Pub Date : 2025-12-10DOI: 10.1109/LCSYS.2025.3642487
Reetish Padhi;Ion Victor Gosea;Igor Pontes Duff;Serkan Gugercin
We develop the theoretical formulation for a non-intrusive, quadrature-based method for approximate balanced truncation (QuadBT) of linear systems with quadratic outputs, thus extending the applicability of QuadBT, which was originally designed for data-driven balanced truncation of standard linear systems with linear outputs only. The new approach makes use of the time-domain and frequency-domain quadrature-based representation of the system’s infinite Gramians, only implicitly. We show that by sampling solely the extended impulse responses (kernels) of the original system and their derivatives (or the corresponding transfer functions), we construct a reduced-order model that mimics the approximation quality of the intrusive (projection-based) balanced truncation. Although the sampling of the required kernels via input/output simulations or physical experiments is still an open question, we demonstrate a proof of concept for the proposed framework on an example using numerically evaluated data.
{"title":"Data-Driven Balancing Formulation for Linear Systems With Quadratic Outputs","authors":"Reetish Padhi;Ion Victor Gosea;Igor Pontes Duff;Serkan Gugercin","doi":"10.1109/LCSYS.2025.3642487","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3642487","url":null,"abstract":"We develop the theoretical formulation for a non-intrusive, quadrature-based method for approximate balanced truncation (QuadBT) of linear systems with quadratic outputs, thus extending the applicability of QuadBT, which was originally designed for data-driven balanced truncation of standard linear systems with linear outputs only. The new approach makes use of the time-domain and frequency-domain quadrature-based representation of the system’s infinite Gramians, only implicitly. We show that by sampling solely the extended impulse responses (kernels) of the original system and their derivatives (or the corresponding transfer functions), we construct a reduced-order model that mimics the approximation quality of the intrusive (projection-based) balanced truncation. Although the sampling of the required kernels via input/output simulations or physical experiments is still an open question, we demonstrate a proof of concept for the proposed framework on an example using numerically evaluated data.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2843-2848"},"PeriodicalIF":2.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830840","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.2025.3642769
Delphine Bresch-Pietri;Jean Auriol
This letter addresses the existence and uniqueness of the solution to a class of Fredholm integral equations associated with scalar Linear Integral Delay Equations (LIDEs). Based on an operator-theoretic framework involving transport partial differential equations, we provide general conditions that guarantee well-posedness of these equations. Leveraging this result, we introduce a constructive approach for the numerical computation of the Lyapunov matrix corresponding to scalar LIDEs with commensurate delays. Specifically, the Lyapunov matrix equations are reformulated as the Fredholm integral equation under consideration, for which the proposed conditions are satisfied under exponential stability of the LIDE. The resulting integral equation can then be discretized, and the linear system solved to efficiently compute the Delay Lyapunov matrix. Numerical examples illustrate the effectiveness and applicability of this methodology.
{"title":"Existence and Uniqueness of the Solution to a Class of Fredholm Integral Equations Related to Difference Equations","authors":"Delphine Bresch-Pietri;Jean Auriol","doi":"10.1109/LCSYS.2025.3642769","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3642769","url":null,"abstract":"This letter addresses the existence and uniqueness of the solution to a class of Fredholm integral equations associated with scalar Linear Integral Delay Equations (LIDEs). Based on an operator-theoretic framework involving transport partial differential equations, we provide general conditions that guarantee well-posedness of these equations. Leveraging this result, we introduce a constructive approach for the numerical computation of the Lyapunov matrix corresponding to scalar LIDEs with commensurate delays. Specifically, the Lyapunov matrix equations are reformulated as the Fredholm integral equation under consideration, for which the proposed conditions are satisfied under exponential stability of the LIDE. The resulting integral equation can then be discretized, and the linear system solved to efficiently compute the Delay Lyapunov matrix. Numerical examples illustrate the effectiveness and applicability of this methodology.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"3113-3118"},"PeriodicalIF":2.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929507","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.2025.3642222
Hossein Gholampour;Logan E. Beaver
Many robotic systems must follow planned paths yet pause safely and resume when people or objects intervene. We present an output–space method for systems whose tracked output can be feedback-linearized to a double integrator (e.g., manipulators). The approach has two parts. Offline, we perform a pre-run reachability check to verify that the motion plan respects speed and acceleration magnitude limits. Online, we apply a quadratic program to track the motion plan under the same limits. We use a one-step reachability test to bound the maximum disturbance the system is capable of rejecting. When the state coincides with the reference path we recover perfect tracking in the deterministic case, and we correct errors using a KKT-inspired weight. We demonstrate that safety stops and unplanned deviations are handled efficiently, and the system returns to the motion plan without replanning. We demonstrate our system’s improved performance over pure pursuit in simulation.
{"title":"Trajectory Tracking With Reachability-Guided Quadratic Programming and Freeze-Resume","authors":"Hossein Gholampour;Logan E. Beaver","doi":"10.1109/LCSYS.2025.3642222","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3642222","url":null,"abstract":"Many robotic systems must follow planned paths yet pause safely and resume when people or objects intervene. We present an output–space method for systems whose tracked output can be feedback-linearized to a double integrator (e.g., manipulators). The approach has two parts. Offline, we perform a pre-run reachability check to verify that the motion plan respects speed and acceleration magnitude limits. Online, we apply a quadratic program to track the motion plan under the same limits. We use a one-step reachability test to bound the maximum disturbance the system is capable of rejecting. When the state coincides with the reference path we recover perfect tracking in the deterministic case, and we correct errors using a KKT-inspired weight. We demonstrate that safety stops and unplanned deviations are handled efficiently, and the system returns to the motion plan without replanning. We demonstrate our system’s improved performance over pure pursuit in simulation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2795-2800"},"PeriodicalIF":2.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778263","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 : 2025-12-09DOI: 10.1109/LCSYS.2025.3641995
Hang Zhang;Abigail J. Winn;Yuhao Zhang;Xiangru Xu
This letter investigates goal-reaching control synthesis for neural network control systems. A backward reachability framework is developed based on constrained zonotopes, in which the graph set of a ReLU-activated feedforward neural network is encoded as a finite union of constrained zonotopes. Using this representation, under-approximations of backward reachable sets are computed for systems with nonlinear plant models, ensuring the feasibility of the goal-reaching task. Control sequences are then synthesized through an optimization procedure that exploits the under-approximated set. A numerical example demonstrates the effectiveness of the proposed approach.
{"title":"Goal-Reaching Control Synthesis for Neural Network Control Systems via Backward Reachability","authors":"Hang Zhang;Abigail J. Winn;Yuhao Zhang;Xiangru Xu","doi":"10.1109/LCSYS.2025.3641995","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641995","url":null,"abstract":"This letter investigates goal-reaching control synthesis for neural network control systems. A backward reachability framework is developed based on constrained zonotopes, in which the graph set of a ReLU-activated feedforward neural network is encoded as a finite union of constrained zonotopes. Using this representation, under-approximations of backward reachable sets are computed for systems with nonlinear plant models, ensuring the feasibility of the goal-reaching task. Control sequences are then synthesized through an optimization procedure that exploits the under-approximated set. A numerical example demonstrates the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2771-2776"},"PeriodicalIF":2.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778168","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 : 2025-12-09DOI: 10.1109/LCSYS.2025.3641989
Zahra Hashemi;Dipankar Maity
This letter explores intelligent scheduling of sensor-to-controller communication in networked control systems, particularly when data transmission incurs a cost. While the optimal controller in a standard linear-quadratic Gaussian (LQG) setup can be computed analytically, determining the optimal times to transmit sensor data remains computationally and analytically challenging. We show that, through reformulation and the introduction of auxiliary binary variables, the scheduling problem can be cast as a computationally efficient mixed-integer linear program (MILP). This formulation not only simplifies the analysis but also reveals structural insights and provides clear decision criterion at each step. Embedding the approach within a model predictive control (MPC) framework enables dynamic adaptation, and we prove that the resulting scheduler performs at least as well as any deterministic strategy (e.g., periodic strategy). Simulation results further demonstrate that our method consistently outperforms traditional periodic scheduling.
{"title":"A Linear Programming Framework for Optimal Event-Triggered LQG Control","authors":"Zahra Hashemi;Dipankar Maity","doi":"10.1109/LCSYS.2025.3641989","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641989","url":null,"abstract":"This letter explores intelligent scheduling of sensor-to-controller communication in networked control systems, particularly when data transmission incurs a cost. While the optimal controller in a standard linear-quadratic Gaussian (LQG) setup can be computed analytically, determining the optimal times to transmit sensor data remains computationally and analytically challenging. We show that, through reformulation and the introduction of auxiliary binary variables, the scheduling problem can be cast as a computationally efficient mixed-integer linear program (MILP). This formulation not only simplifies the analysis but also reveals structural insights and provides clear decision criterion at each step. Embedding the approach within a model predictive control (MPC) framework enables dynamic adaptation, and we prove that the resulting scheduler performs at least as well as any deterministic strategy (e.g., periodic strategy). Simulation results further demonstrate that our method consistently outperforms traditional periodic scheduling.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2783-2788"},"PeriodicalIF":2.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778264","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 : 2025-12-09DOI: 10.1109/LCSYS.2025.3642051
Jian Zheng;Mario Sznaier
This letter presents a robust data-driven receding-horizon control framework for the discrete-time linear quadratic regulator (LQR) with input constraints. Unlike earlier data-driven approaches that design a controller from initial data and apply it unchanged throughout the trajectory, our method exploits all available execution data in a receding-horizon manner, thereby capturing additional information about the unknown system and enabling less conservative performance. Existing data-driven LQR model predictive control methods rely on over-approximations of the consistency set and $ell _{2}$ descriptions of noise. In contrast, the proposed approach uses exact descriptions of the consistency set under $ell _{infty }$ -bounded noise, leveraging duality to recast the problem into a tractable convex optimization. Further, the proposed controller renders the closed-loop system input-to-state stable. Simulation results demonstrate the effectiveness of the method.
{"title":"Robust Data-Driven Receding-Horizon Control for LQR With Input Constraints","authors":"Jian Zheng;Mario Sznaier","doi":"10.1109/LCSYS.2025.3642051","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3642051","url":null,"abstract":"This letter presents a robust data-driven receding-horizon control framework for the discrete-time linear quadratic regulator (LQR) with input constraints. Unlike earlier data-driven approaches that design a controller from initial data and apply it unchanged throughout the trajectory, our method exploits all available execution data in a receding-horizon manner, thereby capturing additional information about the unknown system and enabling less conservative performance. Existing data-driven LQR model predictive control methods rely on over-approximations of the consistency set and <inline-formula> <tex-math>$ell _{2}$ </tex-math></inline-formula> descriptions of noise. In contrast, the proposed approach uses exact descriptions of the consistency set under <inline-formula> <tex-math>$ell _{infty }$ </tex-math></inline-formula>-bounded noise, leveraging duality to recast the problem into a tractable convex optimization. Further, the proposed controller renders the closed-loop system input-to-state stable. Simulation results demonstrate the effectiveness of the method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2765-2770"},"PeriodicalIF":2.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778265","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641882
Fat-Hy O. Rajab;Olugbenga M. Anubi;Marcos M. Vasconcelos
This letter investigates a two-player coordination game in which the players exhibit heterogeneous levels of bounded rationality. We analyze the log-linear learning dynamics where the probability distribution used to select which of the agents gets to revise its strategy is fixed but not necessarily uniform. The stationary distribution of the resulting Markov chain on the strategy profile space is derived in closed-form as a function of the rationalities and the agent selection probabilities. We proceed by showing that adjusting the selection probabilities can be used to bias the stationary distribution toward the potential-maximizing state. However, this optimization comes at the cost of a reduced convergence rate, whereas the uniform selection probabilities uniquely maximizes the convergence speed irrespective of the players’ rationality levels. A Pareto-optimal probability selection rule is proposed, trading-off the distributional bias with convergence rate. Moreover, it is shown that in coordination games, high levels of rationality sometimes accelerate convergence, whereas in other cases they may paradoxically hinder the convergence rate of log-linear learning dynamics.
{"title":"Log-Linear Learning for Coordination With Heterogeneous Bounded Rationalities","authors":"Fat-Hy O. Rajab;Olugbenga M. Anubi;Marcos M. Vasconcelos","doi":"10.1109/LCSYS.2025.3641882","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641882","url":null,"abstract":"This letter investigates a two-player coordination game in which the players exhibit heterogeneous levels of bounded rationality. We analyze the log-linear learning dynamics where the probability distribution used to select which of the agents gets to revise its strategy is fixed but not necessarily uniform. The stationary distribution of the resulting Markov chain on the strategy profile space is derived in closed-form as a function of the rationalities and the agent selection probabilities. We proceed by showing that adjusting the selection probabilities can be used to bias the stationary distribution toward the potential-maximizing state. However, this optimization comes at the cost of a reduced convergence rate, whereas the uniform selection probabilities uniquely maximizes the convergence speed irrespective of the players’ rationality levels. A Pareto-optimal probability selection rule is proposed, trading-off the distributional bias with convergence rate. Moreover, it is shown that in coordination games, high levels of rationality sometimes accelerate convergence, whereas in other cases they may paradoxically hinder the convergence rate of log-linear learning dynamics.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2741-2746"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778192","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641880
Martin T. Köhler;Artemi Makarow;Christian Kirches
We exploit the convergence of graph sequences to some limit object, a graphon, to generate scalable initial estimates for optimal control of large-scale networks. We derive a state error bound under the assumptions of converging graph sequences and initial state functions and, given this bound, we propose an iterative scale-up approach to accelerate the time required for numerical solvers to reach the optimum. The idea is to scale up the optimal control solution for a network dynamical system and use it to initialize the optimal control problem for a larger structurally similar network system. The numerical results demonstrate a significant reduction of the number of optimization iterations and the optimization time for a nonlinear large-scale coupled network.
{"title":"Exploiting Graph Convergence for Accelerated Optimization in Optimal Control of Large-Scale Networks","authors":"Martin T. Köhler;Artemi Makarow;Christian Kirches","doi":"10.1109/LCSYS.2025.3641880","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641880","url":null,"abstract":"We exploit the convergence of graph sequences to some limit object, a graphon, to generate scalable initial estimates for optimal control of large-scale networks. We derive a state error bound under the assumptions of converging graph sequences and initial state functions and, given this bound, we propose an iterative scale-up approach to accelerate the time required for numerical solvers to reach the optimum. The idea is to scale up the optimal control solution for a network dynamical system and use it to initialize the optimal control problem for a larger structurally similar network system. The numerical results demonstrate a significant reduction of the number of optimization iterations and the optimization time for a nonlinear large-scale coupled network.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2747-2752"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11284794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778212","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641886
Mohammad Khajenejad
We introduce a guaranteed privacy-preserving controller for nonlinear discrete-time systems with bounded uncertainties. Moving beyond stochastic differential privacy, our design offers deterministic privacy through hard bounds on the proximity of set-valued estimates. The solution involves synthesizing a stabilizing controller for a perturbed framer system, where control gains and a privacy-inducing noise factor are co-optimized via semi-definite programming (SDP). This integrated approach ensures both input-to-s tate stable closed-loop dynamics and certified privacy. We also formalize the inherent performance-privacy trade-off by quantifying the accuracy loss due to privacy constraints. Simulations confirm that our method outperforms standard differential privacy techniques.
{"title":"Guaranteed Privacy-Preserving Control of Discrete-Time Systems","authors":"Mohammad Khajenejad","doi":"10.1109/LCSYS.2025.3641886","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641886","url":null,"abstract":"We introduce a guaranteed privacy-preserving controller for nonlinear discrete-time systems with bounded uncertainties. Moving beyond stochastic differential privacy, our design offers deterministic privacy through hard bounds on the proximity of set-valued estimates. The solution involves synthesizing a stabilizing controller for a perturbed framer system, where control gains and a privacy-inducing noise factor are co-optimized via semi-definite programming (SDP). This integrated approach ensures both input-to-s tate stable closed-loop dynamics and certified privacy. We also formalize the inherent performance-privacy trade-off by quantifying the accuracy loss due to privacy constraints. Simulations confirm that our method outperforms standard differential privacy techniques.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2759-2764"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778145","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641525
Muzaffar Qureshi;Tochukwu E. Ogri;Humberto Ramos;Wanjiku A. Makumi;Zachary I. Bell;Rushikesh Kamalapurkar
This letter considers an autonomous agent that intermittently acquires state measurements to maintain trajectory tracking performance. The objective is to minimize sensing needs by extending periods of sensor-denied operation. A Lyapunov-based adaptive switched systems approach is developed, where the agent uses the intermittently acquired state measurements to learn the system model. The learned system models are then used during sensor-denied intervals to extend their length while maintaining tracking performance. The design uses a modeling error-dependent bound on the duration of the sensor-denied intervals to progressively reduce sensing needs as the modeling error decreases. The effectiveness of the developed technique is verified in a simulation study.
{"title":"A Switched Adaptive Control Approach to Reduce Sensing Needs in Trajectory Tracking Problems","authors":"Muzaffar Qureshi;Tochukwu E. Ogri;Humberto Ramos;Wanjiku A. Makumi;Zachary I. Bell;Rushikesh Kamalapurkar","doi":"10.1109/LCSYS.2025.3641525","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641525","url":null,"abstract":"This letter considers an autonomous agent that intermittently acquires state measurements to maintain trajectory tracking performance. The objective is to minimize sensing needs by extending periods of sensor-denied operation. A Lyapunov-based adaptive switched systems approach is developed, where the agent uses the intermittently acquired state measurements to learn the system model. The learned system models are then used during sensor-denied intervals to extend their length while maintaining tracking performance. The design uses a modeling error-dependent bound on the duration of the sensor-denied intervals to progressively reduce sensing needs as the modeling error decreases. The effectiveness of the developed technique is verified in a simulation study.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2777-2782"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778277","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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