Pub Date : 2025-12-22DOI: 10.1109/LCSYS.2025.3647069
Yang Zhao;Elikplim Gah;Sze Zheng Yong
This letter presents an output-feedback tube-based model predictive control (MPC) framework for linear sampled-data control systems subject to external disturbances and non-convex constraints. The proposed approach rigorously incorporates inter-sample reachability analysis to account for the continuous-time evolution of system trajectories between discrete sampling instances and to ensure constraint satisfaction in the continuous time domain. The resulting continuous-time tube-based MPC scheme is demonstrated to ensure that trajectories remain within (potentially non-convex) safe sets throughout the continuous-time evolution.
{"title":"Tube-Based MPC for Uncertain Sampled-Data Control Systems With Inter-Sample Reachability Analysis","authors":"Yang Zhao;Elikplim Gah;Sze Zheng Yong","doi":"10.1109/LCSYS.2025.3647069","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3647069","url":null,"abstract":"This letter presents an output-feedback tube-based model predictive control (MPC) framework for linear sampled-data control systems subject to external disturbances and non-convex constraints. The proposed approach rigorously incorporates inter-sample reachability analysis to account for the continuous-time evolution of system trajectories between discrete sampling instances and to ensure constraint satisfaction in the continuous time domain. The resulting continuous-time tube-based MPC scheme is demonstrated to ensure that trajectories remain within (potentially non-convex) safe sets throughout the continuous-time evolution.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3047-3052"},"PeriodicalIF":2.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929336","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 : 2025-12-22DOI: 10.1109/LCSYS.2025.3646686
Siqi Du;Heling Zhang;Roy Dong
Classical data-driven methods can be conceptualized as mappings from data distributions to decisions. However, in practice, decisions can influence the data distributions themselves. One of the common methods for handling unknown decision-dependent distribution shift is repeated optimization. In this letter, we model repeated optimization as a discrete-time feedback interconnection system. Our framework enables convergence analysis based on dissipation inequalities and integral quadratic constraints, which provides a novel method to show convergence under unknown decision-dependent distribution shift. We bound the suboptimality when using repeated gradient descent and ignoring the distribution shift when taking gradient steps. Additionally, our framework provides a method to bound the distance between performatively stable points and performatively optimal points.
{"title":"Convergence Analysis of Repeated Optimization in Performative Prediction","authors":"Siqi Du;Heling Zhang;Roy Dong","doi":"10.1109/LCSYS.2025.3646686","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3646686","url":null,"abstract":"Classical data-driven methods can be conceptualized as mappings from data distributions to decisions. However, in practice, decisions can influence the data distributions themselves. One of the common methods for handling unknown decision-dependent distribution shift is repeated optimization. In this letter, we model repeated optimization as a discrete-time feedback interconnection system. Our framework enables convergence analysis based on dissipation inequalities and integral quadratic constraints, which provides a novel method to show convergence under unknown decision-dependent distribution shift. We bound the suboptimality when using repeated gradient descent and ignoring the distribution shift when taking gradient steps. Additionally, our framework provides a method to bound the distance between performatively stable points and performatively optimal points.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2999-3004"},"PeriodicalIF":2.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886554","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 : 2025-12-22DOI: 10.1109/LCSYS.2025.3646701
Erik I. Verriest
Geometric insight may lead to a quick solution for a class of non-LQ optimal control problems. We illustrate this with a simple, inconspicuous-looking example. While necessary conditions for optimality are easily obtained, their analytic solution may not be easy. But some problems are locally reducible to an Euclidean distance problem, but not necessarily globally due to the underlying topology. This insight leads to the additional realization that in some cases, optimality may require impulsive inputs. However, Dirac deltas cannot be compatible with nonlinear operations in Schwartz’s distribution theory. Thus, it seems that we may have a solution but not a theory. Since the solution is transparent in its geometric form, it suggests that another approach to generalized functions, as proposed by Colombeau, should be used. This is very valuable as it corroborates our earlier work. Generalizations are then sought for other problems reducible to Euclidean minimum distance problems, and even more general Riemannian spaces. We make some connections with the notion of persistence of behavior, where these results apply.
{"title":"Geometric Insight in Solving Optimal Control Problems and the Emergence of Generalized Functions","authors":"Erik I. Verriest","doi":"10.1109/LCSYS.2025.3646701","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3646701","url":null,"abstract":"Geometric insight may lead to a quick solution for a class of non-LQ optimal control problems. We illustrate this with a simple, inconspicuous-looking example. While necessary conditions for optimality are easily obtained, their analytic solution may not be easy. But some problems are locally reducible to an Euclidean distance problem, but not necessarily globally due to the underlying topology. This insight leads to the additional realization that in some cases, optimality may require impulsive inputs. However, Dirac deltas cannot be compatible with nonlinear operations in Schwartz’s distribution theory. Thus, it seems that we may have a solution but not a theory. Since the solution is transparent in its geometric form, it suggests that another approach to generalized functions, as proposed by Colombeau, should be used. This is very valuable as it corroborates our earlier work. Generalizations are then sought for other problems reducible to Euclidean minimum distance problems, and even more general Riemannian spaces. We make some connections with the notion of persistence of behavior, where these results apply.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3053-3058"},"PeriodicalIF":2.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929414","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 : 2025-12-22DOI: 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}
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