IEEE Control Systems Letters最新文献

Safety Verification of Stochastic Systems: A Set-Erosion Approach

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-16 DOI: 10.1109/LCSYS.2024.3518394

Zishun Liu;Saber Jafarpour;Yongxin Chen

引用次数: 0

Traffic Density Control for Heterogeneous Highway Systems With Input Constraints

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3516073

Arash Rahmanidehkordi;Amir H. Ghasemi

{"title":"Traffic Density Control for Heterogeneous Highway Systems With Input Constraints","authors":"Arash Rahmanidehkordi;Amir H. Ghasemi","doi":"10.1109/LCSYS.2024.3516073","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3516073","url":null,"abstract":"This letter introduces a traffic management algorithm for heterogeneous highway corridors consisting of both human-driven vehicles (HVs) and autonomous vehicles (AVs). The traffic flow dynamics are modeled using the heterogeneous METANET model, with variable speed control employed to maintain desired vehicle densities and reduce congestion. To generate speed control commands, we developed a hybrid framework that combines feedback linearization (FL) and model predictive control (MPC), treating the traffic system as an over-actuated, constrained nonlinear system. The FL component linearizes the nonlinear dynamics, while the MPC component handles constraints by generating virtual control inputs that ensure control limits are respected. To address the over-actuated nature of the system, we introduce a novel constraint mapping algorithm within the MPC that links virtual control input constraints to the actual control commands. Additionally, we propose a real-time reference density generation method that accounts for both AVs and HVs to mitigate congestion. Numerical simulations were conducted for two scenarios: controlling only AVs and controlling both AVs and HVs. The results demonstrate that the proposed FL-MPC framework effectively reduces congestion, even when speed control is applied exclusively to AVs.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2787-2792"},"PeriodicalIF":2.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858882","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}

引用次数: 0

Reduced Sample Complexity in Scenario-Based Control System Design via Constraint Scaling

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3515861

Jaeseok Choi;Anand Deo;Constantino Lagoa;Anirudh Subramanyam

引用次数: 0

On the Trade-Off Between Efficiency and Unpredictability in Stochastic Robotic Surveillance

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3515858

Weizhen Wang;Jianping He;Xiaoming Duan

{"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}

引用次数: 0

Multi-Agent Reinforcement Learning in Non-Cooperative Stochastic Games Using Large Language Models

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3515879

Shayan Meshkat Alsadat;Zhe Xu

{"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}

引用次数: 0

Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3516632

Anand Gokhale;Alexander Davydov;Francesco Bullo

引用次数: 0

Port-Hamiltonian-Based Geometric Control for Rigid Body Platoons With Mesh Stability Guarantee

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-11 DOI: 10.1109/LCSYS.2024.3516672

Zihao Song;Panos J. Antsaklis;Hai Lin

{"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}

引用次数: 0

A Characterization of Passivizing Input-Output Transformations of Nonlinear MIMO Systems 非线性多输入多输出系统钝化输入输出变换的特征描述

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-09 DOI: 10.1109/LCSYS.2024.3513238

Miel Sharf;Daniel Zelazo

引用次数: 0

Sample Complexity Bounds for Linear System Identification From a Finite Set

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-12-09 DOI: 10.1109/LCSYS.2024.3514995

Nicolas Chatzikiriakos;Andrea Iannelli

引用次数: 0

Optimal Constrained Stabilization of Stochastic Time-Delay Systems