Pub Date : 2025-12-22DOI: 10.1109/LCSYS.2025.3647098
Yutong Li;Ilya Kolmanovsky
Proton Pump Inhibitors (PPIs) are the standard of care for gastric acid disorders but carry significant risks when administered chronically at high doses. Precise long-term control of gastric acidity is challenged by the impracticality of invasive gastric acid monitoring beyond 72 hours and wide inter-patient variability. We propose a noninvasive, symptom-based framework that tailors PPI dosing solely on patient-reported reflux and digestive symptom patterns. A Bayesian Neural Network (BNN) prediction model learns to predict patient symptoms and quantifies its uncertainty from historical symptom scores, meal, and PPIs intake data. These probabilistic forecasts feed a chance-constrained Model Predictive Control (MPC) algorithm that dynamically computes future PPI doses to minimize drug usage while enforcing acid suppression with high confidence—without any direct acid measurement. In silico studies over diverse dietary schedules and virtual patient profiles demonstrate that our learning-augmented MPC reduces total PPI consumption by 65% compared to standard fixed regimens, while maintaining acid suppression with at least 95% probability. The proposed approach offers a practical path to personalized PPI therapy, minimizing treatment burden and overdose risk without invasive sensors.
{"title":"Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control","authors":"Yutong Li;Ilya Kolmanovsky","doi":"10.1109/LCSYS.2025.3647098","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3647098","url":null,"abstract":"Proton Pump Inhibitors (PPIs) are the standard of care for gastric acid disorders but carry significant risks when administered chronically at high doses. Precise long-term control of gastric acidity is challenged by the impracticality of invasive gastric acid monitoring beyond 72 hours and wide inter-patient variability. We propose a noninvasive, symptom-based framework that tailors PPI dosing solely on patient-reported reflux and digestive symptom patterns. A Bayesian Neural Network (BNN) prediction model learns to predict patient symptoms and quantifies its uncertainty from historical symptom scores, meal, and PPIs intake data. These probabilistic forecasts feed a chance-constrained Model Predictive Control (MPC) algorithm that dynamically computes future PPI doses to minimize drug usage while enforcing acid suppression with high confidence—without any direct acid measurement. In silico studies over diverse dietary schedules and virtual patient profiles demonstrate that our learning-augmented MPC reduces total PPI consumption by 65% compared to standard fixed regimens, while maintaining acid suppression with at least 95% probability. The proposed approach offers a practical path to personalized PPI therapy, minimizing treatment burden and overdose risk without invasive sensors.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3023-3028"},"PeriodicalIF":2.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1109/LCSYS.2025.3645824
Abdullah Tokmak;Thomas B. Schön;Dominik Baumann
Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. In this letter, we propose a straightforward yet rigorous approach for safe BO across noise models, including homoscedastic sub-Gaussian and heteroscedastic heavy-tailed distributions. We provide a high-probability bound on the measurement noise via the scenario approach, integrate these bounds into high probability confidence intervals, and prove safety and optimality for our proposed safe BO algorithm. We deploy our algorithm in synthetic examples and in tuning a controller for the Franka Emika manipulator in simulation.
{"title":"Safe Bayesian Optimization Across Noise Models via Scenario Programming","authors":"Abdullah Tokmak;Thomas B. Schön;Dominik Baumann","doi":"10.1109/LCSYS.2025.3645824","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645824","url":null,"abstract":"Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. In this letter, we propose a straightforward yet rigorous approach for safe BO across noise models, including homoscedastic sub-Gaussian and heteroscedastic heavy-tailed distributions. We provide a high-probability bound on the measurement noise via the scenario approach, integrate these bounds into high probability confidence intervals, and prove safety and optimality for our proposed safe BO algorithm. We deploy our algorithm in synthetic examples and in tuning a controller for the Franka Emika manipulator in simulation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3029-3034"},"PeriodicalIF":2.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1109/LCSYS.2025.3645829
Juan Javier Palacios Roman;Matthijs van Berkel;Maurice Heemels;Thijs van Keulen
In this letter, we show uniform semi-global practical asymptotic stability of fast extremum seeking control (ESC) for single-input single-output Wiener systems. While classic ESC requires a time-scale separation between plant and dither, the fast ESC method circumvents this time-scale separation by exploiting limited knowledge of the frequency response of the linear part of the Wiener system, thereby achieving faster convergence. The assumptions under which the fast ESC method works are relaxed compared to existing work and explicit bounds on the design parameters of the fast ESC scheme are provided. A numerical case study illustrates the enhanced convergence and the robustness of the fast ESC method.
{"title":"Stability Analysis of Fast Extremum Seeking Control for Wiener Systems Using Online Complex Curve Fitting","authors":"Juan Javier Palacios Roman;Matthijs van Berkel;Maurice Heemels;Thijs van Keulen","doi":"10.1109/LCSYS.2025.3645829","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645829","url":null,"abstract":"In this letter, we show uniform semi-global practical asymptotic stability of fast extremum seeking control (ESC) for single-input single-output Wiener systems. While classic ESC requires a time-scale separation between plant and dither, the fast ESC method circumvents this time-scale separation by exploiting limited knowledge of the frequency response of the linear part of the Wiener system, thereby achieving faster convergence. The assumptions under which the fast ESC method works are relaxed compared to existing work and explicit bounds on the design parameters of the fast ESC scheme are provided. A numerical case study illustrates the enhanced convergence and the robustness of the fast ESC method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2993-2998"},"PeriodicalIF":2.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1109/LCSYS.2025.3645825
Sayan Chakraborty;Zhong-Ping Jiang
This letter studies the resilience of cyber-physical systems under denial-of-service attacks. We develop a novel framework for resilient control that avoids the need for detailed information about the system or attacker dynamics by treating the plant–attacker interaction as an interconnected system. Using small-gain analysis and switching systems theory, we derive explicit resilience conditions, and employ reinforcement learning to synthesize an optimal policy directly from input–state data, estimating the required small-gain bounds in a data-driven manner. A numerical example illustrates the effectiveness of the proposed approach.
{"title":"A Small-Gain Look at Cyber-Physical Security","authors":"Sayan Chakraborty;Zhong-Ping Jiang","doi":"10.1109/LCSYS.2025.3645825","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645825","url":null,"abstract":"This letter studies the resilience of cyber-physical systems under denial-of-service attacks. We develop a novel framework for resilient control that avoids the need for detailed information about the system or attacker dynamics by treating the plant–attacker interaction as an interconnected system. Using small-gain analysis and switching systems theory, we derive explicit resilience conditions, and employ reinforcement learning to synthesize an optimal policy directly from input–state data, estimating the required small-gain bounds in a data-driven manner. A numerical example illustrates the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3035-3040"},"PeriodicalIF":2.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Risk sensitivity has become a central theme in reinforcement learning (RL), where convex risk measures and robust formulations provide principled ways to model preferences beyond expected return. Recent extensions to multi-agent RL (MARL) have largely emphasized the risk-averse setting, prioritizing robustness to uncertainty. In cooperative MARL, however, such conservatism often leads to suboptimal equilibria, and a parallel line of work has shown that optimism can promote cooperation. Existing optimistic methods, though effective in practice, are typically heuristic and lack theoretical grounding. Building on the dual representation for convex risk measures, we propose a principled framework that interprets risk-seeking objectives as optimism. We introduce optimistic value functions, which formalize optimism as divergence-penalized risk-seeking evaluations. Building on this foundation, we derive a policy-gradient theorem for optimistic value functions, including explicit formulas for the entropic risk/KL-penalty setting, and develop decentralized optimistic actor-critic algorithms that implement these updates. Empirical results on cooperative benchmarks demonstrate that risk-seeking optimism consistently improves coordination over both risk-neutral baselines and heuristic optimistic methods. Our framework thus unifies risk-sensitive learning and optimism, offering a theoretically grounded and practically effective approach to cooperation in MARL.
{"title":"Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning","authors":"Runyu Zhang;Na Li;Asuman Ozdaglar;Jeff Shamma;Gioele Zardini","doi":"10.1109/LCSYS.2025.3645109","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645109","url":null,"abstract":"Risk sensitivity has become a central theme in reinforcement learning (RL), where convex risk measures and robust formulations provide principled ways to model preferences beyond expected return. Recent extensions to multi-agent RL (MARL) have largely emphasized the risk-averse setting, prioritizing robustness to uncertainty. In cooperative MARL, however, such conservatism often leads to suboptimal equilibria, and a parallel line of work has shown that optimism can promote cooperation. Existing optimistic methods, though effective in practice, are typically heuristic and lack theoretical grounding. Building on the dual representation for convex risk measures, we propose a principled framework that interprets risk-seeking objectives as optimism. We introduce optimistic value functions, which formalize optimism as divergence-penalized risk-seeking evaluations. Building on this foundation, we derive a policy-gradient theorem for optimistic value functions, including explicit formulas for the entropic risk/KL-penalty setting, and develop decentralized optimistic actor-critic algorithms that implement these updates. Empirical results on cooperative benchmarks demonstrate that risk-seeking optimism consistently improves coordination over both risk-neutral baselines and heuristic optimistic methods. Our framework thus unifies risk-sensitive learning and optimism, offering a theoretically grounded and practically effective approach to cooperation in MARL.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"1-6"},"PeriodicalIF":2.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/LCSYS.2025.3645033
Prajakta Surve;Shaunak D. Bopardikar;Alexander Von Moll;Isaac Weintraub;David W. Casbeer
This letter studies a heterogeneous three-agent pursuit-evasion scenario in which a sensor–attacker team attempts to capture an active target capable of changing its heading at fixed time intervals. The sensor has a limited sensing range, and the attacker must intercept the target before it escapes sensing. We formulate this problem as a game of kind and extend the optimal sensor and attacker strategies from prior work on passive targets to the active target setting. The sensor updates its heading in each interval by assuming that the target will keep its heading fixed for the rest of the engagement, while the attacker uses an Apollonius circle-based approach for minimum-time interception and updating its heading corresponding to the target heading in every interval. We show that the conditions for capture or escape of a passive target also extend to the case of an active target. In particular, if the speed of the active target is less than a critical value identified for passive targets in our prior work, then capture is guaranteed.
{"title":"Heterogeneous Pursuit of an Active Target Under Sensing Constraints","authors":"Prajakta Surve;Shaunak D. Bopardikar;Alexander Von Moll;Isaac Weintraub;David W. Casbeer","doi":"10.1109/LCSYS.2025.3645033","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645033","url":null,"abstract":"This letter studies a heterogeneous three-agent pursuit-evasion scenario in which a sensor–attacker team attempts to capture an active target capable of changing its heading at fixed time intervals. The sensor has a limited sensing range, and the attacker must intercept the target before it escapes sensing. We formulate this problem as a game of kind and extend the optimal sensor and attacker strategies from prior work on passive targets to the active target setting. The sensor updates its heading in each interval by assuming that the target will keep its heading fixed for the rest of the engagement, while the attacker uses an Apollonius circle-based approach for minimum-time interception and updating its heading corresponding to the target heading in every interval. We show that the conditions for capture or escape of a passive target also extend to the case of an active target. In particular, if the speed of the active target is less than a critical value identified for passive targets in our prior work, then capture is guaranteed.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3017-3022"},"PeriodicalIF":2.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/LCSYS.2025.3645221
Thomas Chapman;Alexander Von Moll;Isaac E. Weintraub
This letter examines the degree to which an evader seeking a safe and efficient path to a target location can benefit from increasing levels of knowledge regarding one or more range-limited pursuers seeking to intercept it. Unlike previous work, this letter considers the time of flight of the pursuers actively attempting interception. It is shown that additional knowledge allows the evader to safely steer closer to the threats, shortening paths without accepting additional risk of capture. A control heuristic is presented, suitable for real-time implementation, which capitalizes on all knowledge available to the evader.
{"title":"Safe Navigation in the Presence of Range-Limited Pursuers","authors":"Thomas Chapman;Alexander Von Moll;Isaac E. Weintraub","doi":"10.1109/LCSYS.2025.3645221","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3645221","url":null,"abstract":"This letter examines the degree to which an evader seeking a safe and efficient path to a target location can benefit from increasing levels of knowledge regarding one or more range-limited pursuers seeking to intercept it. Unlike previous work, this letter considers the time of flight of the pursuers actively attempting interception. It is shown that additional knowledge allows the evader to safely steer closer to the threats, shortening paths without accepting additional risk of capture. A control heuristic is presented, suitable for real-time implementation, which capitalizes on all knowledge available to the evader.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2849-2854"},"PeriodicalIF":2.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/LCSYS.2025.3644797
Marjan Khaledi;Bahare Kiumarsi
This letter proposes a novel approach to safe navigation in environments with static and dynamic obstacles by embedding control barrier functions (CBFs) within the model predictive control (MPC) framework. Unlike conventional methods that rely on unbounded additive slack variables, the proposed approach enforces each CBF constraint separately, allowing individual flexibility through dedicated slack variables with bounded relaxation weights. These weights modulate the permissible degree of constraint relaxation, ensuring that any safety softening remains quantitatively bounded, systematically tunable, and theoretically consistent with the CBF-based safety guaranties. Furthermore, the feasibility of the proposed approach is guaranteed, and the effectiveness of our method is demonstrated through the simulation results.
{"title":"Safety-Certified Planning and Control in Dynamic Environments via Model Predictive Control","authors":"Marjan Khaledi;Bahare Kiumarsi","doi":"10.1109/LCSYS.2025.3644797","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3644797","url":null,"abstract":"This letter proposes a novel approach to safe navigation in environments with static and dynamic obstacles by embedding control barrier functions (CBFs) within the model predictive control (MPC) framework. Unlike conventional methods that rely on unbounded additive slack variables, the proposed approach enforces each CBF constraint separately, allowing individual flexibility through dedicated slack variables with bounded relaxation weights. These weights modulate the permissible degree of constraint relaxation, ensuring that any safety softening remains quantitatively bounded, systematically tunable, and theoretically consistent with the CBF-based safety guaranties. Furthermore, the feasibility of the proposed approach is guaranteed, and the effectiveness of our method is demonstrated through the simulation results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2837-2842"},"PeriodicalIF":2.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11300835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/LCSYS.2025.3644800
Mohammad Rasoul Narimani;Katherine R. Davis;Daniel K. Molzahn
By providing the optimal operating point that satisfies both the power flow equations and engineering limits, the optimal power flow (OPF) problem is central to power systems operations. While extensive research has focused on computing high-quality OPF solutions, assessing the feasibility of transitioning between operating points remains challenging since the feasible spaces of OPF problems may consist of multiple disconnected components. It is not possible to transition between operating points in different disconnected components without violating OPF constraints. To identify such situations, this letter introduces an algorithm for certifying the infeasibility of transitioning between two operating points within an OPF feasible space. As an indication of potential disconnectedness, the algorithm first seeks an infeasible point on the line connecting a pair of feasible points. The algorithm then certifies disconnectedness by using convex relaxation and bound tightening techniques to show that all points on the plane that is normal to this line are infeasible. Using this algorithm, we provide the first certifications of disconnected feasible spaces for a variety of OPF test cases.
{"title":"Certifying the Nonexistence of Feasible Paths Between Power System Operating Points","authors":"Mohammad Rasoul Narimani;Katherine R. Davis;Daniel K. Molzahn","doi":"10.1109/LCSYS.2025.3644800","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3644800","url":null,"abstract":"By providing the optimal operating point that satisfies both the power flow equations and engineering limits, the optimal power flow (OPF) problem is central to power systems operations. While extensive research has focused on computing high-quality OPF solutions, assessing the feasibility of transitioning between operating points remains challenging since the feasible spaces of OPF problems may consist of multiple disconnected components. It is not possible to transition between operating points in different disconnected components without violating OPF constraints. To identify such situations, this letter introduces an algorithm for certifying the infeasibility of transitioning between two operating points within an OPF feasible space. As an indication of potential disconnectedness, the algorithm first seeks an infeasible point on the line connecting a pair of feasible points. The algorithm then certifies disconnectedness by using convex relaxation and bound tightening techniques to show that all points on the plane that is normal to this line are infeasible. Using this algorithm, we provide the first certifications of disconnected feasible spaces for a variety of OPF test cases.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2987-2992"},"PeriodicalIF":2.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1109/LCSYS.2025.3642534
M. Yusuf Uzun;Yildiray Yildiz
By combining automation accuracy with human adaptability, shared control provides enhanced performance and safety in dynamic, complex environments. Traditional arbitration methods for integrating automation and human inputs often rely on system-specific, parameter-dependent functions that are based on shared control metrics such as trust, workload, or attention. Meanwhile, Control Barrier Functions (CBFs) enforce safety constraints on automated systems but are typically limited to safeguarding plant states. This letter introduces a novel arbitration method based on Control Barrier Functions (CBFs), where shared control metrics such as workload, attention, and trust are expressed as real-time inequality constraints. The resulting quadratic-programming formulation determines the automation assistance input that enforces these constraints while preserving feasibility and safety. This CBF-based arbitration provides a systematic, interpretable, and scalable foundation for safe human–autonomy integration.
{"title":"Arbitration With Control Barrier Functions for Safe Shared Control","authors":"M. Yusuf Uzun;Yildiray Yildiz","doi":"10.1109/LCSYS.2025.3642534","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3642534","url":null,"abstract":"By combining automation accuracy with human adaptability, shared control provides enhanced performance and safety in dynamic, complex environments. Traditional arbitration methods for integrating automation and human inputs often rely on system-specific, parameter-dependent functions that are based on shared control metrics such as trust, workload, or attention. Meanwhile, Control Barrier Functions (CBFs) enforce safety constraints on automated systems but are typically limited to safeguarding plant states. This letter introduces a novel arbitration method based on Control Barrier Functions (CBFs), where shared control metrics such as workload, attention, and trust are expressed as real-time inequality constraints. The resulting quadratic-programming formulation determines the automation assistance input that enforces these constraints while preserving feasibility and safety. This CBF-based arbitration provides a systematic, interpretable, and scalable foundation for safe human–autonomy integration.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2789-2794"},"PeriodicalIF":2.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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