IEEE Transactions on Control Systems Technology最新文献

Discrete Neural Dynamics Combined With Kalman Filter for Cooperative Control of Multirobot Systems With Constraints 离散神经动力学结合卡尔曼滤波的约束多机器人系统协同控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-23 DOI: 10.1109/TCST.2026.3682333

Duojicairang Ma;Jiazheng Zhang;Long Jin;Shuai Li

引用次数: 0

Air-Cooled Battery Pack Thermal Management via Control Barrier Proximal Dynamics 通过控制屏障近端动力学的气冷电池组热管理

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/TCST.2026.3684072

Zahra Marvi;Francesco Bullo;Andrew G. Alleyne

{"title":"Air-Cooled Battery Pack Thermal Management via Control Barrier Proximal Dynamics","authors":"Zahra Marvi;Francesco Bullo;Andrew G. Alleyne","doi":"10.1109/TCST.2026.3684072","DOIUrl":"https://doi.org/10.1109/TCST.2026.3684072","url":null,"abstract":"Efficient thermal management in lightweight electric mobility systems is critical for battery safety, performance, and longevity. However, challenges remain due to cost and weight constraints and limited onboard computational resources. In battery packs composed of hundreds of cells, lumped-parameter models fail to capture spatial temperature variations, while high-fidelity models render real-time, optimization-based control computationally intractable. This work addresses these challenges through: 1) a modular, conservation-based electrothermal graph model that balances fidelity and tractability; 2) a computationally scalable control strategy, called control barrier proximal dynamics (CBPD), which closely approximates safe and optimal control barrier function (CBF)-based controllers with major improvements in computational efficiency. We further extend CBPD to ensure safety under input disturbances and modeling uncertainties via an input-to-state safe (ISSf) and robust formulation. As a candidate system, we consider a fan-cooled lithium-ion battery pack in the University of Minnesota’s solar electric vehicle (EV), comprising 1296 cells. We validate the thermal model on this system and apply the CBPD controller on the model under realistic operational scenarios. The results demonstrate strict safety constraint satisfaction, performance comparable to intensive optimization-based methods, computational efficiency on par with classic controllers, and robustness to parameter variations.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1923-1937"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507911","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}

引用次数: 0

Particle Filter-Based Fault Diagnosis With Hidden Markov Models for Nonlinear and Uncertain Dynamical Systems 非线性不确定动力系统隐马尔可夫模型的粒子滤波故障诊断

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-17 DOI: 10.1109/TCST.2026.3682345

Vincent Kwao;Ioannis Raptis

{"title":"Particle Filter-Based Fault Diagnosis With Hidden Markov Models for Nonlinear and Uncertain Dynamical Systems","authors":"Vincent Kwao;Ioannis Raptis","doi":"10.1109/TCST.2026.3682345","DOIUrl":"https://doi.org/10.1109/TCST.2026.3682345","url":null,"abstract":"This article presents a fault diagnosis (FD) scheme for nonlinear discrete-time stochastic systems that combines particle filtering with a hidden Markov model. Fault modes are represented as binary states embedded in the system dynamics, and the posterior expectation of these states provides a principled decision rule for diagnosis. The formulation eliminates the need for parallel estimator banks, reducing computational complexity while preserving accuracy. The hybrid filter is derived using sequential Bayesian inference and realized via the bootstrap particle filter (PF), chosen for its deployment simplicity and design flexibility for nonlinear, non-Gaussian systems. A central feature is the treatment of fault transitions: rather than requiring explicit transition probabilities, the method introduces a single mode retention parameter that regulates exploratory transitions of binary particles. The likelihood function drives their evolution, allowing fault-indicative particles to proliferate when faults occur. Validation through benchmark simulations and a real-world process control experiment demonstrates the effectiveness of the approach.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1964-1978"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508033","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}

引用次数: 0

A Novel Non-Gaussian Dynamic Process Monitoring Method for Key Performance Indicators 一种新的关键性能指标的非高斯动态过程监控方法

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-12 DOI: 10.1109/TCST.2026.3671333

Yan Chen;Jinglin Zhou;Dazi Li;Rui Zhao

{"title":"A Novel Non-Gaussian Dynamic Process Monitoring Method for Key Performance Indicators","authors":"Yan Chen;Jinglin Zhou;Dazi Li;Rui Zhao","doi":"10.1109/TCST.2026.3671333","DOIUrl":"https://doi.org/10.1109/TCST.2026.3671333","url":null,"abstract":"Key performance indicators (KPIs) play a crucial role in ensuring enterprise economic benefits and product quality in modern industrial production. However, the existence of dynamic and non-Gaussian disturbances between process and KPI variables presents significant challenges for conventional static fault detection techniques based on Gaussian assumptions. In this article, a novel KPI-related process monitoring framework for non-Gaussian dynamic processes is developed, referred to as a class of Gaussianized slow feature analysis monitoring (GSFAM) method. First, a dual-space information entropy Gaussianization (DEG) method is constructed, which realizes simultaneous marginal Gaussianization transform and simultaneous rotational independence of the dual data space by maximizing mutual information (MI) and minimizing residual entropy. Then, based on the data Gaussianization, a novel KPI-related enhanced probabilistic slow feature analysis (EPSFA) model is constructed, whose extracted slow features account for both predictive performance and smoothness, in view of the inherent dynamic characteristics of chemical processes. Furthermore, a class of non-Gaussian dynamic monitoring strategies with high transferability is formed by integrating the data Gaussianization strategy and the KPI-related EPSFA model. Experiments on the Tennessee Eastman process, the fluidized catalytic cracking unit, and a real closed-loop water circulation system verify the effectiveness of GSFAM method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1675-1687"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509066","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}

引用次数: 0

Asynchronous Networked Control With Multisensor Fusion: A Hybrid Framework for Active Suspension Optimization 基于多传感器融合的异步网络控制：主动悬架优化的混合框架

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-29 DOI: 10.1109/TCST.2026.3685684

Guoliang Chen;Guixiang Du;Kun Liu;Jianwei Xia;Ju H. Park

引用次数: 0

Design of a Bio-Inspired Adaptive Central Pattern Generator Control for Pneumatic Muscle Antagonistic Joint 气动肌肉对抗关节仿生自适应中枢模式发生器控制设计

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-27 DOI: 10.1109/TCST.2026.3671978

Binrui Wang;Chenchao Ma;Feifei Qin;Pengfei Zhang;Yinglian Jin;Qingyun Li

{"title":"Design of a Bio-Inspired Adaptive Central Pattern Generator Control for Pneumatic Muscle Antagonistic Joint","authors":"Binrui Wang;Chenchao Ma;Feifei Qin;Pengfei Zhang;Yinglian Jin;Qingyun Li","doi":"10.1109/TCST.2026.3671978","DOIUrl":"https://doi.org/10.1109/TCST.2026.3671978","url":null,"abstract":"This brief proposes a control scheme for an antagonistic joint driven by pneumatic artificial muscles (PAMs). Departing from the common practice of treating central pattern generators (CPGs) as trajectory planners that feed a secondary controller, our architecture elevates the CPGs network to the primary controller, issuing control inputs directly. Inspired by biological motor circuits, this yields a tightly integrated and efficient control loop. The method also removes reliance on an explicit internal dynamic model, thereby avoiding singular behavior, numerical fragility in inverse-dynamics calculations, and the heavy computation of inertia and Coriolis terms. Consequently, the architecture is simpler, achieves better real-time responsiveness, and generalizes well across diverse trajectories and operating conditions. The robustness of the proposed approach is validated through simulations and hardware experiments, which demonstrate smooth and precise tracking performance in the presence of perturbations and measurement noise. Moreover, in a highly dynamic scenario, the chattering amplitude is reduced by 75.7% compared with sliding mode control (SMC) and 68.4% compared with proportional–integral–derivative control. These enhancements improve system stability and alleviate actuator stress. In addition, this scheme pushes forward bio-inspired robotic systems, providing autonomous, flexible, and high-efficiency approaches for real-time control applications.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"2138-2145"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442828","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}

引用次数: 0

Interval State Estimation for Li-Ion Batteries Using Thermally Coupled Equivalent Hydraulic Model 基于热耦合等效水力模型的锂离子电池区间状态估计

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-27 DOI: 10.1109/TCST.2026.3690754

Youdao Ma;Zhenhua Wang;Alejandro Goldar;Michel Kinnaert;Yi Shen

引用次数: 0

Model Predictive Control for High-Speed Train Braking Systems With Priority-Based Force Distribution 基于优先级力分配的高速列车制动系统模型预测控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-29 DOI: 10.1109/TCST.2026.3685687

Jiuhe Wang;Zhiyong Chen;Zhiwen Chen

引用次数: 0

Data-driven and Learning-based Control of Autonomous Systems 数据驱动和基于学习的自治系统控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-06-26 DOI: 10.1109/TCST.2026.3691264

引用次数: 0

A Deadbeat Predictive Adhesion Control Method With Generalized Optimal Creep Model for HST Considering Low Adhesion Condition 基于广义最优蠕变模型的低黏附条件下HST无拍预测黏附控制方法

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-01 DOI: 10.1109/TCST.2026.3677857

Ruiqi Li;Deqing Huang;Wei Yu;Luanhui Li;Hanlin Cai

{"title":"A Deadbeat Predictive Adhesion Control Method With Generalized Optimal Creep Model for HST Considering Low Adhesion Condition","authors":"Ruiqi Li;Deqing Huang;Wei Yu;Luanhui Li;Hanlin Cai","doi":"10.1109/TCST.2026.3677857","DOIUrl":"https://doi.org/10.1109/TCST.2026.3677857","url":null,"abstract":"Running high-speed trains (HSTs) at the optimal slip ratio can significantly improve energy efficiency and actively prevent wheel slip. However, the intrusion of a third medium (such as water, oil, snow, etc.) introduces random uncertainties in the wheel–rail system’s creep characteristics, making it challenging for existing methods to estimate and track the optimal slip rate accurately, especially under high-speed conditions with low adhesion. To address this, a deadbeat predictive adhesion control method with a generalized optimal creep model is proposed. First, a predictive bandwidth (PB) adhesion force observer is introduced to determine the wheel–rail adhesion coefficient quickly and accurately. Next, a generalized optimal creep model is developed, which incorporates the measured wheel–rail creep curve for running speeds ranging from 40 to 400 km/h under water medium conditions, enabling the identification of the optimal slip ratio. Building on the deadbeat control, a fast adhesion torque controller is then designed to track the optimal slip ratio. The hardware-in-the-loop (HIL) simulation results validate the effectiveness of the proposed method. Even when the train operates under high-speed low-adhesion conditions at 300 km/h, the proposed method achieves an adhesion utilization rate of 94.38%, which is a 12.5% improvement over the traditional PI adhesion control method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1735-1748"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507630","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}

引用次数: 0