IEEE Transactions on Control Systems Technology最新文献_第2页

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 Spatiotemporal Graph Convolutional Network for Predictive Control With Stability Guarantees 具有稳定性保证的预测控制的时空图卷积网络

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-01 DOI: 10.1109/TCST.2026.3686435

Zixuan Chen;Keke Huang;Ke Wei;Dehao Wu;Chunhua Yang;Weihua Gui

{"title":"A Spatiotemporal Graph Convolutional Network for Predictive Control With Stability Guarantees","authors":"Zixuan Chen;Keke Huang;Ke Wei;Dehao Wu;Chunhua Yang;Weihua Gui","doi":"10.1109/TCST.2026.3686435","DOIUrl":"https://doi.org/10.1109/TCST.2026.3686435","url":null,"abstract":"Since controlled systems are becoming large-scale, complicated, and highly coupled, spatiotemporal features are becoming increasingly essential for accurately representing the system’s dynamics. Recent years have witnessed numerous attempts to enhance predictive models with spatiotemporal features. Inspired by the powerful spatial feature extraction capabilities of graph neural networks, this brief proposes a model predictive control (MPC) method based on graph convolutional networks (GCNs). Specifically, a novel GCN-based predictive model is first proposed, which can extract both spatial and temporal features. Notably, the model can also adaptively learn the spatial graph structure from time series data without requiring any prior knowledge. Then, combined with the MPC framework, the gradient descent (GD) method is introduced to handle the optimization problem. Moreover, stability and feasibility analyses have been conducted to guarantee the effectiveness of the proposed method during practical applications. The experimental results indicate the strength and reliability of the proposed method, which can greatly improve control accuracy compared to other learning-based MPC methods.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"2146-2153"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443071","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

Hybrid Control of an Islanded Network of Microgrids via Hybrid Dynamical Systems 基于混合动力系统的孤岛微电网混合控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-08 DOI: 10.1109/TCST.2026.3676141

Carolina Albea;Alexandre Seuret;Francesco Ferrante

{"title":"Hybrid Control of an Islanded Network of Microgrids via Hybrid Dynamical Systems","authors":"Carolina Albea;Alexandre Seuret;Francesco Ferrante","doi":"10.1109/TCST.2026.3676141","DOIUrl":"https://doi.org/10.1109/TCST.2026.3676141","url":null,"abstract":"This article proposes a distributed hybrid control law for an islanded network of microgrids, which are allowed to exchange their own power and their own information on the state of charge of their own batteries. The control objective consists in reaching a tradeoff between driving the state of charge of the battery toward a neighborhood of a consensus and reducing the losses in power exchange. The model includes a saturating integrator to further take into account the nonlinear dynamics of the state of charge. The modeling and the control design strongly rely on the hybrid dynamical systems theory, where the state of charge of the batteries and the power flows are considered as continuous-time variables, while the state of connections/disconnections between microgrids is considered as discrete-time variables. The main result of this article guarantees the uniform global asymptotic stability of an attractor, which is specifically chosen to balance proximity to the consensus equilibrium and the reduction of energy losses due to interconnections between microgrids. Finally, this article illustrates the theoretical results through several simulations.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1871-1882"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509295","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

Adaptive Optimal PI Control for Grid-Forming Inverters With Provable Multitime-Scale Stability via Lyapunov-Constrained Learning 基于李雅普诺夫约束学习的可证明多时间尺度稳定性的成网逆变器自适应最优PI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: 2026-03-18 DOI: 10.1109/TCST.2026.3692709

Hamad Alduaij;Chenxu Chao;Yang Weng

{"title":"Adaptive Optimal PI Control for Grid-Forming Inverters With Provable Multitime-Scale Stability via Lyapunov-Constrained Learning","authors":"Hamad Alduaij;Chenxu Chao;Yang Weng","doi":"10.1109/TCST.2026.3692709","DOIUrl":"https://doi.org/10.1109/TCST.2026.3692709","url":null,"abstract":"Modern inverter-based power systems require controllers that adapt to changing grid conditions while maintaining strict stability across nested control loops. Existing approaches face a fundamental tradeoff: classical proportional–integral (PI) controllers offer provable stability but are static, while learning-based methods can adapt but often violate the structural stability guarantees needed for safe operation, especially in systems with tightly coupled, multitime-scale dynamics. This work proposes a distributed adaptive control framework for grid-forming inverters that combines neural gain scheduling with provable Lyapunov stability across multiple time scales. Our approach reformulates nested Lyapunov stability conditions as explicit gain-ratio inequality constraints and embeds them into both: 1) an offline augmented-Lagrangian constrained optimization for base gain synthesis and 2) online neural schedulers that adapt controller parameters to changing operating conditions while preserving stability certificates. A key contribution is the introduction of per-converter dynamic adaptation states governed by Lyapunov-passivity constraints, enabling state-dependent gain scheduling within certified stability envelopes. Implemented within a physics-informed, differentiable simulation environment, controllers tuned with our method preserve stability under severe transients, including large load steps and setpoint changes, while improving closed-loop regulation. This framework retains the familiar PI control architecture while rigorously enforcing stability margins, offering a practical stability-certified learning paradigm for emerging power-electronic systems. The adaptive control performs algorithmic gain synthesis and scheduling as operating conditions change, implemented through learned neural schedulers operating at multiple timescales.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1979-1990"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507924","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

Comments on "Efficient Group Key Management for Resilient Operation of LoRaWAN-Based Smart Grid Applications" “基于lorawan的智能电网应用弹性运行的高效组密钥管理”评论

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-21 DOI: 10.1109/TCST.2026.3682777

Lihua Liu;Zhengjun Cao

引用次数: 0

Crash-Risk-Aware Integrated Predictive Control in Emergency Conditions for Intelligent Vehicles 紧急情况下智能汽车碰撞风险感知集成预测控制

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-12 DOI: 10.1109/TCST.2026.3688255

Yu Zhang;Fuwei Wu;Yechen Qin;Mingming Dong;Shaoyang Shi;Ke Chen

{"title":"Crash-Risk-Aware Integrated Predictive Control in Emergency Conditions for Intelligent Vehicles","authors":"Yu Zhang;Fuwei Wu;Yechen Qin;Mingming Dong;Shaoyang Shi;Ke Chen","doi":"10.1109/TCST.2026.3688255","DOIUrl":"https://doi.org/10.1109/TCST.2026.3688255","url":null,"abstract":"Crash-avoidance systems in current intelligent vehicles (IVs) require accurate crash risk estimation and optimization of multiple control inputs. However, the uncertainties in vehicle states caused by the inherent noise in onboard sensors lead to increased errors in spatiotemporal crash risk calculations, which, in turn, affect the optimization of control inputs. To address these challenges, this article introduces a crash-risk-aware integrated predictive control (CRIPC), which computes crash positions and time-to-collision with uncertainties (UTTC) between vehicles under nonideal conditions by constructing an extensive elliptical vehicle geometry model to quantify the uncertainties arising from sensor noise. Additionally, CRIPC utilizes UTTC and the crash-point position between vehicles to dynamically adjust the crash-risk-aware repulsive field, which is subsequently used to define the objective function for CRIPC optimization. Validation results from real-vehicle experiments and driver-in-the-loop (DiL) simulations demonstrate that CRIPC effectively quantifies the motion uncertainties of surrounding vehicles, significantly enhancing crash avoidance capabilities while maintaining vehicle stability. Furthermore, CRIPC shows robustness to variations in road conditions and system uncertainties, meeting the real-time requirements of practical applications.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"2049-2060"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443189","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

Controlling PV Panels Tilt Orientation for Optimal Crop Shading and Power Generation in Agrivoltaics 控制光伏板倾斜方向以获得最佳作物遮阳和农业发电

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-04-13 DOI: 10.1109/TCST.2026.3675731

Nicola Mignoni;Paolo Scarabaggio;Raffaele Carli;Mariagrazia Dotoli

{"title":"Controlling PV Panels Tilt Orientation for Optimal Crop Shading and Power Generation in Agrivoltaics","authors":"Nicola Mignoni;Paolo Scarabaggio;Raffaele Carli;Mariagrazia Dotoli","doi":"10.1109/TCST.2026.3675731","DOIUrl":"https://doi.org/10.1109/TCST.2026.3675731","url":null,"abstract":"Recently, the merger of agriculture and photovoltaic (PV)-based energy generation has led to the new field of agrivoltaic technology. The baseline paradigm consists of mounting PV modules over cultivated areas, increasing the economic return of the land by enabling simultaneous power generation and crop production. However, the presence of PV modules can reduce the amount of light absorbed by the plants, possibly impacting the final harvest. This article focuses on the interaction between plantations and modules, proposing a novel optimization-based approach for automatically controlling the balance between the crops’ light exposure and the power generation. First, we detail the proposed mathematical model and the arising nonconvex mixed-integer optimization problem. Then, we provide an approximate convex mixed-integer formulation, where the guarantees on the approximation error bounds are formally shown. Moreover, to guarantee scalability, we propose a pruning strategy to remove unnecessary variables. Finally, the results of numerical simulations are reported and discussed, using real-world data based on an existing agrivoltaic plant in Southern Italy.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 4","pages":"1883-1898"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11480707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507624","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}

引用次数: 0

Visual-RRT*: Generating Asymptotically Optimal Trajectories With Vision-Based Controllers Visual-RRT*：基于视觉控制器的渐近最优轨迹生成

IF 4.4 2区计算机科学

IEEE Transactions on Control Systems Technology Pub Date : 2026-07-01 Epub Date: 2026-03-16 DOI: 10.1109/TCST.2026.3672186

Ramses Reyes;Israel Becerra;Rafael Murrieta-Cid

引用次数: 0

Safe On-Orbit Dislodging of Deployable Structures via Robust Adaptive MPC 基于鲁棒自适应MPC的可展开结构安全在轨位移研究

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.2026.3686441

Longsen Gao;Claus Danielson;Andrew Kwas;Rafael Fierro

引用次数: 0