Pub Date : 2024-12-23DOI: 10.1109/LCSYS.2024.3521190
Sotaro Fushimi;Yuto Watanabe;Kazunori Sakurama
This letter addresses the centralized synthesis of distributed controllers using linear matrix inequalities (LMIs). Sparsity constraints on control gains of distributed controllers result in conservatism via the convexification of the existing methods such as the extended LMI method. In order to mitigate the conservatism, we introduce a novel LMI formulation for this problem, utilizing the clique-wise decomposition method from our previous work on continuous-time systems. By reformulating the sparsity constraint on the gain matrix within cliques, this method achieves a broader solution set. Also, the analytical superiority of our method is confirmed through numerical examples.
{"title":"Design of Distributed Controller for Discrete-Time Systems via the Integration of Extended LMI and Clique-Wise Decomposition","authors":"Sotaro Fushimi;Yuto Watanabe;Kazunori Sakurama","doi":"10.1109/LCSYS.2024.3521190","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521190","url":null,"abstract":"This letter addresses the centralized synthesis of distributed controllers using linear matrix inequalities (LMIs). Sparsity constraints on control gains of distributed controllers result in conservatism via the convexification of the existing methods such as the extended LMI method. In order to mitigate the conservatism, we introduce a novel LMI formulation for this problem, utilizing the clique-wise decomposition method from our previous work on continuous-time systems. By reformulating the sparsity constraint on the gain matrix within cliques, this method achieves a broader solution set. Also, the analytical superiority of our method is confirmed through numerical examples.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3171-3176"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938117","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521673
Tianchen Liu;Kushal Chakrabarti;Nikhil Chopra
We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algorithm. IPG utilized Richardson iteration to update a preconditioner matrix that approximates the inverse of the Hessian matrix. In this letter, we substitute the Richardson iteration with a successive over-relaxation (SOR) formulation. The convergence guarantee of the proposed algorithm and its theoretical improvement over vanilla IPG are presented. The algorithm is used in a mobile robot position estimation problem for numerical validation using a moving horizon estimation (MHE) formulation. Compared with IPG, the results demonstrate an improved performance of the proposed algorithm in terms of computational time and the number of iterations needed for convergence, matching our theoretical results.
{"title":"Novel Iteratively Preconditioned Gradient-Descent Algorithm via Successive Over-Relaxation Formulation","authors":"Tianchen Liu;Kushal Chakrabarti;Nikhil Chopra","doi":"10.1109/LCSYS.2024.3521673","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521673","url":null,"abstract":"We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algorithm. IPG utilized Richardson iteration to update a preconditioner matrix that approximates the inverse of the Hessian matrix. In this letter, we substitute the Richardson iteration with a successive over-relaxation (SOR) formulation. The convergence guarantee of the proposed algorithm and its theoretical improvement over vanilla IPG are presented. The algorithm is used in a mobile robot position estimation problem for numerical validation using a moving horizon estimation (MHE) formulation. Compared with IPG, the results demonstrate an improved performance of the proposed algorithm in terms of computational time and the number of iterations needed for convergence, matching our theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3105-3110"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962846","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521556
Julian Golembiewski;Timm Faulwasser
Nonsmooth phenomena, such as abrupt changes, impacts, and switching behaviors, frequently arise in real-world systems and present significant challenges for traditional optimal control methods, which typically assume smoothness and differentiability. These phenomena introduce numerical challenges in both simulation and optimization, highlighting the need for specialized solution methods. Although various applications and test problems have been documented in the literature, many are either overly simplified, excessively complex, or narrowly focused on specific domains. On this canvas, this letter proposes two novel tutorial problems that are both conceptually accessible and allow for further scaling of problem difficulty. The first problem features a simple ski jump model, characterized by state-dependent jumps and sliding motion on impact surfaces. This system does not involve control inputs and serves as a testbed for simulating nonsmooth dynamics. The second problem considers optimal control of a special type of bicycle model. This problem is inspired by practical techniques observed in BMX riding and mountain biking, where riders accelerate their bike without pedaling by strategically shifting their center of mass in response to the track’s slope.
{"title":"Tutorial Problems for Nonsmooth Dynamics and Optimal Control: Ski Jumping and Accelerating a Bike Without Pedaling","authors":"Julian Golembiewski;Timm Faulwasser","doi":"10.1109/LCSYS.2024.3521556","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521556","url":null,"abstract":"Nonsmooth phenomena, such as abrupt changes, impacts, and switching behaviors, frequently arise in real-world systems and present significant challenges for traditional optimal control methods, which typically assume smoothness and differentiability. These phenomena introduce numerical challenges in both simulation and optimization, highlighting the need for specialized solution methods. Although various applications and test problems have been documented in the literature, many are either overly simplified, excessively complex, or narrowly focused on specific domains. On this canvas, this letter proposes two novel tutorial problems that are both conceptually accessible and allow for further scaling of problem difficulty. The first problem features a simple ski jump model, characterized by state-dependent jumps and sliding motion on impact surfaces. This system does not involve control inputs and serves as a testbed for simulating nonsmooth dynamics. The second problem considers optimal control of a special type of bicycle model. This problem is inspired by practical techniques observed in BMX riding and mountain biking, where riders accelerate their bike without pedaling by strategically shifting their center of mass in response to the track’s slope.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3291-3296"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976169","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 : 2024-12-23DOI: 10.1109/LCSYS.2024.3521193
Emanuele Musicò;Camilla Ancona;Francesco Lo Iudice;Luigi Glielmo
Energy storage systems are key to take advantage of the potential of renewable energy. Renewable energy communities endowed with a Shared Battery Energy Storage System (SBESS) have been proposed as a key factor to efficiently exploit distributed renewable generation. However, ensuring renewable energy communities are not only environmentally friendly but also cost-effective requires that their SBESS be optimally managed. In this letter we cast the problem of optimally managing a SBESS in an energy community as the problem of minimizing the sum of the daily energy bills of the community. Then, under mild assumptions, we rigorously show that solving this optimal control problem over an horizon of N days is equivalent to sequentially solving N optimization problems over a single day. Our theoretical results are compounded by numerical simulations on a real dataset of Australian households’ demand and generation.
{"title":"An Optimal Control Approach for Enhancing Efficiency in Renewable Energy Communities","authors":"Emanuele Musicò;Camilla Ancona;Francesco Lo Iudice;Luigi Glielmo","doi":"10.1109/LCSYS.2024.3521193","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3521193","url":null,"abstract":"Energy storage systems are key to take advantage of the potential of renewable energy. Renewable energy communities endowed with a Shared Battery Energy Storage System (SBESS) have been proposed as a key factor to efficiently exploit distributed renewable generation. However, ensuring renewable energy communities are not only environmentally friendly but also cost-effective requires that their SBESS be optimally managed. In this letter we cast the problem of optimally managing a SBESS in an energy community as the problem of minimizing the sum of the daily energy bills of the community. Then, under mild assumptions, we rigorously show that solving this optimal control problem over an horizon of N days is equivalent to sequentially solving N optimization problems over a single day. Our theoretical results are compounded by numerical simulations on a real dataset of Australian households’ demand and generation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3039-3044"},"PeriodicalIF":2.4,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962849","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}
This letter investigates the distributed optimal cooperative tracking control problem for multi-input linear time-invariant (LTI) systems. In this context, the system inputs are generated by a group of agents that communicate with each other over a network, i.e., each control input channel is considered as an agent, which can communicate over a network to transmit information and compute control input. Unlike centralized optimal tracking control, where inputs are designed using global information, each agent in the distributed framework has access only to its own input matrix and communicates solely with its neighbors within the network. This limitation introduces significant challenges in designing the optimal controller. To address this issue, an information fusion method is first proposed, enabling each agent to derive its optimal controller in a distributed manner. For scenarios where the system model is unknown, a fusion-based learning algorithm is further developed. The convergence and optimality of this algorithm are rigorously proved. A simulation example is provided to illustrate the effectiveness of the proposed approach.
{"title":"Distributed Optimal Cooperative Tracking Control of Multi-Input LTI Systems: An Information Fusion-Based Learning Approach","authors":"Yunxiao Ren;Dingguo Liang;Silong Wang;Tao Xu;Yuezu Lv","doi":"10.1109/LCSYS.2024.3520917","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520917","url":null,"abstract":"This letter investigates the distributed optimal cooperative tracking control problem for multi-input linear time-invariant (LTI) systems. In this context, the system inputs are generated by a group of agents that communicate with each other over a network, i.e., each control input channel is considered as an agent, which can communicate over a network to transmit information and compute control input. Unlike centralized optimal tracking control, where inputs are designed using global information, each agent in the distributed framework has access only to its own input matrix and communicates solely with its neighbors within the network. This limitation introduces significant challenges in designing the optimal controller. To address this issue, an information fusion method is first proposed, enabling each agent to derive its optimal controller in a distributed manner. For scenarios where the system model is unknown, a fusion-based learning algorithm is further developed. The convergence and optimality of this algorithm are rigorously proved. A simulation example is provided to illustrate the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3129-3134"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962834","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 : 2024-12-19DOI: 10.1109/LCSYS.2024.3520906
Shiang Cao;Yangquan Chen
This letter presents a data-driven approach to assess the controllability of nabla discrete fractional-order systems. This letter also proposes a data-driven approximation method for these systems, grounded in behavioral system theory. Instead of identifying a model that replicates the input-output dynamics, a direct modification is applied to the collected input-output data by solving a Hankel-structured low-rank approximation problem. The performance of the approximated nonparametric models in simulating system responses is compared to that of ARX models with the same number of states. The results indicate that the proposed method achieves similar simulation performance with improved accuracy. Furthermore, leveraging this approximation, many data-driven controllers can be designed. As an example, a data-driven predictive control strategy is designed and applied to a discrete fractional-order system. Simulation results demonstrate that the controller successfully drives the system outputs to the desired positions.
{"title":"Data-Driven Controllability and Controller Designs for Nabla Fractional Order Systems","authors":"Shiang Cao;Yangquan Chen","doi":"10.1109/LCSYS.2024.3520906","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520906","url":null,"abstract":"This letter presents a data-driven approach to assess the controllability of nabla discrete fractional-order systems. This letter also proposes a data-driven approximation method for these systems, grounded in behavioral system theory. Instead of identifying a model that replicates the input-output dynamics, a direct modification is applied to the collected input-output data by solving a Hankel-structured low-rank approximation problem. The performance of the approximated nonparametric models in simulating system responses is compared to that of ARX models with the same number of states. The results indicate that the proposed method achieves similar simulation performance with improved accuracy. Furthermore, leveraging this approximation, many data-driven controllers can be designed. As an example, a data-driven predictive control strategy is designed and applied to a discrete fractional-order system. Simulation results demonstrate that the controller successfully drives the system outputs to the desired positions.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3033-3038"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962891","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 : 2024-12-19DOI: 10.1109/LCSYS.2024.3520152
Yating Yuan;Thanin Quartz;Jun Liu
This letter aims to generate a continuous-time trajectory consisting of piecewise Bézier curves that satisfy signal temporal logic (STL) specifications with piecewise time-varying robustness. The time-varying robustness is less conservative than the real-valued robustness, which enables more effective tracking in practical applications. Specifically, continuous-time trajectories account for dynamic feasibility, leading to smaller tracking errors and ensuring that the STL specifications can be met by the tracking trajectory. Comparative experiments demonstrate the efficiency and effectiveness of the proposed approach. The implementation is available at https://github.com/ViviaY/TimeVaryingBound_STL .
{"title":"Signal Temporal Logic Planning With Time-Varying Robustness","authors":"Yating Yuan;Thanin Quartz;Jun Liu","doi":"10.1109/LCSYS.2024.3520152","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520152","url":null,"abstract":"This letter aims to generate a continuous-time trajectory consisting of piecewise Bézier curves that satisfy signal temporal logic (STL) specifications with piecewise time-varying robustness. The time-varying robustness is less conservative than the real-valued robustness, which enables more effective tracking in practical applications. Specifically, continuous-time trajectories account for dynamic feasibility, leading to smaller tracking errors and ensuring that the STL specifications can be met by the tracking trajectory. Comparative experiments demonstrate the efficiency and effectiveness of the proposed approach. The implementation is available at \u0000<uri>https://github.com/ViviaY/TimeVaryingBound_STL</uri>\u0000.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3015-3020"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912415","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 : 2024-12-19DOI: 10.1109/LCSYS.2024.3520519
Samuel G. Gessow;David Thorne;Brett T. Lopez
This letter develops a new framework for preventing localization failures in mobile systems that estimate their state using measurements. Safety is guaranteed by imposing that the nonlinear least squares optimization, solved in modern localization algorithms, remains well-conditioned. Specifically, the eigenvalues of the Hessian matrix are made to be positive via two methods that leverage control barrier functions to achieve safe set invariance. The proposed method is not constrained to any specific measurement or system type, offering a general solution to the safe mobility with localization problem. The efficacy of the approach is demonstrated on a system provided range-only and heading-only measurements for localization.
{"title":"Information Control Barrier Functions: Preventing Localization Failures in Mobile Systems Through Control","authors":"Samuel G. Gessow;David Thorne;Brett T. Lopez","doi":"10.1109/LCSYS.2024.3520519","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520519","url":null,"abstract":"This letter develops a new framework for preventing localization failures in mobile systems that estimate their state using measurements. Safety is guaranteed by imposing that the nonlinear least squares optimization, solved in modern localization algorithms, remains well-conditioned. Specifically, the eigenvalues of the Hessian matrix are made to be positive via two methods that leverage control barrier functions to achieve safe set invariance. The proposed method is not constrained to any specific measurement or system type, offering a general solution to the safe mobility with localization problem. The efficacy of the approach is demonstrated on a system provided range-only and heading-only measurements for localization.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3003-3008"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962828","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 : 2024-12-19DOI: 10.1109/LCSYS.2024.3520918
J. Escareno;J. U. Alvarez-Munoz;L. R. Garcia Carrillo;I. Rubio Scola;J. Franco-Robles;O. Labbani-Igbida
Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.
{"title":"Limbic System-Inspired Robust Event-Driven Control for High-Order Uncertain Nonlinear Systems","authors":"J. Escareno;J. U. Alvarez-Munoz;L. R. Garcia Carrillo;I. Rubio Scola;J. Franco-Robles;O. Labbani-Igbida","doi":"10.1109/LCSYS.2024.3520918","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520918","url":null,"abstract":"Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3057-3062"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962847","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 : 2024-12-19DOI: 10.1109/LCSYS.2024.3520348
Melanie Gallant;Christoph Mark;Paolo Pazzaglia;Johannes von Keler;Laura Beermann;Kevin Schmidt;Martina Maggio
The contribution of this letter is the mean-square stabilization of discrete-time Markov jump linear systems with mixed known, unknown, and time-varying transition probabilities. To handle uncertainties in the transition probabilities, we develop a control strategy utilizing mode-dependent static state feedback controllers and introduce data-based ambiguity sets that, extending existing literature, account for known, unknown and time-varying probabilities. These ambiguity sets are constructed using estimated transition matrices and probabilistic bounds derived from the Dvoretzky-Kiefer-Wolfowitz inequality. We validate the effectiveness of our method with numerical simulations on a control system subject to deadline overruns, demonstrating the improvements of incorporating partial knowledge of the transition probabilities.
{"title":"Structure-Exploiting Distributionally Robust Control of Non-Homogeneous Markov Jump Linear Systems","authors":"Melanie Gallant;Christoph Mark;Paolo Pazzaglia;Johannes von Keler;Laura Beermann;Kevin Schmidt;Martina Maggio","doi":"10.1109/LCSYS.2024.3520348","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3520348","url":null,"abstract":"The contribution of this letter is the mean-square stabilization of discrete-time Markov jump linear systems with mixed known, unknown, and time-varying transition probabilities. To handle uncertainties in the transition probabilities, we develop a control strategy utilizing mode-dependent static state feedback controllers and introduce data-based ambiguity sets that, extending existing literature, account for known, unknown and time-varying probabilities. These ambiguity sets are constructed using estimated transition matrices and probabilistic bounds derived from the Dvoretzky-Kiefer-Wolfowitz inequality. We validate the effectiveness of our method with numerical simulations on a control system subject to deadline overruns, demonstrating the improvements of incorporating partial knowledge of the transition probabilities.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3069-3074"},"PeriodicalIF":2.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962882","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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