abstract":"This work presents a novel Digital Twin (DT) framework, integrating state-space analysis with advanced optimization for predictive health monitoring in a two-stage, single-phase grid-connected power electronic converter system. The DT is constructed as a high-fidelity mathematical model, continuously synchronized with its Physical System (PS) through real-time sensor-driven data acquisition, enabling seamless condition monitoring and anomaly detection. A well-defined objective function ensures precise system representation by integrating PS sampled data with the DT. To enhance predictive maintenance, an advanced E2FD-HO (Electromagnetic Field and Electrostatic Discharge Hybrid Optimization) algorithm is introduced, offering superior convergence speed and parameter estimation accuracy. Additionally, real-time fault diagnostics are conducted under varying operational conditions, with the inverter’s switching control governed by <italic>αβ</i>CDSC (alpha-beta Cascaded Delayed Signal Cancellation)-UVT (Unit Vector Template)-based IMPC (Integral Model Predictive Control) strategy, improving transient stability and system adaptability. The FPGA-based OPAL-RT real-time DT validation demonstrates a Percentage Similarity (PST) exceeding 98.55%, underscoring the DT’s robustness in predictive maintenance for system. These findings contribute to pre-fault detection, condition-based monitoring, and secure DT implementations, offering scalable solutions for Industrial Cyber-Physical Systems (ICPS).","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"464-471"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Trustworthy Detection and Classification of Cyber-Physical Attacks in the Presence of Disturbances Using Morphological Image Processing and Explainable AI","authors":"Ahmad Abu Nassar;Matthew Oinonen;Walid G. Morsi","doi":"10.1109/TICPS.2025.3586211","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3586211","url":null,"abstract":"Smart Digital Substations (SDSs), are cyber-physical systems (CPSs) that rely on communication networks to exchange information among physical devices, making such CPSs vulnerable to cybersecurity threats. The problem of detecting and classifying attacks in SDSs has been traditionally studied by considering machine learning as a closed box with no interpretation of the decisions made, which has led to untrustworthy algorithms. The attack detection becomes more challenging in the presence of disturbances, as certain types of attacks may exhibit similar behavior to some disturbance events. Furthermore, some attacks may concurrently occur in the presence of disturbances, which may increase the misclassification rate. This paper presents a novel trustworthy approach for detecting and classifying attacks considering the simultaneous occurrence of disturbances in SDSs. This study uses Explainable Artificial Intelligence (XAI) to provide interpretability of the classification decisions using the cyber and physical features in SDSs. This method applies a series of processes, including the use of the Bartlett observation window and morphological image processing, to enhance the learning of the Convolutional Neural Network (CNN) to better extract the hidden features relevant to the attacks and the disturbances when applying the Continuous Wavelet Transform. The proposed approach achieved detection and classification accuracies of 99.37% and 98.44%, while reducing the computational time by 90%, due to the incorporation of a hardware acceleration of multiple graphics processing units (GPUs).","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"442-453"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Integrated Load Redistribution Attack Model to Induce Cascading Failures in Cyber Physical Systems","authors":"Rohini Haridas;Satish Sharma;Rohit Bhakar;Chenghong Gu","doi":"10.1109/TICPS.2025.3585901","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3585901","url":null,"abstract":"Load Redistribution (LR) attacks pose a significant threat to the reliability and security of Cyber-Physical Systems (CPS), particularly in critical infrastructures such as power system, potentially triggering cascading failures. Accurate modeling is essential for a comprehensive understanding of these attacks and their consequences. Existing models for these attacks typically handle critical line identification and exploitation as separate tasks. This segregated approach might overlook power flow changes during an attack and post-contingency phase, possibly affecting the accurate identification of vulnerable lines. To overcome this limitation, this paper introduces a new integrated LR attack model. This model handles critical line identification and exploitation simultaneously, considering power flow changes during and after the attack. This approach aids in the accurate identification of the critical line that could cause a cascading failure. The findings presented in this study, particularly from an attacker’s perspective, could significantly contribute to the development of robust and secure CPS. The proposed model is validated using the modified IEEE 14 and IEEE 57 bus system, demonstrating its ability to induce overloads on multiple lines by targeting a single critical line, leading to a cascading failure.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"454-463"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Triggered Predictive Control for Semi-Markov Jumping Networked Control Systems With Random DoS Attack","authors":"Li Qiu;Baixi He;Chenmei Song;Zhen Huang;Jie Teng;Zongze Wu","doi":"10.1109/TICPS.2025.3577549","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3577549","url":null,"abstract":"Networked control systems (NCSs) are an important component of industrial cyber-physical systems (ICPSs). This paper proposes an event-triggered discrete-time stochastic semi-Markov hopping networked control systems robust predictive control strategy for ICPSs to address common parameter uncertainties and external disturbances. The control scheme aims to tackle the issues of randomness and time-varying characteristics inherent in ICPS, such as random time delays and denial-of-services (DoS) attacks in the channels of sensor-to-controller (S-C) and controller-to-actuator (C-A). To overcome the inherent bandwidth limitations, a dynamic event-triggered scheme is suggested to alleviate the network strain. The stability analysis gives a sufficient criterion for the <inline-formula><tex-math>$sigma$</tex-math></inline-formula>-error mean-square stability (<inline-formula><tex-math>$sigma$</tex-math></inline-formula>-MSS) of a semi-Markov jump linear system (s-MJLS) using Lyapunov function. Based on this analysis, a network predictive controller is designed to guarantee the robustness of the system. A linear switched reluctance motor (LSRM) system, as a typical application of ICPSs, is used for simulation and experimentation, thereby validating the practical efficacy and suitability of this approach.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"419-428"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Group Formation Control of Cyber-Physical Systems via Distributed Cloud Computing","authors":"Hongru Ren;Yinren Long;Hongyi Li;Tingwen Huang","doi":"10.1109/TICPS.2025.3561726","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3561726","url":null,"abstract":"This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"341-350"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Relative-Error-Dependent TOD Protocol for Cyber-Physical Systems Under an Event-Triggered Communication","authors":"Hongchenyu Yang;Chen Peng;Engang Tian","doi":"10.1109/TICPS.2025.3557733","DOIUrl":"https://doi.org/10.1109/TICPS.2025.3557733","url":null,"abstract":"To deal with different data magnitudes or measurement units of different system nodes, a novel relative-error-dependent try-once-discard (RED-TOD) communication protocol with event-triggered detectors is proposed to improve the accuracy and reliability of node selection for band-limited cyber-physical systems. With the combined effect of the absolute error and the relative error, a novel node activation condition is proposed to comprehensively reflect and fairly compare node transmission requirements. Under such a stricter scheduling rule, the node selection is more accurate as required and the available communication bandwidth is utilized with higher efficiency. Moreover, periodic sampling module and event-triggered detectors are placed before RED-TOD scheduling scheme to deal with redundancy and inaccurate transmissions of the existing variable sampling scheme. Taking the proposed scheduling scheme and non-small network-induced delay into account, an impulsive closed-loop system model is well established. Sufficient criteria for input-to-state stabilization are derived by employing the Lyapunov-Krasovskii functional approach. A searching algorithm is also presented to co-design optimized scheduler parameters and controller gains. Furthermore, to avoid continuous calculations, the next transmission instant and the dormant period of the event-triggered detector are predicted according to the present information carried by each node. Finally, an illustrative example is employed to demonstrate the effectiveness and superiority of the proposed scheduling method.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"318-328"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}