Pub Date : 2024-12-11DOI: 10.1109/LCSYS.2024.3516636
Vade Shah;Keith Paarporn;Jason R. Marden
When multiple agents are engaged in a network of conflict, some can advance their competitive positions by forming alliances with each other. However, the costs associated with establishing an alliance may outweigh the potential benefits. This study investigates costly alliance formation in the framework of coalitional Blotto games, in which two players compete separately against a common adversary and are able to collude by exchanging resources with one another. Previous work has shown that both players in the alliance can mutually benefit if one player unilaterally donates, or transfers, a portion of their budget to the other. In this letter, we consider a variation where the transfer of resources is inherently inefficient, meaning that the recipient of the transfer only receives a fraction of the donation. Our findings reveal that even in the presence of inefficiencies, mutually beneficial transfers are still possible. More formally, our main result provides necessary and sufficient conditions for the existence of such transfers, offering insights into the robustness of alliance formation in competitive environments with resource constraints.
{"title":"Inefficient Alliance Formation in Coalitional Blotto Games","authors":"Vade Shah;Keith Paarporn;Jason R. Marden","doi":"10.1109/LCSYS.2024.3516636","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516636","url":null,"abstract":"When multiple agents are engaged in a network of conflict, some can advance their competitive positions by forming alliances with each other. However, the costs associated with establishing an alliance may outweigh the potential benefits. This study investigates costly alliance formation in the framework of coalitional Blotto games, in which two players compete separately against a common adversary and are able to collude by exchanging resources with one another. Previous work has shown that both players in the alliance can mutually benefit if one player unilaterally donates, or transfers, a portion of their budget to the other. In this letter, we consider a variation where the transfer of resources is inherently inefficient, meaning that the recipient of the transfer only receives a fraction of the donation. Our findings reveal that even in the presence of inefficiencies, mutually beneficial transfers are still possible. More formally, our main result provides necessary and sufficient conditions for the existence of such transfers, offering insights into the robustness of alliance formation in competitive environments with resource constraints.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2907-2912"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976172","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}
The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand large sample sizes, making it intractable for safety-critical applications that require very low levels of constraint violation. To address this challenge, we propose a novel yet simple constraint scaling method, inspired by large deviations theory. Under mild nonparametric conditions on the underlying probability distribution, we show that our method yields an exponential reduction in sample size requirements for bilinear constraints with low violation levels compared to the classical approach, thereby significantly improving computational tractability. Numerical experiments on robust pole assignment problems support our theoretical findings.
{"title":"Reduced Sample Complexity in Scenario-Based Control System Design via Constraint Scaling","authors":"Jaeseok Choi;Anand Deo;Constantino Lagoa;Anirudh Subramanyam","doi":"10.1109/LCSYS.2024.3515861","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3515861","url":null,"abstract":"The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand large sample sizes, making it intractable for safety-critical applications that require very low levels of constraint violation. To address this challenge, we propose a novel yet simple constraint scaling method, inspired by large deviations theory. Under mild nonparametric conditions on the underlying probability distribution, we show that our method yields an exponential reduction in sample size requirements for bilinear constraints with low violation levels compared to the classical approach, thereby significantly improving computational tractability. Numerical experiments on robust pole assignment problems support our theoretical findings.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2793-2798"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858886","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3515858
Weizhen Wang;Jianping He;Xiaoming Duan
We study the inherent trade-off in Markov chain-based surveillance strategies between the efficiency, as measured by Kemeny’s constant, and unpredictability, as measured by the entropy rate. We first formulate a multi-objective optimization problem to account for these two criteria and demonstrate the intrinsic contradiction between them, emphasizing the need for a trade-off through the concept of Pareto optimality. We then employ the $varepsilon $ -constraint method to approximate the Pareto curve and illustrate its concavity and strict monotonicity. Due to the lack of a natural order, the points along the Pareto curve are noncomparable and we introduce two additional metrics—the distance to an ideal point and the mixing rate—to discriminate over different Pareto optimal solutions. We demonstrate that the optimal Markov chain minimizing the distance to an ideal point can be identified through convex optimization. While for optimizing the mixing rate over the Pareto curve, we first analyze several tractable examples to establish some intuitions and then propose a bisection-based heuristic algorithm.
{"title":"On the Trade-Off Between Efficiency and Unpredictability in Stochastic Robotic Surveillance","authors":"Weizhen Wang;Jianping He;Xiaoming Duan","doi":"10.1109/LCSYS.2024.3515858","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3515858","url":null,"abstract":"We study the inherent trade-off in Markov chain-based surveillance strategies between the efficiency, as measured by Kemeny’s constant, and unpredictability, as measured by the entropy rate. We first formulate a multi-objective optimization problem to account for these two criteria and demonstrate the intrinsic contradiction between them, emphasizing the need for a trade-off through the concept of Pareto optimality. We then employ the \u0000<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>\u0000-constraint method to approximate the Pareto curve and illustrate its concavity and strict monotonicity. Due to the lack of a natural order, the points along the Pareto curve are noncomparable and we introduce two additional metrics—the distance to an ideal point and the mixing rate—to discriminate over different Pareto optimal solutions. We demonstrate that the optimal Markov chain minimizing the distance to an ideal point can be identified through convex optimization. While for optimizing the mixing rate over the Pareto curve, we first analyze several tractable examples to establish some intuitions and then propose a bisection-based heuristic algorithm.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2829-2834"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858892","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3516635
Majid Shahbazzadeh;Christopher M. Richards
This letter considers the problem of trajectory tracking for quadrotors operating in wind conditions that result in propeller thrust saturation. To address this problem, an anti-windup compensator (AWC) is developed to reduce the tracking performance degradation and destabilizing effects from thrust saturation. Relationships are derived showing how the tracking error and AWC states are influenced by the wind disturbance and saturation, and how the influences depend on the controller and AWC gains. As a result, these gains can be tuned to achieve desired performance levels. Simulation results are presented to validate the effectiveness of the proposed method.
{"title":"Anti-Windup Compensation for Quadrotor Trajectory Tracking With External Disturbances","authors":"Majid Shahbazzadeh;Christopher M. Richards","doi":"10.1109/LCSYS.2024.3516635","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516635","url":null,"abstract":"This letter considers the problem of trajectory tracking for quadrotors operating in wind conditions that result in propeller thrust saturation. To address this problem, an anti-windup compensator (AWC) is developed to reduce the tracking performance degradation and destabilizing effects from thrust saturation. Relationships are derived showing how the tracking error and AWC states are influenced by the wind disturbance and saturation, and how the influences depend on the controller and AWC gains. As a result, these gains can be tuned to achieve desired performance levels. Simulation results are presented to validate the effectiveness of the proposed method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2913-2918"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975931","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3515879
Shayan Meshkat Alsadat;Zhe Xu
We study the use of large language models (LLMs) to integrate high-level knowledge in stochastic games using reinforcement learning with reward machines to encode non-Markovian and Markovian reward functions. In non-cooperative games, one challenge is to provide agents with knowledge about the task efficiently to speed up the convergence to an optimal policy. We aim to provide this knowledge in the form of deterministic finite automata (DFA) generated by LLMs (LLM-generated DFA). Additionally, we use reward machines (RMs) to encode the temporal structure of the game and the non-Markovian or Markovian reward functions. Our proposed algorithm, LLM-generated DFA for Multi-agent Reinforcement Learning with Reward Machines for Stochastic Games (StochQ-RM), can learn an equivalent reward machine to the ground truth reward machine (specified task) in the environment using the LLM-generated DFA. Additionally, we propose DFA-based q-learning with reward machines (DBQRM) to find the best responses for each agent using Nash equilibrium in stochastic games. Despite the fact that the LLMs are known to hallucinate, we show that our method is robust and guaranteed to converge to an optimal policy. Furthermore, we study the performance of our proposed method in three case studies.
我们研究了使用大型语言模型（llm）来整合随机博弈中的高级知识，使用强化学习和奖励机来编码非马尔可夫和马尔可夫奖励函数。在非合作博弈中，一个挑战是如何有效地向智能体提供有关任务的知识，以加速向最优策略的收敛。我们的目标是以llm生成的确定性有限自动机（DFA）的形式提供这些知识（llm生成的DFA）。此外，我们使用奖励机（RMs）来编码游戏的时间结构和非马尔可夫或马尔可夫奖励函数。我们提出的算法，llm生成的基于随机博弈奖励机的多智能体强化学习的DFA（随机博弈奖励机），可以使用llm生成的DFA在环境中学习到与地面真相奖励机（指定任务）等效的奖励机。此外，我们提出了基于dfa的q-learning with reward machines (DBQRM)，利用随机博弈中的纳什均衡为每个agent找到最佳响应。尽管已知llm会产生幻觉，但我们证明了我们的方法是鲁棒的，并保证收敛到最优策略。此外，我们在三个案例中研究了我们提出的方法的性能。
{"title":"Multi-Agent Reinforcement Learning in Non-Cooperative Stochastic Games Using Large Language Models","authors":"Shayan Meshkat Alsadat;Zhe Xu","doi":"10.1109/LCSYS.2024.3515879","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3515879","url":null,"abstract":"We study the use of large language models (LLMs) to integrate high-level knowledge in stochastic games using reinforcement learning with reward machines to encode non-Markovian and Markovian reward functions. In non-cooperative games, one challenge is to provide agents with knowledge about the task efficiently to speed up the convergence to an optimal policy. We aim to provide this knowledge in the form of deterministic finite automata (DFA) generated by LLMs (LLM-generated DFA). Additionally, we use reward machines (RMs) to encode the temporal structure of the game and the non-Markovian or Markovian reward functions. Our proposed algorithm, LLM-generated DFA for Multi-agent Reinforcement Learning with Reward Machines for Stochastic Games (StochQ-RM), can learn an equivalent reward machine to the ground truth reward machine (specified task) in the environment using the LLM-generated DFA. Additionally, we propose DFA-based q-learning with reward machines (DBQRM) to find the best responses for each agent using Nash equilibrium in stochastic games. Despite the fact that the LLMs are known to hallucinate, we show that our method is robust and guaranteed to converge to an optimal policy. Furthermore, we study the performance of our proposed method in three case studies.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2757-2762"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858883","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3516632
Anand Gokhale;Alexander Davydov;Francesco Bullo
In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsmooth convex and a smooth component. First, we show that the cost function decreases monotonically along the trajectories of the proximal gradient dynamics. We then introduce a new condition that guarantees exponential convergence of the cost function to its optimal value, and show that this condition implies the proximal Polyak-Łojasiewicz condition. We also show that the proximal Polyak-Łojasiewicz condition guarantees exponential convergence of the cost function. Moreover, we extend these results to time-varying optimization problems, providing bounds for equilibrium tracking. Finally, we discuss applications of these findings, including the LASSO problem, certain matrix based problems and a numerical experiment on a feed-forward neural network.
{"title":"Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications","authors":"Anand Gokhale;Alexander Davydov;Francesco Bullo","doi":"10.1109/LCSYS.2024.3516632","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516632","url":null,"abstract":"In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsmooth convex and a smooth component. First, we show that the cost function decreases monotonically along the trajectories of the proximal gradient dynamics. We then introduce a new condition that guarantees exponential convergence of the cost function to its optimal value, and show that this condition implies the proximal Polyak-Łojasiewicz condition. We also show that the proximal Polyak-Łojasiewicz condition guarantees exponential convergence of the cost function. Moreover, we extend these results to time-varying optimization problems, providing bounds for equilibrium tracking. Finally, we discuss applications of these findings, including the LASSO problem, certain matrix based problems and a numerical experiment on a feed-forward neural network.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2853-2858"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880452","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 : 2024-12-11DOI: 10.1109/LCSYS.2024.3516672
Zihao Song;Panos J. Antsaklis;Hai Lin
Rigid body platoons are widely applied in many scenarios, such as planar vehicular platoons, satellite networks, and aerial/underwater navigation formations. Like string stability, mesh stability is adopted in these higher dimensional platoons to capture the non-increasing tracking errors over the networks. In this letter, we extend the traditional vehicular platooning control to higher dimensional rigid body scenarios with mesh stability concerns. The main challenges stem from the inherent underactuation of rigid body dynamics, the nonlinearity introduced by the $SOtextit {(}3textit {)}$ -based rotations, and the maintenance of mesh stability for all formations. To this end, we first apply the notion of $l_{2}$ weak mesh stability to capture the effect of propagation of errors over the network. Then, by assuming all the followers have access to the leader’s information, we propose a novel and constructive rigid body platooning control method based on the port-Hamiltonian framework, which also guarantees the $l_{2}$ weak mesh stability. This designed controller is further refined for the case when each follower only knows the neighboring information. Finally, the effectiveness of the proposed methods is verified via numerical simulations.
{"title":"Port-Hamiltonian-Based Geometric Control for Rigid Body Platoons With Mesh Stability Guarantee","authors":"Zihao Song;Panos J. Antsaklis;Hai Lin","doi":"10.1109/LCSYS.2024.3516672","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516672","url":null,"abstract":"Rigid body platoons are widely applied in many scenarios, such as planar vehicular platoons, satellite networks, and aerial/underwater navigation formations. Like string stability, mesh stability is adopted in these higher dimensional platoons to capture the non-increasing tracking errors over the networks. In this letter, we extend the traditional vehicular platooning control to higher dimensional rigid body scenarios with mesh stability concerns. The main challenges stem from the inherent underactuation of rigid body dynamics, the nonlinearity introduced by the \u0000<inline-formula> <tex-math>$SOtextit {(}3textit {)}$ </tex-math></inline-formula>\u0000-based rotations, and the maintenance of mesh stability for all formations. To this end, we first apply the notion of \u0000<inline-formula> <tex-math>$l_{2}$ </tex-math></inline-formula>\u0000 weak mesh stability to capture the effect of propagation of errors over the network. Then, by assuming all the followers have access to the leader’s information, we propose a novel and constructive rigid body platooning control method based on the port-Hamiltonian framework, which also guarantees the \u0000<inline-formula> <tex-math>$l_{2}$ </tex-math></inline-formula>\u0000 weak mesh stability. This designed controller is further refined for the case when each follower only knows the neighboring information. Finally, the effectiveness of the proposed methods is verified via numerical simulations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2805-2810"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858891","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 : 2024-12-09DOI: 10.1109/LCSYS.2024.3513238
Miel Sharf;Daniel Zelazo
This letter provides a characterization of linear passivizing input-output transformations for MIMO systems with known passivity indices. Building on recent results for SISO systems, we show that any transformation mapping an I/O $({rho },~{nu })$ -passive MIMO system to an I/O $(rho ^{*},nu ^{star })$ -passive system can be expressed as the product of three matrices - two depending on the original and desired passivity indices, and a matrix satisfying a matrix inequality. This parameterization enables formulation of optimal passivation problems that we explore as an application example.
{"title":"A Characterization of Passivizing Input-Output Transformations of Nonlinear MIMO Systems","authors":"Miel Sharf;Daniel Zelazo","doi":"10.1109/LCSYS.2024.3513238","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3513238","url":null,"abstract":"This letter provides a characterization of linear passivizing input-output transformations for MIMO systems with known passivity indices. Building on recent results for SISO systems, we show that any transformation mapping an I/O \u0000<inline-formula> <tex-math>$({rho },~{nu })$ </tex-math></inline-formula>\u0000-passive MIMO system to an I/O \u0000<inline-formula> <tex-math>$(rho ^{*},nu ^{star })$ </tex-math></inline-formula>\u0000-passive system can be expressed as the product of three matrices - two depending on the original and desired passivity indices, and a matrix satisfying a matrix inequality. This parameterization enables formulation of optimal passivation problems that we explore as an application example.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2733-2738"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821292","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 : 2024-12-09DOI: 10.1109/LCSYS.2024.3514995
Nicolas Chatzikiriakos;Andrea Iannelli
This letter considers a finite sample perspective on the problem of identifying an LTI system from a finite set of possible systems using trajectory data. To this end, we use the maximum likelihood estimator to identify the true system and provide an upper bound for its sample complexity. Crucially, the derived bound does not rely on a potentially restrictive stability assumption. Additionally, we leverage tools from information theory to provide a lower bound to the sample complexity that holds independently of the used estimator. The derived sample complexity bounds are analyzed analytically and numerically.
{"title":"Sample Complexity Bounds for Linear System Identification From a Finite Set","authors":"Nicolas Chatzikiriakos;Andrea Iannelli","doi":"10.1109/LCSYS.2024.3514995","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514995","url":null,"abstract":"This letter considers a finite sample perspective on the problem of identifying an LTI system from a finite set of possible systems using trajectory data. To this end, we use the maximum likelihood estimator to identify the true system and provide an upper bound for its sample complexity. Crucially, the derived bound does not rely on a potentially restrictive stability assumption. Additionally, we leverage tools from information theory to provide a lower bound to the sample complexity that holds independently of the used estimator. The derived sample complexity bounds are analyzed analytically and numerically.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2751-2756"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858922","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 : 2024-12-09DOI: 10.1109/LCSYS.2024.3514702
Zhuo-Rui Pan;Wenbang Wang;Wei Ren;Xi-Ming Sun
Physical systems in the real world are usually constrained due to different considerations. These constraints are closely related to the system safety and stability. In this letter we investigate the optimal stabilization control problem of stochastic time-delay systems under safety constraints. We first follow the Razumikhin approach to propose stochastic control Lyapunov and barrier functions, which result in the closed-form controllers for the stabilization and safety control individually. Next, based on the modification of the quadratic programming, an optimization problem is established to address the stabilization control under safe constraints. The optimal controller is derived explicitly in a switching form to tradeoff the stabilization and safety requirements. Finally, a numerical example is presented to illustrate the proposed control strategy.
{"title":"Optimal Constrained Stabilization of Stochastic Time-Delay Systems","authors":"Zhuo-Rui Pan;Wenbang Wang;Wei Ren;Xi-Ming Sun","doi":"10.1109/LCSYS.2024.3514702","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3514702","url":null,"abstract":"Physical systems in the real world are usually constrained due to different considerations. These constraints are closely related to the system safety and stability. In this letter we investigate the optimal stabilization control problem of stochastic time-delay systems under safety constraints. We first follow the Razumikhin approach to propose stochastic control Lyapunov and barrier functions, which result in the closed-form controllers for the stabilization and safety control individually. Next, based on the modification of the quadratic programming, an optimization problem is established to address the stabilization control under safe constraints. The optimal controller is derived explicitly in a switching form to tradeoff the stabilization and safety requirements. Finally, a numerical example is presented to illustrate the proposed control strategy.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2775-2780"},"PeriodicalIF":2.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858924","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：