Pub Date : 2026-01-23DOI: 10.1109/TCST.2026.3651454
Hanwen Zhang;Qingqing Liu;Jun Shang;Qinyuan Liu
Traditional multivariate statistical models, trained exclusively on normal data, often exhibit limited effectiveness in detecting abnormal conditions. Moreover, incipient faults exhibit subtle symptoms, rendering timely detection both challenging and critical to prevent escalation. To address these challenges, this brief proposes a fault-relevant statistical analysis (FSA) method and a structured FSA (SFSA) strategy, which leverage fault data to improve the detection of incipient faults. In the FSA framework, both normal and fault-specific samples are employed to construct an objective function that maximizes the separability between normal and fault conditions, thereby facilitating data projection into a fault-relevant subspace where fault features are accentuated. This subspace also maximizes the fault-to-signal ratio, thereby providing the clearest differentiation between normal and fault conditions. To enhance adaptability under varying fault scenarios, the SFSA strategy integrates multiple FSA sub-models, each trained on distinct fault types, with a general statistical analysis (SA) model. These models are combined through Bayesian inference to form a hybrid monitoring architecture capable of detecting both known and novel fault patterns. The proposed method is validated through numerical simulations and the Tennessee Eastman process (TEP). The SFSA achieves more than a 10% improvement in fault detection rate while maintaining a near-zero false alarm rate compared with existing methods. Even under data-scarce conditions, it continues to perform significantly better, demonstrating strong robustness and practical applicability.
{"title":"Structured Fault-Relevant Statistical Analysis for Incipient Fault Detection","authors":"Hanwen Zhang;Qingqing Liu;Jun Shang;Qinyuan Liu","doi":"10.1109/TCST.2026.3651454","DOIUrl":"https://doi.org/10.1109/TCST.2026.3651454","url":null,"abstract":"Traditional multivariate statistical models, trained exclusively on normal data, often exhibit limited effectiveness in detecting abnormal conditions. Moreover, incipient faults exhibit subtle symptoms, rendering timely detection both challenging and critical to prevent escalation. To address these challenges, this brief proposes a fault-relevant statistical analysis (FSA) method and a structured FSA (SFSA) strategy, which leverage fault data to improve the detection of incipient faults. In the FSA framework, both normal and fault-specific samples are employed to construct an objective function that maximizes the separability between normal and fault conditions, thereby facilitating data projection into a fault-relevant subspace where fault features are accentuated. This subspace also maximizes the fault-to-signal ratio, thereby providing the clearest differentiation between normal and fault conditions. To enhance adaptability under varying fault scenarios, the SFSA strategy integrates multiple FSA sub-models, each trained on distinct fault types, with a general statistical analysis (SA) model. These models are combined through Bayesian inference to form a hybrid monitoring architecture capable of detecting both known and novel fault patterns. The proposed method is validated through numerical simulations and the Tennessee Eastman process (TEP). The SFSA achieves more than a 10% improvement in fault detection rate while maintaining a near-zero false alarm rate compared with existing methods. Even under data-scarce conditions, it continues to perform significantly better, demonstrating strong robustness and practical applicability.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"1060-1066"},"PeriodicalIF":3.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287856","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}
We propose a hybrid feedback control framework for autonomous robot navigation in $n$ -dimensional Euclidean spaces with spherical obstacles. The proposed approach ensures safe and global navigation toward a target location by dynamically switching between motion-to-destination and locally optimal obstacle-avoidance modes. It generates continuous velocity inputs, collision-free trajectories, and locally optimal avoidance maneuvers. Compatible with range sensors, the framework enables navigation in both known and unknown environments. Extensive 2-D/3-D simulations and experiments on a TurtleBot 4 platform demonstrate the approach’s effectiveness, yielding shorter paths and smoother trajectories compared to state-of-the-art methods, while remaining efficient and practical.
{"title":"Hybrid Feedback Control for Global Navigation With Locally Optimal Obstacle Avoidance in n-Dimensional Spaces","authors":"Ishak Cheniouni;Soulaimane Berkane;Abdelhamid Tayebi","doi":"10.1109/TCST.2026.3650824","DOIUrl":"https://doi.org/10.1109/TCST.2026.3650824","url":null,"abstract":"We propose a hybrid feedback control framework for autonomous robot navigation in <inline-formula> <tex-math>$n$ </tex-math></inline-formula>-dimensional Euclidean spaces with spherical obstacles. The proposed approach ensures safe and global navigation toward a target location by dynamically switching between motion-to-destination and locally optimal obstacle-avoidance modes. It generates continuous velocity inputs, collision-free trajectories, and locally optimal avoidance maneuvers. Compatible with range sensors, the framework enables navigation in both known and unknown environments. Extensive 2-D/3-D simulations and experiments on a TurtleBot 4 platform demonstrate the approach’s effectiveness, yielding shorter paths and smoother trajectories compared to state-of-the-art methods, while remaining efficient and practical.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"1074-1081"},"PeriodicalIF":3.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287854","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}
Pub Date : 2026-01-21DOI: 10.1109/TCST.2026.3650897
Tushar Desai;Federico Oliva;Daniele Carnevale;Riccardo M. G. Ferrari
Accurate and robust state of charge (SOC) estimation is vital for reliable and safe battery operations. However, the nonlinear and time-varying dependence of the model parameters on the battery states makes the problem challenging. To address this task, we propose a moving-horizon estimation (MHE)-based robust approach for joint state and parameter reconstruction. Due to its optimization-based nature, the computational burden of MHE is a crucial challenge. To overcome this, we introduce a real-time adaptive sampling method based on wavelet analysis. The resulting adaptive multirate MHE dynamically selects the best measurements for solving the estimation problem. Such an approach reduces the computational burden by focusing on the most informative data in the measurement buffer. Last, we implement a parallelized observer structure, combining both multirate and reduced-order MHEs to further lower the computational burden while maintaining estimation accuracy. The proposed methods are validated using a first-order equivalent circuit model on real battery datasets, under moderate operating conditions. The adaptive sampling framework extends naturally to higher-order models with appropriate recalibration for more demanding scenarios.
{"title":"Adaptive Multirate Moving Horizon Estimator for Real-Time Battery State and Parameter Estimation","authors":"Tushar Desai;Federico Oliva;Daniele Carnevale;Riccardo M. G. Ferrari","doi":"10.1109/TCST.2026.3650897","DOIUrl":"https://doi.org/10.1109/TCST.2026.3650897","url":null,"abstract":"Accurate and robust state of charge (SOC) estimation is vital for reliable and safe battery operations. However, the nonlinear and time-varying dependence of the model parameters on the battery states makes the problem challenging. To address this task, we propose a moving-horizon estimation (MHE)-based robust approach for joint state and parameter reconstruction. Due to its optimization-based nature, the computational burden of MHE is a crucial challenge. To overcome this, we introduce a real-time adaptive sampling method based on wavelet analysis. The resulting <italic>adaptive multirate MHE</i> dynamically selects the best measurements for solving the estimation problem. Such an approach reduces the computational burden by focusing on the most informative data in the measurement buffer. Last, we implement a parallelized observer structure, combining both <italic>multirate</i> and reduced-order MHEs to further lower the computational burden while maintaining estimation accuracy. The proposed methods are validated using a first-order equivalent circuit model on real battery datasets, under moderate operating conditions. The adaptive sampling framework extends naturally to higher-order models with appropriate recalibration for more demanding scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"1067-1073"},"PeriodicalIF":3.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288173","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}
Pub Date : 2026-01-19DOI: 10.1109/TCST.2026.3651782
Peng Chen;Qiang Chen;Tiantian Xu;Xiongxiong He;Sheng Li
In this article, a data-driven control framework with unknown input constraints is proposed for the orientation tracking of magnetically actuated capsule (MAC) endoscopy. First, by designing an unknown input constraint event-triggered mechanism and augmented pseudo-partial derivatives (APPDs) reset conditions, a data-driven unknown input constraint estimator is constructed to ensure that the MAC’s deflection angles remain bounded without sliding. This constraint estimator can determine the MAC’s maximum deflection angle without force measurement or computation, addressing the issue of lesions not being observed due to excessive deflection. Second, a model-free adaptive control strategy based on APPDs is proposed, where the nonlinear disturbance and pseudo-gradient are estimated together, thereby ensuring tracking accuracy without any model information and reducing the computational burden. Additionally, data model errors are incorporated into the proposed controller to improve the accuracy of adaptive parameter estimation. Finally, experiments on a manipulator with permanent magnets and orthogonal cameras are conducted to demonstrate the effectiveness of the proposed control method.
{"title":"Data-Driven Orientation Tracking Control of Magnetically Actuated Capsule Endoscopy With Unknown Input Constraint","authors":"Peng Chen;Qiang Chen;Tiantian Xu;Xiongxiong He;Sheng Li","doi":"10.1109/TCST.2026.3651782","DOIUrl":"https://doi.org/10.1109/TCST.2026.3651782","url":null,"abstract":"In this article, a data-driven control framework with unknown input constraints is proposed for the orientation tracking of magnetically actuated capsule (MAC) endoscopy. First, by designing an unknown input constraint event-triggered mechanism and augmented pseudo-partial derivatives (APPDs) reset conditions, a data-driven unknown input constraint estimator is constructed to ensure that the MAC’s deflection angles remain bounded without sliding. This constraint estimator can determine the MAC’s maximum deflection angle without force measurement or computation, addressing the issue of lesions not being observed due to excessive deflection. Second, a model-free adaptive control strategy based on APPDs is proposed, where the nonlinear disturbance and pseudo-gradient are estimated together, thereby ensuring tracking accuracy without any model information and reducing the computational burden. Additionally, data model errors are incorporated into the proposed controller to improve the accuracy of adaptive parameter estimation. Finally, experiments on a manipulator with permanent magnets and orthogonal cameras are conducted to demonstrate the effectiveness of the proposed control method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"1001-1015"},"PeriodicalIF":3.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287880","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}
Pub Date : 2026-01-14DOI: 10.1109/TCST.2026.3651453
Yongwei Zhang;Shuli Lv;Kairong Liu;Quanyi Liang;Quan Quan;Zhikun She
With the rapid development of robot swarm technology and its diverse applications, navigating robot swarms through complex environments has emerged as a critical research direction. To ensure safe navigation and avoid potential collisions with obstacles, the concept of virtual tubes has been introduced to define safe and navigable regions. However, current control methods in virtual tubes face congestion issues, particularly in narrow ones with low throughput. To address these challenges, we first propose a novel control method that combines a modified artificial potential field (APF) for swarm navigation and density feedback control for distribution regulation. Then, we generate a global velocity field that not only ensures collision-free navigation but also achieves locally input-to-state stability (LISS) for density tracking. Finally, numerical simulations and realistic applications validate the effectiveness and advantages of our proposed method in navigating robot swarms through narrow virtual tubes.
{"title":"Navigating Robot Swarm Through a Virtual Tube With Flow-Adaptive Distribution Control","authors":"Yongwei Zhang;Shuli Lv;Kairong Liu;Quanyi Liang;Quan Quan;Zhikun She","doi":"10.1109/TCST.2026.3651453","DOIUrl":"https://doi.org/10.1109/TCST.2026.3651453","url":null,"abstract":"With the rapid development of robot swarm technology and its diverse applications, navigating robot swarms through complex environments has emerged as a critical research direction. To ensure safe navigation and avoid potential collisions with obstacles, the concept of virtual tubes has been introduced to define safe and navigable regions. However, current control methods in virtual tubes face congestion issues, particularly in narrow ones with low throughput. To address these challenges, we first propose a novel control method that combines a modified artificial potential field (APF) for swarm navigation and density feedback control for distribution regulation. Then, we generate a global velocity field that not only ensures collision-free navigation but also achieves locally input-to-state stability (LISS) for density tracking. Finally, numerical simulations and realistic applications validate the effectiveness and advantages of our proposed method in navigating robot swarms through narrow virtual tubes.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"1082-1088"},"PeriodicalIF":3.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287874","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}
We present an approach to address a multirobot persistent monitoring problem, where a team of agents must repeatedly survey specific points of interest (POIs) within a 2-D area. Our approach models the interest value of each POI with a heat-like dynamics. Each agent then solves online a nonlinear model predictive control (NMPC) problem to determine feasible trajectories that minimize the cumulative heat across all POIs. The trajectories are parameterized with Bézier curves, whose control points are used as optimization variables; this parametrization enables agents to efficiently communicate their optimized motions. An additional quadratic optimization layer adds safety guarantees while a central unit updates the global POIs’ map. The method has been validated in simulation and real experiments, demonstrating that the algorithm can run online and on computationally limited hardware platforms. In addition, an extensive simulation campaign compares our NMPC against a state-of-the-art baseline across 90 randomly generated scenarios with different numbers of POIs. Our NMPC outperforms the baseline along the considered metrics, attaining lower robot velocities.
{"title":"Multi-Robot Nonlinear Model Predictive Control for Persistent Monitoring","authors":"Francesca Pagano;Salvatore Marcellini;Mario Selvaggio;Vincenzo Lippiello;Fabio Ruggiero","doi":"10.1109/TCST.2025.3648511","DOIUrl":"https://doi.org/10.1109/TCST.2025.3648511","url":null,"abstract":"We present an approach to address a multirobot persistent monitoring problem, where a team of agents must repeatedly survey specific points of interest (POIs) within a 2-D area. Our approach models the interest value of each POI with a heat-like dynamics. Each agent then solves online a nonlinear model predictive control (NMPC) problem to determine feasible trajectories that minimize the cumulative heat across all POIs. The trajectories are parameterized with Bézier curves, whose control points are used as optimization variables; this parametrization enables agents to efficiently communicate their optimized motions. An additional quadratic optimization layer adds safety guarantees while a central unit updates the global POIs’ map. The method has been validated in simulation and real experiments, demonstrating that the algorithm can run online and on computationally limited hardware platforms. In addition, an extensive simulation campaign compares our NMPC against a state-of-the-art baseline across 90 randomly generated scenarios with different numbers of POIs. Our NMPC outperforms the baseline along the considered metrics, attaining lower robot velocities.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"906-918"},"PeriodicalIF":3.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288194","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}
Pub Date : 2026-01-06DOI: 10.1109/TCST.2025.3646708
Jacopo Giordano;Angelo Cenedese;Andrea Serrani
In this work, a distributed indirect adaptive controller is designed for a group of robotic agents cooperatively manipulating a common payload. Uncertainty on the model of the manipulated object and the limited actuation capabilities of the single agents can significantly impact the overall behavior of the control system. An indirect adaptive control scheme is proposed in this article to address these shortcomings. In particular, model uncertainty and loss of effectiveness of the actuators are handled in a unifying fashion by an adaptive control architecture that preserves physical consistency of the estimated inertial parameters of the manipulated object, while simultaneously providing an antiwindup mechanism for the estimated inertial parameters in case of actuator saturation. In addition, a dynamic input allocation strategy is proposed to distribute the control effort among the agents in such a way that the intrinsic input redundancy of the overall setup is exploited for dynamic optimization of additional performance criteria, including optimization of the control efforts on each agent. The stability of the closed-loop system is proven theoretically, and the performance and robustness of the control system are validated by means of comparative simulations with respect to a baseline state-of-the-art controller.
{"title":"Distributed Robust Adaptive Control of Cooperative Robotic Agents With Input Allocation","authors":"Jacopo Giordano;Angelo Cenedese;Andrea Serrani","doi":"10.1109/TCST.2025.3646708","DOIUrl":"https://doi.org/10.1109/TCST.2025.3646708","url":null,"abstract":"In this work, a distributed indirect adaptive controller is designed for a group of robotic agents cooperatively manipulating a common payload. Uncertainty on the model of the manipulated object and the limited actuation capabilities of the single agents can significantly impact the overall behavior of the control system. An indirect adaptive control scheme is proposed in this article to address these shortcomings. In particular, model uncertainty and loss of effectiveness of the actuators are handled in a unifying fashion by an adaptive control architecture that preserves physical consistency of the estimated inertial parameters of the manipulated object, while simultaneously providing an antiwindup mechanism for the estimated inertial parameters in case of actuator saturation. In addition, a dynamic input allocation strategy is proposed to distribute the control effort among the agents in such a way that the intrinsic input redundancy of the overall setup is exploited for dynamic optimization of additional performance criteria, including optimization of the control efforts on each agent. The stability of the closed-loop system is proven theoretically, and the performance and robustness of the control system are validated by means of comparative simulations with respect to a baseline state-of-the-art controller.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"963-978"},"PeriodicalIF":3.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287884","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}
Pub Date : 2026-01-01DOI: 10.1109/TCST.2025.3647936
Hao Chen;He Bai
Wind estimation can be widely used in the planning and control of uncrewed aerial systems and for meteorology and environmental studies. Fusion of wind estimates from multiple quadcopters can result in improved accuracy of wind estimation that may not be attainable by a single quadcopter. In this article, we consider a multi-quadcopter wind field estimation problem. We design an invariant extended Kalman filter (IEKF) and integrate it with a sequential covariance intersection (SCI) algorithm to fuse wind estimates from multiple quadcopters. We also develop baseline algorithms such as a least-squares method and a multiplicative-EKF (MEKF) SCI algorithm. The effectiveness of the proposed IEKF-SCI algorithm is compared with the baseline algorithms and demonstrated in simulations under various wind field models.
{"title":"Distributed Estimation and Fusion for Multi-Quadcopter Wind Field Estimation","authors":"Hao Chen;He Bai","doi":"10.1109/TCST.2025.3647936","DOIUrl":"https://doi.org/10.1109/TCST.2025.3647936","url":null,"abstract":"Wind estimation can be widely used in the planning and control of uncrewed aerial systems and for meteorology and environmental studies. Fusion of wind estimates from multiple quadcopters can result in improved accuracy of wind estimation that may not be attainable by a single quadcopter. In this article, we consider a multi-quadcopter wind field estimation problem. We design an invariant extended Kalman filter (IEKF) and integrate it with a sequential covariance intersection (SCI) algorithm to fuse wind estimates from multiple quadcopters. We also develop baseline algorithms such as a least-squares method and a multiplicative-EKF (MEKF) SCI algorithm. The effectiveness of the proposed IEKF-SCI algorithm is compared with the baseline algorithms and demonstrated in simulations under various wind field models.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"934-946"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288164","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}
MPC has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehicle thermal management system (TMS) with integrated thermal energy storage (TES), also referred to as a hybrid TMS. Here, hybrid refers to the ability to achieve cooling through a conventional heat exchanger or via melting of a phase change material (PCM), or both. Given increased electrification in vehicle platforms, more stringent performance specifications are being placed on TMS, in turn requiring more advanced control methods. In this article, we present the design and real-time implementation of a nonlinear model predictive controller with 77 states on an experimental hybrid TMS testbed. We show how, in spite of high dimensions and stiff dynamics, an explicit integration method can be obtained by finding a suitable linear system at each time step within the MPC horizon online. This integration method further allows the first-order gradients to be calculated with minimal additional computational cost. Through simulated and experimental results, we demonstrate the utility of the proposed solution method and the benefits of TES for mitigating highly transient heat loads.
{"title":"Nonlinear Model Predictive Control of a Hybrid Thermal Management System","authors":"Demetrius Gulewicz;Uduak Inyang-Udoh;Trevor Bird;Neera Jain","doi":"10.1109/TCST.2025.3646704","DOIUrl":"https://doi.org/10.1109/TCST.2025.3646704","url":null,"abstract":"MPC has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehicle thermal management system (TMS) with integrated thermal energy storage (TES), also referred to as a hybrid TMS. Here, hybrid refers to the ability to achieve cooling through a conventional heat exchanger or via melting of a phase change material (PCM), or both. Given increased electrification in vehicle platforms, more stringent performance specifications are being placed on TMS, in turn requiring more advanced control methods. In this article, we present the design and real-time implementation of a nonlinear model predictive controller with 77 states on an experimental hybrid TMS testbed. We show how, in spite of high dimensions and stiff dynamics, an explicit integration method can be obtained by finding a suitable linear system at each time step within the MPC horizon online. This integration method further allows the first-order gradients to be calculated with minimal additional computational cost. Through simulated and experimental results, we demonstrate the utility of the proposed solution method and the benefits of TES for mitigating highly transient heat loads.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"892-905"},"PeriodicalIF":3.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288163","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}
Pub Date : 2025-12-22DOI: 10.1109/TCST.2025.3643507
Wenqu Li;Zhi-Wei Liu;Ming Chi;Yuanzheng Li;Yan-Wu Wang
Voltage regulation is of vital importance for the safe operation of distribution grids, particularly in the presence of increasing integration of renewable energy resources. This article proposes a distributed online feedback optimization method that leverages real-time measurements to address the challenges of coordinated voltage regulation in distribution grids. To account for potential shortcomings arising from incomplete model information or topological changes, a real-time sensitivity estimation strategy is introduced. In addition, a persistent excitation strategy is employed to ensure the reliability of estimation results. Extensive simulations demonstrate the effectiveness and applicability of the proposed algorithm under various scenarios, highlighting its robustness against model mismatches and external disturbances.
{"title":"Distributed Online Feedback Optimization With Real-Time Sensitivity Estimation for Coordinated Voltage Regulation in Distribution Grids","authors":"Wenqu Li;Zhi-Wei Liu;Ming Chi;Yuanzheng Li;Yan-Wu Wang","doi":"10.1109/TCST.2025.3643507","DOIUrl":"https://doi.org/10.1109/TCST.2025.3643507","url":null,"abstract":"Voltage regulation is of vital importance for the safe operation of distribution grids, particularly in the presence of increasing integration of renewable energy resources. This article proposes a distributed online feedback optimization method that leverages real-time measurements to address the challenges of coordinated voltage regulation in distribution grids. To account for potential shortcomings arising from incomplete model information or topological changes, a real-time sensitivity estimation strategy is introduced. In addition, a persistent excitation strategy is employed to ensure the reliability of estimation results. Extensive simulations demonstrate the effectiveness and applicability of the proposed algorithm under various scenarios, highlighting its robustness against model mismatches and external disturbances.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 2","pages":"990-1000"},"PeriodicalIF":3.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287875","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}
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