Pub Date : 2025-04-28DOI: 10.1109/TCST.2025.3560546
Andres Cordoba-Pacheco;Lorenzo Petrucci;Matteo Filippone;Fredy Ruiz
Optimizing resources in industrial processes requires precise control strategies but often relies on complex mathematical models that are difficult to develop, posing challenges for traditional control methods. To solve this problem, an innovative methodology for fine-tuning controllers is presented based on the set-membership data-driven (SMDD) approach. This novel technique can change the traditional methods used for tuning controllers in the industry by leveraging real-world data and presents a transformative alternative in controller design. To demonstrate the effectiveness of the proposed methodology, it is applied in a controller design process for a real battery energy storage system (BESS) with a peak capacity of 267 kW, which is connected to the main grid. The study conducts a detailed comparative analysis between conventional tuning techniques based on simple open-loop step response characteristics, an existing controller for the BESS tuned by trial and error, and the SMDD methodology. The data-driven approach shows significant improvements in the system performance with reductions of up to 18% in step response time and overshoots under 1%, proving its robustness at dealing with time-variant communication delays and fixed controller structures. The study concludes that the SMDD methodology emerges as a tool with the potential for tuning controllers in real-world systems.
{"title":"Data-Driven Controller Tuning for Battery Energy Storage Systems: A Set-Membership Approach","authors":"Andres Cordoba-Pacheco;Lorenzo Petrucci;Matteo Filippone;Fredy Ruiz","doi":"10.1109/TCST.2025.3560546","DOIUrl":"https://doi.org/10.1109/TCST.2025.3560546","url":null,"abstract":"Optimizing resources in industrial processes requires precise control strategies but often relies on complex mathematical models that are difficult to develop, posing challenges for traditional control methods. To solve this problem, an innovative methodology for fine-tuning controllers is presented based on the set-membership data-driven (SMDD) approach. This novel technique can change the traditional methods used for tuning controllers in the industry by leveraging real-world data and presents a transformative alternative in controller design. To demonstrate the effectiveness of the proposed methodology, it is applied in a controller design process for a real battery energy storage system (BESS) with a peak capacity of 267 kW, which is connected to the main grid. The study conducts a detailed comparative analysis between conventional tuning techniques based on simple open-loop step response characteristics, an existing controller for the BESS tuned by trial and error, and the SMDD methodology. The data-driven approach shows significant improvements in the system performance with reductions of up to 18% in step response time and overshoots under 1%, proving its robustness at dealing with time-variant communication delays and fixed controller structures. The study concludes that the SMDD methodology emerges as a tool with the potential for tuning controllers in real-world systems.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1757-1770"},"PeriodicalIF":3.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891246","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 : 2025-04-22DOI: 10.1109/TCST.2025.3557665
David J. N. Limebeer;R. Austin Dollar
This article studies the impact of aerodynamic drafting on optimal overtaking maneuvers in NASCAR. Two interlinked optimal control problems (OCPs) are employed: one for computing the strategy of the overtaken car and the other for the overtaking vehicle. Intervehicular interaction is due to a combination of aerodynamic drafting and collision avoidance. In the formulation presented, the cross coupling between these OCPs is unidirectional, with collision avoidance the responsibility of the overtaking vehicle. The racing vehicles are assumed to travel on a highly banked 3-D closed-circuit track. The overtaking car is subject to near-field aerodynamic interactions (“drafting”) that have a significant impact on the overtaking vehicle’s strategy. Results are presented that illustrate overtaking strategies with and without drafting; differences in the two strategies highlight the importance of drafting influences. The results are based on a Generation 7 (Gen 7) NASCAR driven on the Darlington Raceway.
{"title":"Optimal Overtaking in NASCAR","authors":"David J. N. Limebeer;R. Austin Dollar","doi":"10.1109/TCST.2025.3557665","DOIUrl":"https://doi.org/10.1109/TCST.2025.3557665","url":null,"abstract":"This article studies the impact of aerodynamic drafting on optimal overtaking maneuvers in NASCAR. Two interlinked optimal control problems (OCPs) are employed: one for computing the strategy of the overtaken car and the other for the overtaking vehicle. Intervehicular interaction is due to a combination of aerodynamic drafting and collision avoidance. In the formulation presented, the cross coupling between these OCPs is unidirectional, with collision avoidance the responsibility of the overtaking vehicle. The racing vehicles are assumed to travel on a highly banked 3-D closed-circuit track. The overtaking car is subject to near-field aerodynamic interactions (“drafting”) that have a significant impact on the overtaking vehicle’s strategy. Results are presented that illustrate overtaking strategies with and without drafting; differences in the two strategies highlight the importance of drafting influences. The results are based on a Generation 7 (Gen 7) NASCAR driven on the Darlington Raceway.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1785-1798"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891199","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 : 2025-04-21DOI: 10.1109/TCST.2025.3556236
Suhao Feng;Deheng Cai;Jing Chen;Dawei Shi;Ling Shi;Wei Liu;Linong Ji
Accurate and physiologically interpretable postprandial glucose prediction is of importance in diabetes self-management. In this work, the problem of personalized glucose prediction is considered, and an interpretable postprandial glucose trajectory prediction framework is proposed based on kernel-based system identification under physiological constraints. Considering treatment requirements in inpatient scenarios, an online prediction update mechanism is developed to deal with intrasubject variability. Through incorporating physiological constraints abstracted from linearized compartmental models, a posterior performance assessment and adaptation mechanism is designed to guarantee the interpretability of the predicted glucose responses. The proposed method is evaluated through clinical data from type 1 diabetes mellitus (T1DM) subjects, and the results indicate that the proposed method can achieve physiologically interpretable postprandial glucose trajectory prediction with satisfactory performance.
{"title":"Kernel-Based Learning With Adaptive Physiological Constraints for Personalized Postprandial Glucose Prediction","authors":"Suhao Feng;Deheng Cai;Jing Chen;Dawei Shi;Ling Shi;Wei Liu;Linong Ji","doi":"10.1109/TCST.2025.3556236","DOIUrl":"https://doi.org/10.1109/TCST.2025.3556236","url":null,"abstract":"Accurate and physiologically interpretable postprandial glucose prediction is of importance in diabetes self-management. In this work, the problem of personalized glucose prediction is considered, and an interpretable postprandial glucose trajectory prediction framework is proposed based on kernel-based system identification under physiological constraints. Considering treatment requirements in inpatient scenarios, an online prediction update mechanism is developed to deal with intrasubject variability. Through incorporating physiological constraints abstracted from linearized compartmental models, a posterior performance assessment and adaptation mechanism is designed to guarantee the interpretability of the predicted glucose responses. The proposed method is evaluated through clinical data from type 1 diabetes mellitus (T1DM) subjects, and the results indicate that the proposed method can achieve physiologically interpretable postprandial glucose trajectory prediction with satisfactory performance.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1712-1726"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891195","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 : 2025-04-08DOI: 10.1109/TCST.2025.3552618
Chris van der Ploeg;Pedro Vieira Oliveira;Emilia Silvas;Peyman Mohajerin Esfahani;Nathan van de Wouw
To increase system robustness and autonomy, in this article, we propose a nonlinear fault estimation filter for a class of linear dynamical systems, subject to structured uncertainty, measurement noise, and system delays, in the presence of additive and multiplicative faults. The proposed filter architecture combines tools from model-based control approaches, regression techniques, and convex optimization. The proposed method estimates the additive and multiplicative faults using a linear residual generator combined with nonlinear regression. An offline simulator allows us to numerically characterize the mismatch between an assumed linear model and a range of alternative linear models that exhibit different levels of structured uncertainty. Moreover, we show how the performance bounds of the estimator, valid in the absence of uncertainty, can be used to determine appropriate countermeasures for measurement noise. In the scope of this work, we focus particularly on a fault estimation problem for Society of Automotive Engineers (SAEs) level 4 automated vehicles, which must remain operational in various cases and cannot rely on the driver. The proposed approach is demonstrated in simulations and in an experimental setting, where it is shown that additive and multiplicative faults can be estimated in a real vehicle under the influence of model uncertainty, measurement noise, and delay.
{"title":"Robust Fault Estimation With Structured Uncertainty: Scalable Algorithms and Experimental Validation in Automated Vehicles","authors":"Chris van der Ploeg;Pedro Vieira Oliveira;Emilia Silvas;Peyman Mohajerin Esfahani;Nathan van de Wouw","doi":"10.1109/TCST.2025.3552618","DOIUrl":"https://doi.org/10.1109/TCST.2025.3552618","url":null,"abstract":"To increase system robustness and autonomy, in this article, we propose a nonlinear fault estimation filter for a class of linear dynamical systems, subject to structured uncertainty, measurement noise, and system delays, in the presence of additive and multiplicative faults. The proposed filter architecture combines tools from model-based control approaches, regression techniques, and convex optimization. The proposed method estimates the additive and multiplicative faults using a linear residual generator combined with nonlinear regression. An offline simulator allows us to numerically characterize the mismatch between an assumed linear model and a range of alternative linear models that exhibit different levels of structured uncertainty. Moreover, we show how the performance bounds of the estimator, valid in the absence of uncertainty, can be used to determine appropriate countermeasures for measurement noise. In the scope of this work, we focus particularly on a fault estimation problem for Society of Automotive Engineers (SAEs) level 4 automated vehicles, which must remain operational in various cases and cannot rely on the driver. The proposed approach is demonstrated in simulations and in an experimental setting, where it is shown that additive and multiplicative faults can be estimated in a real vehicle under the influence of model uncertainty, measurement noise, and delay.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1651-1666"},"PeriodicalIF":3.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891307","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 : 2025-04-02DOI: 10.1109/TCST.2025.3550033
Jean Gonzalez Silva;Twan Keijzer;Alexander Julian Gallo;Riccardo Ferrari;Jan-Willem van Wingerden
High penetration of wind energy is pushing wind farms (WFs) to offer grid support capabilities, such as active power tracking. One of the main challenges in active power tracking for WFs is the interaction of wind turbines (WTs) through their wakes. This reduces the available wind in downstream WTs, leading them to saturation, while also affecting structural loading. With the increasing number of WTs in individual WFs, the computational and communication complexity of implementing centralized control architectures grows, posing challenges for real-world applications. In this article, we present a novel distributed control approach for active power tracking for WFs, namely multirate consensus-based distributed control (MCDC). The MCDC is designed to ensure that tracking errors caused by WT saturation are equally compensated throughout the WF, while only requiring local information exchanges between WTs. Furthermore, the proposed controller ensures that WT aerodynamic loading is balanced across the WF in a distributed manner. Finally, the overall power reference is distributed via a leader-follower consensus algorithm, resulting in a fully distributed approach. Our control approach facilitates the WF modularity and sparsity, which reduces the costs associated with control design and its applicability. Throughout this article, we demonstrate the effectiveness of the proposed MCDC through high-fidelity simulations, presenting performance comparable to the centralized control.
{"title":"Multirate Consensus-Based Distributed Control for Large-Scale Wind Farms","authors":"Jean Gonzalez Silva;Twan Keijzer;Alexander Julian Gallo;Riccardo Ferrari;Jan-Willem van Wingerden","doi":"10.1109/TCST.2025.3550033","DOIUrl":"https://doi.org/10.1109/TCST.2025.3550033","url":null,"abstract":"High penetration of wind energy is pushing wind farms (WFs) to offer grid support capabilities, such as active power tracking. One of the main challenges in active power tracking for WFs is the interaction of wind turbines (WTs) through their wakes. This reduces the available wind in downstream WTs, leading them to saturation, while also affecting structural loading. With the increasing number of WTs in individual WFs, the computational and communication complexity of implementing centralized control architectures grows, posing challenges for real-world applications. In this article, we present a novel distributed control approach for active power tracking for WFs, namely multirate consensus-based distributed control (MCDC). The MCDC is designed to ensure that tracking errors caused by WT saturation are equally compensated throughout the WF, while only requiring local information exchanges between WTs. Furthermore, the proposed controller ensures that WT aerodynamic loading is balanced across the WF in a distributed manner. Finally, the overall power reference is distributed via a leader-follower consensus algorithm, resulting in a fully distributed approach. Our control approach facilitates the WF modularity and sparsity, which reduces the costs associated with control design and its applicability. Throughout this article, we demonstrate the effectiveness of the proposed MCDC through high-fidelity simulations, presenting performance comparable to the centralized control.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1572-1585"},"PeriodicalIF":3.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891295","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 : 2025-04-01DOI: 10.1109/TCST.2025.3549347
Andrés González;Luis Ovalle;Leonid Fridman
This article studies the behavior of barrier function adaptation (BFA) in the presence of bounded deterministic noises with the known upper bound, both theoretically and experimentally. It is shown that, in the case of discontinuous noises, the predefined performance of the output can be lost. Nevertheless, if the barrier function width (BFW) is selected in accordance to the upper bound of the perturbation, the prescribed performance of the output can be asserted. In the case of continuous noises, BFW needs to be selected at least twice as big as the bound of the measurement noise, i.e., the knowledge of the upper bound of the perturbations is not needed, and the predefined performance of the output is ensured. In the case of Lipschitz continuous noises, BFW can be selected arbitrary, and the system output will be kept in BFW. An experimental study, considering different classes of noises, is designed and implemented on a brushed dc motor to illustrate the conclusions drawn from the theoretical analysis.
{"title":"Is it Reasonable to Implement Barrier Function Adaptation Under Sensor Noise?","authors":"Andrés González;Luis Ovalle;Leonid Fridman","doi":"10.1109/TCST.2025.3549347","DOIUrl":"https://doi.org/10.1109/TCST.2025.3549347","url":null,"abstract":"This article studies the behavior of barrier function adaptation (BFA) in the presence of bounded deterministic noises with the known upper bound, both theoretically and experimentally. It is shown that, in the case of discontinuous noises, the predefined performance of the output can be lost. Nevertheless, if the barrier function width (BFW) is selected in accordance to the upper bound of the perturbation, the prescribed performance of the output can be asserted. In the case of continuous noises, BFW needs to be selected at least twice as big as the bound of the measurement noise, i.e., the knowledge of the upper bound of the perturbations is not needed, and the predefined performance of the output is ensured. In the case of Lipschitz continuous noises, BFW can be selected arbitrary, and the system output will be kept in BFW. An experimental study, considering different classes of noises, is designed and implemented on a brushed dc motor to illustrate the conclusions drawn from the theoretical analysis.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1627-1639"},"PeriodicalIF":3.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891296","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 : 2025-04-01DOI: 10.1109/TCST.2025.3550807
Faizan Shahid;Hao Luo;Yuchen Jiang;Muhammad Noman Hasan
This article presents an adaptive fault-tolerant attitude control (FTAC) system designed for flexible spacecraft with minimum computational burden. It offers exceptional energy efficiency and robust anti-unwinding capabilities. First, the spacecraft system dynamics are redefined for the actuator’s performance matrix by considering the flexible appendage’s vibration effects, inertial uncertainties, external disturbances, and faults associated with the actuators (misalignment, bias, and loss of effectiveness). The control scheme is conceived by introducing a modified nonsingular terminal sliding mode (MNTSM) surface with an imposed constraint, and a modal vibration observer (MVO) estimates vibrations induced by flexible appendages. Then, based on the designed sliding manifold, an adaptive law and switching function are employed to estimate and compensate lumped disturbances. Subsequently, an anti-unwinding finite-time adaptive sliding mode (AFASM) fault-tolerant control law is proposed. The remarkable characteristic of the proposed control is its ability to simultaneously handle unwinding and modal vibrations in the presence of actuator faults and demonstrate finite-time precise attitude tracking. The stability and finite-time convergence of the proposed control is established using the Lyapunov and finite-time theory. Finally, numerical simulations illustrate the effectiveness and efficacy of the propounded control for fault-free and faulty actuator scenarios.
{"title":"Finite-Time Adaptive Sliding Mode Fault-Tolerant Attitude Control for Flexible Spacecraft","authors":"Faizan Shahid;Hao Luo;Yuchen Jiang;Muhammad Noman Hasan","doi":"10.1109/TCST.2025.3550807","DOIUrl":"https://doi.org/10.1109/TCST.2025.3550807","url":null,"abstract":"This article presents an adaptive fault-tolerant attitude control (FTAC) system designed for flexible spacecraft with minimum computational burden. It offers exceptional energy efficiency and robust anti-unwinding capabilities. First, the spacecraft system dynamics are redefined for the actuator’s performance matrix by considering the flexible appendage’s vibration effects, inertial uncertainties, external disturbances, and faults associated with the actuators (misalignment, bias, and loss of effectiveness). The control scheme is conceived by introducing a modified nonsingular terminal sliding mode (MNTSM) surface with an imposed constraint, and a modal vibration observer (MVO) estimates vibrations induced by flexible appendages. Then, based on the designed sliding manifold, an adaptive law and switching function are employed to estimate and compensate lumped disturbances. Subsequently, an anti-unwinding finite-time adaptive sliding mode (AFASM) fault-tolerant control law is proposed. The remarkable characteristic of the proposed control is its ability to simultaneously handle unwinding and modal vibrations in the presence of actuator faults and demonstrate finite-time precise attitude tracking. The stability and finite-time convergence of the proposed control is established using the Lyapunov and finite-time theory. Finally, numerical simulations illustrate the effectiveness and efficacy of the propounded control for fault-free and faulty actuator scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1700-1711"},"PeriodicalIF":3.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891194","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 : 2025-03-31DOI: 10.1109/TCST.2025.3552543
Tianyang Li;Ying Meng;Lixin Tang;Yuxuan Zhang
This article investigates a dynamic slab assignment problem (DSAP) that arises in the slab production process of steel industry. In DSAP, a set of slabs and orders arrive dynamically at each time step of a planning period, and their information cannot be observed in advance. For a planning period, a series of decisions need to be made on allocating the slabs to customer orders, self-designed orders, or holding them in inventory to maximize total rewards. To address DSAP effectively, we formulate a Markov decision process (MDP) model and propose a deep reinforcement learning algorithm combined with an integer programming (DRLIP) model. DRLIP decomposes each decision time step into two stages, i.e., dynamic selection stage and static assignment stage. The dynamic selection stage primarily uses a double-pointer network (DPN) to select the slabs and orders to be involved in matching. In the static assignment stage, an extension of a multiknapsack problem is constructed based on the selected slabs and orders. We formulate an integer programming (IP) model to solve this multiknapsack problem for obtaining an optimal assignment decision, which in turn provides a reward for each time step. To evaluate the effectiveness of DRLIP, we use a global method, three advanced heuristic methods, and a scenario tree method for comparison on practical and randomly generated problem instances. Computational results show that DRLIP yields a mean gap of 21.5% versus the optimum from the global method and outperforms the other comparison methods.
{"title":"Deep Reinforcement Learning With Integer Optimization for Dynamic Slab Assignment Problem","authors":"Tianyang Li;Ying Meng;Lixin Tang;Yuxuan Zhang","doi":"10.1109/TCST.2025.3552543","DOIUrl":"https://doi.org/10.1109/TCST.2025.3552543","url":null,"abstract":"This article investigates a dynamic slab assignment problem (DSAP) that arises in the slab production process of steel industry. In DSAP, a set of slabs and orders arrive dynamically at each time step of a planning period, and their information cannot be observed in advance. For a planning period, a series of decisions need to be made on allocating the slabs to customer orders, self-designed orders, or holding them in inventory to maximize total rewards. To address DSAP effectively, we formulate a Markov decision process (MDP) model and propose a deep reinforcement learning algorithm combined with an integer programming (DRLIP) model. DRLIP decomposes each decision time step into two stages, i.e., dynamic selection stage and static assignment stage. The dynamic selection stage primarily uses a double-pointer network (DPN) to select the slabs and orders to be involved in matching. In the static assignment stage, an extension of a multiknapsack problem is constructed based on the selected slabs and orders. We formulate an integer programming (IP) model to solve this multiknapsack problem for obtaining an optimal assignment decision, which in turn provides a reward for each time step. To evaluate the effectiveness of DRLIP, we use a global method, three advanced heuristic methods, and a scenario tree method for comparison on practical and randomly generated problem instances. Computational results show that DRLIP yields a mean gap of 21.5% versus the optimum from the global method and outperforms the other comparison methods.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1586-1600"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891245","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 : 2025-03-31DOI: 10.1109/TCST.2025.3553000
Seyyed Shaho Alaviani;Marc W. van Iersel;Javad Mohammadpour Velni
This brief aims at developing an optimal supplemental lighting control method using light-emitting diode (LED) lamps in controlled environmental agriculture. Existing results in the literature have not contemplated the problem under supplemental light distribution and/or multicrops in greenhouses where “supplemental light distribution” is referred to the horizontal pattern it casts and amount of light that reaches certain vertical angles and “multicrops” is defined as cultivating different plants in a greenhouse. To the best knowledge of the authors, this work is the first that fills the aforementioned gap. To address the problem, an approach for sectioning LED lamps’ control sources in a greenhouse is proposed, and an optimization problem is formulated for minimizing the electricity cost, light intensity regulation, and/or peak of supplemental lights minimization under both supplemental light distribution and multicrops cases for which a learning-based algorithm is given under sunlight prediction (which is based on the Markov process). A switched algorithm is then proposed to reduce electricity cost to the lowest possible. Finally, a numerical example is provided in order to demonstrate the result and its advantages.
{"title":"Optimal Supplemental Lighting Control Using LED Lamps in Greenhouses Considering Multicrops and Spatial Supplemental Light Distribution","authors":"Seyyed Shaho Alaviani;Marc W. van Iersel;Javad Mohammadpour Velni","doi":"10.1109/TCST.2025.3553000","DOIUrl":"https://doi.org/10.1109/TCST.2025.3553000","url":null,"abstract":"This brief aims at developing an <italic>optimal</i> supplemental lighting control method using light-emitting diode (LED) lamps in controlled environmental agriculture. Existing results in the literature have <italic>not</i> contemplated the problem under <italic>supplemental light distribution</i> and/or <italic>multicrops</i> in greenhouses where “supplemental light distribution” is referred to the horizontal pattern it casts and amount of light that reaches certain vertical angles and “multicrops” is defined as cultivating different plants in a greenhouse. To the best knowledge of the authors, this work is the <italic>first</i> that fills the aforementioned gap. To address the problem, an approach for <italic>sectioning</i> LED lamps’ control sources in a greenhouse is proposed, and an optimization problem is formulated for minimizing the electricity cost, light intensity regulation, and/or peak of supplemental lights minimization under <italic>both</i> supplemental light distribution and multicrops cases for which a learning-based algorithm is given under sunlight prediction (which is based on the Markov process). A switched algorithm is then proposed to reduce electricity cost to the lowest possible. Finally, a numerical example is provided in order to demonstrate the result and its advantages.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1946-1952"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891042","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}
In this work, we address the problem of precise motion planning and control of flexible-link robots for throwing small objects. Thanks to lightweight materials and elastic bodies, flexible robots can perform fast motions with few actuators. However, they need a planning and control strategy capable of exploiting the robot’s elasticity, negotiating with the system’s underactuation, and compensating for the model’s uncertainties. To solve this challenging task, we 1) compare multiple discrete models for continuum robots’ dynamics and, after selecting a lumped-parameter (LPs) model, experimentally identify its parameters; 2) plan the robot motion via a differential-dynamic-programming-based strategy tailored for flexible-link robots; and 3) employ an iterative learning control (ILC) approach to close the reality-gap. Combining these three steps allows us to execute precise throwing tasks with flexible-link robots. The strategy’s effectiveness has been validated via simulations and experiments with varying trajectories and payloads. We applied the aforementioned approach to realize a throwing motion with a fishing rod for environmental monitoring.
{"title":"Fishing for Data: Modeling, Optimal Planning, and Iterative Learning Control for Flexible Link Robots","authors":"Michele Pierallini;Ramesh Krishnan Muttathil Gopanunni;Franco Angelini;Antonio Bicchi;Manolo Garabini","doi":"10.1109/TCST.2025.3550030","DOIUrl":"https://doi.org/10.1109/TCST.2025.3550030","url":null,"abstract":"In this work, we address the problem of precise motion planning and control of flexible-link robots for throwing small objects. Thanks to lightweight materials and elastic bodies, flexible robots can perform fast motions with few actuators. However, they need a planning and control strategy capable of exploiting the robot’s elasticity, negotiating with the system’s underactuation, and compensating for the model’s uncertainties. To solve this challenging task, we 1) compare multiple discrete models for continuum robots’ dynamics and, after selecting a lumped-parameter (LPs) model, experimentally identify its parameters; 2) plan the robot motion via a differential-dynamic-programming-based strategy tailored for flexible-link robots; and 3) employ an iterative learning control (ILC) approach to close the reality-gap. Combining these three steps allows us to execute precise throwing tasks with flexible-link robots. The strategy’s effectiveness has been validated via simulations and experiments with varying trajectories and payloads. We applied the aforementioned approach to realize a throwing motion with a fishing rod for environmental monitoring.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1667-1683"},"PeriodicalIF":3.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891021","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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