IEEE Transactions on Control Systems Technology最新文献

{"title":"Incremental Data-Driven Fault Diagnosis for Conveyor Rollers With Prior Drift Adaptation: An Online Active Kernel Learning Approach","authors":"Han Zhou;Hao Ren;Hongpeng Yin;Guoxiang Zhong;Guangxia Xu;Chunhua Yang;Weihua Gui","doi":"10.1109/TCST.2025.3636213","DOIUrl":"https://doi.org/10.1109/TCST.2025.3636213","url":null,"abstract":"Conveyor rollers are essential components in the hot steel milling process, responsible for transporting products between various stages of production throughout the entire process. Due to the development of Industrial Internet of Things (IIoT), conveyors are now equipped with sensors to collect real-time data, facilitating data-driven fault diagnosis models. While many well-trained data-driven diagnostic models initially demonstrate accurate performance, their effectiveness may decline over time due to unexpected changes, such as the emergence of new faults. This causes the prior distribution of fault data to change over time, and we refer to this as prior drift. In this study, we provide a mathematical definition of prior drift within the context of fault diagnosis and then develop a general learning criterion to guide the incremental updating of fault diagnosis models under such conditions. Building on this criterion, we propose an online active kernel learning-based fault diagnosis model (OAKL) for conveyor rollers. OAKL updates the diagnosis model in an incremental manner and adapts to prior drift. Particularly, it leverages selective querying and labeling of the most informative samples to facilitate early detection of novel faults, avoid performance bias, and reduce the number of incremental samples required. A rigorous theoretical analysis is conducted to demonstrate the theoretical validity of OAKL. The experimental results on a practical industrial case demonstrate the efficacy of our approach in fault diagnosis.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"698-710"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484443","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 Conditional Invertible Neural Network-Based Fault Detection","authors":"Wenxin Sun;Xudong Shi;Sudao He;Weili Xiong;Hongtian Chen;Biao Huang","doi":"10.1109/TCST.2025.3633773","DOIUrl":"https://doi.org/10.1109/TCST.2025.3633773","url":null,"abstract":"Residual generation and hypothesis test are two important components in residual-based fault detection techniques. Recent studies mainly focused on enhancing residual generation algorithms, but often overlook the Gaussian distribution assumption that is required for hypothesis test. Based on the conditional invertible neural network (CINN), this study proposes a novel approach for mapping residual signals into near-Gaussian-distributed latent variables, thereby enhancing the reliability and effectiveness of the hypothesis test for fault detection. With the specially designed architecture using CINN, the proposed mapping from residual signals to latent variables has no information loss, thus guaranteeing the accuracy of the proposed fault detection method. The main contributions of this study are twofold: 1) to ensure that the latent variables have distributions similar to an ideal Gaussian distribution, a novel CINN training approach is proposed and 2) historical process information is incorporated into the residual-to-latent variable mapping, dynamically refining the mapping procedures in response to the system behavior. This approach is primarily used to tackle the challenges posed by nonadditive and non-Gaussian noises in fault detection. A dc speed control system and a wastewater treatment plant are adopted to verify the effectiveness of the proposed fault detection approach.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"788-799"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484746","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":"Iterative Learning Control of Functional Electrical Stimulation Electrode Arrays","authors":"Lucy Hodgins;Chris T. Freeman;Zehor Belkhatir","doi":"10.1109/TCST.2025.3636546","DOIUrl":"https://doi.org/10.1109/TCST.2025.3636546","url":null,"abstract":"Stroke often causes weakness, paralysis, or loss of coordination in the hand and wrist, making it difficult to perform everyday tasks. Current rehabilitation approaches do not adequately assist patients in regaining their lost function; however, it is possible to produce accurate hand and wrist gestures by artificially stimulating muscles using functional electrical stimulation (FES) applied to multielement electrode arrays. This has been possible using iterative learning control (ILC); however, it required lengthy model identification tests, and accuracy degraded due to fatigue, spasticity, and changes in array position. This article develops a new FES electrode array control framework that maintains high accuracy despite uncertain and potentially time-varying dynamics. First, a model of stimulated hand and wrist dynamics embedding FES array misalignment is developed, and robust stability properties are derived using the gap metric. A compensating controller is then proposed to ameliorate array misalignment, and this is integrated within a powerful framework termed estimation-based multiple-model ILC (EMMILC), which automatically updates the underlying model to maintain performance in the presence of uncertain and changing dynamics. It is shown that EMMILC can remove the need for model identification, whilst maintaining high performance. This significantly improves the usability of FES arrays and opens up the possibility of bringing effective therapy to millions of patients in their own homes. Experimental results reveal that the proposed controller reduces the average converged error norm to 31.3% of that obtained using the existing model-based ILC.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"759-772"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484763","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 Resilient Topology Configuration Framework for Heterogeneous Robots With Fixed-Time Coordination","authors":"Chenggang Wang;Bingyang Wang;Dongyue Huang;Shanying Zhu;Lei Song;Xinping Guan","doi":"10.1109/TCST.2025.3629707","DOIUrl":"https://doi.org/10.1109/TCST.2025.3629707","url":null,"abstract":"Heterogeneous robots capitalize on the diverse strengths of different nodes, enabling them to collaboratively perform tasks unfeasible for homogeneous robots. However, variations in perception capabilities and dynamic models complicate coordination, and potential failures present risks to the safety of the entire coordinated topology. To this end, this article proposes a resilient topology configuration framework for heterogeneous robots with fixed-time coordination. The framework solves the weighted topology configuration with <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-node connectivity to tackle the node failure problem and incorporates a <inline-formula> <tex-math>$p$ </tex-math></inline-formula>-partite graph to solve the compatibility constraints caused by heterogeneous nodes. Building upon this optimized topology, a fixed-time convergence control barrier function is designed. Furthermore, a constraint-driven optimization framework is proposed to achieve fixed-time coordination of heterogeneous nodes with minimum control cost, thereby achieving synchronous configuration while ignoring initial errors, and being capable of recovering topological resilience after node failure. Comparative simulation results demonstrate the effectiveness of the proposed method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"554-568"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485174","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":"DCSV-Krylov Exploration for Control Allocation in Multitime Scale Systems","authors":"C. A. Peña Fernández","doi":"10.1109/TCST.2025.3640715","DOIUrl":"https://doi.org/10.1109/TCST.2025.3640715","url":null,"abstract":"Control allocation is essential for managing MIMO dynamic systems, such as autonomous vehicles and robotics, which operate across varying temporal scales, posing challenges in optimizing control actions. A common approach involves traditional solvers for overdetermined systems---most notably the singular value decomposition (SVD), the least squares QR algorithm (LSQR), and Arnoldi-type methods---, which, while effective for homogeneous temporal scales, struggle to handle mixed dynamics. Their inability to explicitly account for temporal variations and uncertainties often results in suboptimal performance and high computational costs. To address these challenges, this work introduces a novel combination of the Dynamic Confined Space of Velocities (DCSV) method with Krylov subspace exploration, significantly enhancing control allocation in mixed-temporal-scale systems. The DCSV method generates optimized initial estimates, which are iteratively refined using Krylov methods, achieving precise control with reduced computational costs. Comparative analysis against traditional solvers demonstrates that DCSV consistently outperforms these methods, achieving higher accuracy with fewer computational resources, particularly in systems with mixed dynamic scales. Since LSQR is closely related to quadratic programming (QP) and linear programming (LP) through its cost function, and SVD directly computes generalized inverses, their evaluation provides a basis for comparison with conventional control allocation strategies. The proposed method surpasses these approaches by achieving a better balance between computational efficiency and control precision. In addition, the uncertainty principle embedded in the DCSV framework establishes theoretical limits that ensure robustness in the allocation process, supported by a structured sequential exploration process formulated via pseudocode. By optimizing initial estimates through DCSV, fewer refinement iterations are needed, leading to faster and more accurate control allocation, as confirmed by formal analysis. This methodology offers a robust and efficient solution to the challenges posed by mixed temporal scales, with enhanced adaptability for real-world applications in dynamic and computationally constrained environments.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"860-877"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485277","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 Topology-Aware Local Planning and Control for Off-Road Vehicles on 3-D Terrains","authors":"Siyuan Yu;Congkai Shen;Bogdan I. Epureanu;Tulga Ersal","doi":"10.1109/TCST.2026.3653919","DOIUrl":"https://doi.org/10.1109/TCST.2026.3653919","url":null,"abstract":"A novel topology-aware model predictive control (MPC) framework is presented for navigating off-road wheeled vehicles with extreme mobility on 3-D terrains. Prior studies have oversimplified the problem by either treating it solely as a path-planning problem or disregarding the terrain topology. To bridge this gap, this article presents a one-layer model predictive planning and control framework that considers terrain topology for off-road vehicles at operationally relevant speeds. The algorithm is tested in various simulated scenarios with comparisons to benchmarks. The proposed algorithm demonstrates superior performance by capturing the dynamic constraints, while the benchmarks either fail to complete tasks or exhibit inferior performance due to inaccurate representation of the dynamic limitations of the vehicle. Furthermore, the algorithm is tested for robustness to demonstrate its ability to handle uncertainties in terrain topology.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1106-1121"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504792","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":"Learning Terrain-Aware Bipedal Locomotion via Reduced-Dimensional Perceptual Representations","authors":"Guillermo A. Castillo;Himanshu Lodha;Ayonga Hereid","doi":"10.1109/TCST.2026.3664022","DOIUrl":"https://doi.org/10.1109/TCST.2026.3664022","url":null,"abstract":"This work introduces a hierarchical strategy for terrain-aware bipedal locomotion that integrates reduced-dimensional perceptual representations to enhance the reinforcement learning (RL)-based high-level (HL) policies for real-time gait generation. Unlike end-to-end approaches, our framework leverages latent terrain encodings via a convolutional variational autoencoder (CNN-VAE) alongside reduced-order robot dynamics, optimizing the locomotion decision process with a compact state. We systematically analyze the impact of latent space dimensionality on learning efficiency and policy robustness. In addition, we extend our method to be history-aware, incorporating sequences of recent terrain observations into the latent representation to improve robustness. To address real-world feasibility, we introduce a distillation method to learn the latent representation directly from depth camera images and provide preliminary hardware validation by comparing simulated and real sensor data. We further validate our framework using the high-fidelity agility robotics (ARs) simulator, incorporating realistic sensor noise, state estimation, and actuator dynamics. The results confirm the robustness and adaptability of our method, underscoring its potential for hardware deployment.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1149-1161"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507214","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 Collision Cone Approach for Control Barrier Functions","authors":"Manan Tayal;Bhavya Giri Goswami;Karthik Rajgopal;Rajpal Singh;M. Tejas Rao;Jishnu Keshavan;Pushpak Jagtap;Shishir Nadubettu Yadukumar","doi":"10.1109/TCST.2026.3661160","DOIUrl":"https://doi.org/10.1109/TCST.2026.3661160","url":null,"abstract":"This work presents a general framework for collision avoidance using collision cone control barrier functions (C3BFs) in both autonomous ground vehicle (AGV) and autonomous aerial vehicle (AAV). We propose a novel control barrier function (CBF) formulation inspired by collision cones by constraining the relative velocity between the vehicle and the obstacle to always point away from each other. The proposed approach is demonstrated to effectively avoid collisions with kinematically moving obstacles through simulations and hardware implementations on the TurtleBot, Stoch-Jeep, and Crazyflie 2.1 quadrotor. The real-time safety-critical controller is developed using control barrier function-based quadratic programs (CBF-QPs) with popular AGV and AAV models in the literature. Comparison with the state-of-the-art CBF highlights the less conservative nature of the proposed approach. Broadly, this research contributes a collision cone-inspired control barrier framework that provides formal guarantees for collision avoidance in autonomous vehicles.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1297-1310"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508683","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":"Airpath Control of Opposed Piston Two-Stroke Diesel Engines Using Model Predictive Control","authors":"Qilun Zhu;Asit Kumar;Erik Vorwerk;Robert Prucka","doi":"10.1109/TCST.2025.3624424","DOIUrl":"https://doi.org/10.1109/TCST.2025.3624424","url":null,"abstract":"This article presents the development and validation of advanced model predictive control (MPC) strategies for enhancing the airpath control of opposed piston two-stroke (OP2S) diesel engines. The study focuses on two approaches: linear parameter-varying explicit MPC (LPV-EMPC) and nonlinear MPC (NMPC). These strategies aim to optimize key control variables such as intake manifold pressure, pressure differential, and oxygen concentration. A mean value model (MVM) is derived and validated against a GT-Power simulation model, achieving less than 5% prediction error. Both LPV-EMPC and NMPC show a 50% improvement in response time compared with traditional control methods. LPV-EMPC, in particular, demonstrates comparable performance to that of NMPC with lower computational requirements, making it suitable for real-time applications. Simulation results indicate that the developed MPCs significantly improve transient performance, reducing normalized root mean square error (NRMSE) by 31% for intake manifold pressure, 18% for pressure differential, and 46% for oxygen mass flow. Experimental validation on a prototype engine revealed an unmodeled hardware constraint, notably a 5-s ramp-up delay in the e-turbo. Despite these limitations, the feasibility of LPV-EMPC for real-time implementation was confirmed using a prototype control system. This research highlights the potential of LPV-EMPC and NMPC to enhance OP2S engine performance in terms of transient response and emission reduction.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"601-616"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11223109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Repetitive Control for Intermittently Sampled Data: Convergence, Design, and Applications","authors":"Johan Kon;Nard Strijbosch;Sjirk Koekebakker;Leonid Mirkin;Tom Oomen","doi":"10.1109/TCST.2026.3672917","DOIUrl":"https://doi.org/10.1109/TCST.2026.3672917","url":null,"abstract":"The standard assumption that exact measurement data is available at equidistant time instances in repetitive control (RC) is not always justified, e.g., when exploiting time-stamped data from incremental encoders or in systems with data dropouts. The aim of this article is to develop an RC framework that is capable of exploiting intermittently sampled measurement signals. Stability conditions for the intermittent RC framework are provided that can be verified using the frequency response function (FRF) data. This condition results in a frequency-domain design procedure for repetitive controllers in this intermittent setting. The intermittent RC framework is validated on an industrial print belt setup for which nonequidistant measurement data are available.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 3","pages":"1381-1390"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505229","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}