IEEE open journal of control systems最新文献

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Cooperative Pursuit-Evasion Games With a Flat Sphere Condition 具有平球条件的追捕-逃避合作博弈
IEEE open journal of control systems Pub Date : 2025-09-01 DOI: 10.1109/OJCSYS.2025.3604640
Dejan Milutinović;Alexander Von Moll;Satyanarayana Gupta Manyam;David W. Casbeer;Isaac E. Weintraub;Meir Pachter
{"title":"Cooperative Pursuit-Evasion Games With a Flat Sphere Condition","authors":"Dejan Milutinović;Alexander Von Moll;Satyanarayana Gupta Manyam;David W. Casbeer;Isaac E. Weintraub;Meir Pachter","doi":"10.1109/OJCSYS.2025.3604640","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3604640","url":null,"abstract":"In planar pursuit-evasion differential games considering a faster pursuer and a slower evader, the interception points resulting from equilibrium strategies lie on the Apollonius circle. This property is instrumental for leveraging geometric approaches for solving multiple pursuit-evasion scenarios in the plane. In this paper, we study a pursuit-evasion differential game on a sphere and generalize the planar Apollonius circle to the spherical domain. For the differential game, we provide equilibrium strategies for all initial positions of the pursuer and evader, including a special case when they are on the opposite sides of the sphere and on the same line with the center of the sphere when there are infinitely many geodesics between the two players. In contrast to planar scenarios, on the sphere we find that the interception point from the equilibrium strategies can leave the Apollonius domain boundary. We present a condition to ensure the intercept point remains on the boundary of the Apollonius domain. This condition allows for generalizing planar pursuit-evasion strategies to the sphere, and we show how these results are applied by analyzing the scenarios of target guarding and two-pursuer, single evader differential games on the sphere.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"360-372"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141640","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}
引用次数: 0
Gaussian Process Supported Stochastic MPC for Distribution Grids 配电网高斯过程支持的随机MPC算法
IEEE open journal of control systems Pub Date : 2025-08-22 DOI: 10.1109/OJCSYS.2025.3601836
Moritz Wenzel;Edoardo De Din;Marcel Zimmer;Andrea Benigni
{"title":"Gaussian Process Supported Stochastic MPC for Distribution Grids","authors":"Moritz Wenzel;Edoardo De Din;Marcel Zimmer;Andrea Benigni","doi":"10.1109/OJCSYS.2025.3601836","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3601836","url":null,"abstract":"The efficacy of control systems for distribution grids can be influenced by different sources of uncertainty. Stochastic Model Predictive Control (SMPC) can be employed to compensate for such uncertainties by integrating their probability distribution into the control problem. An efficient SMPC algorithm for online control applications is the stochastic tube SMPC, which is able to treat the evaluation of the chance constraints analytically. However, this approach is efficient only when the calculation of the constraint back-off is applied to a linear model. To address this issue, this work employs Gaussian Processes to approximate the nonlinear part of the power flow equations based on offline training, which is integrated into the SMPC formulation. The resulting SMPC is first validated and then tested on a benchmark system, comparing the results with Deterministic MPC and SMPC that excludes Gaussian Processes. The proposed SMPC proves to be more efficient in terms of cost minimization, reference tracking and voltage violationreduction.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"332-348"},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027992","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}
引用次数: 0
Integrating Reinforcement Learning and Model Predictive Control for Mixed- Logical Dynamical Systems 混合逻辑动力系统强化学习与模型预测控制的集成
IEEE open journal of control systems Pub Date : 2025-08-21 DOI: 10.1109/OJCSYS.2025.3601435
Caio Fabio Oliveira da Silva;Azita Dabiri;Bart De Schutter
{"title":"Integrating Reinforcement Learning and Model Predictive Control for Mixed- Logical Dynamical Systems","authors":"Caio Fabio Oliveira da Silva;Azita Dabiri;Bart De Schutter","doi":"10.1109/OJCSYS.2025.3601435","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3601435","url":null,"abstract":"This work proposes an approach that integrates reinforcement learning (RL) and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical systems efficiently. Optimization-based control of such systems with discrete and continuous decision variables entails the online solution of mixed-integer linear programs, which suffer from the curse of dimensionality. In the proposed approach, by repeated interaction with a simulator of the system, a reinforcement learning agent is trained to provide a policy for the discrete decision variables. During online operation, the RL policy simplifies the online optimization problem of the MPC controller from a mixed-integer linear program to a linear program, significantly reducing the computation time. A fundamental contribution of this work is the definition of the decoupled Q-function, which plays a crucial role in making the learning problem tractable in a combinatorial action space. We motivate the use of recurrent neural networks to approximate the decoupled Q-function and show how they can be employed in a reinforcement learning setting. A microgrid system is used as an illustrative example where real-world data is used to demonstrate that the proposed method substantially reduces the maximum online computation time of MPC (up to <inline-formula><tex-math>$20times$</tex-math></inline-formula>) while maintaining high feasibility and average optimality gap lower than 1.1% .","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"316-331"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027930","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}
引用次数: 0
MDP-Based High-Level Decision-Making for Combining Safety and Optimality: Autonomous Overtaking 基于mdp的安全性与最优性相结合的高层决策:自动超车
IEEE open journal of control systems Pub Date : 2025-08-20 DOI: 10.1109/OJCSYS.2025.3600925
Xue-Fang Wang;Jingjing Jiang;Wen-Hua Chen
{"title":"MDP-Based High-Level Decision-Making for Combining Safety and Optimality: Autonomous Overtaking","authors":"Xue-Fang Wang;Jingjing Jiang;Wen-Hua Chen","doi":"10.1109/OJCSYS.2025.3600925","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3600925","url":null,"abstract":"This paper presents a novel solution for optimal high-level decision-making in autonomous overtaking on two-lane roads, considering both opposite-direction and same-direction traffic. The proposed solutionaccounts for key factors such as safety and optimality, while also ensuring recursive feasibility and stability.To safely complete overtaking maneuvers, the solution is built on a constrained Markov decision process (MDP) that generates optimal decisions for path planners. By combining MDP with model predictive control (MPC), the approach guarantees recursive feasibility and stability through a baseline control policy that calculates the terminal cost and is incorporated into a constructed Lyapunov function. The proposed solution is validated through five simulated driving scenarios, demonstrating its robustness in handling diverse interactions within dynamic and complex traffic conditions.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"299-315"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11130904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021272","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}
引用次数: 0
Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning 基于网络控制理论和秩编码的图机学习特征构建
IEEE open journal of control systems Pub Date : 2025-08-15 DOI: 10.1109/OJCSYS.2025.3599371
Anwar Said;Yifan Wei;Obaid Ullah Ahmad;Mudassir Shabbir;Waseem Abbas;Xenofon Koutsoukos
{"title":"Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning","authors":"Anwar Said;Yifan Wei;Obaid Ullah Ahmad;Mudassir Shabbir;Waseem Abbas;Xenofon Koutsoukos","doi":"10.1109/OJCSYS.2025.3599371","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3599371","url":null,"abstract":"In this article, we utilize the concept of average controllability in graphs, along with a novel rank encoding method, to enhance the performance of Graph Neural Networks (GNNs) in social network classification tasks. GNNs have proven highly effective in various network-based learning applications and require some form of node features to function. However, their performance is heavily influenced by the expressiveness of these features. In social networks, node features are often unavailable due to privacy constraints or the absence of inherent attributes, making it challenging for GNNs to achieve optimal performance. To address this limitation, we propose two strategies for constructing expressive node features. First, we introduce average controllability along with other centrality metrics (denoted as NCT-EFA) as node-level metrics that capture critical aspects of network topology. Building on this, we develop a rank encoding method that transforms average controllability—or any other graph-theoretic metric—into a fixed-dimensional feature space, thereby improving feature representation. We conduct extensive numerical evaluations using six benchmark GNN models across four social network datasets to compare different node feature construction methods. Our results demonstrate that incorporating average controllability into the feature space significantly improves GNN performance. Moreover, the proposed rank encoding method outperforms traditional one-hot degree encoding, improving the ROC AUC from 68.7% to 73.9% using GraphSAGE on the GitHub Stargazers dataset, underscoring its effectiveness in generating expressive and efficient node representations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"288-298"},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11126872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021271","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}
引用次数: 0
The Capture-the-Flag Differential Game: Attack, Transition and Retreat 夺旗微分博弈:进攻、转换和撤退
IEEE open journal of control systems Pub Date : 2025-08-14 DOI: 10.1109/OJCSYS.2025.3599473
Eloy Garcia;David W. Casbeer
{"title":"The Capture-the-Flag Differential Game: Attack, Transition and Retreat","authors":"Eloy Garcia;David W. Casbeer","doi":"10.1109/OJCSYS.2025.3599473","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3599473","url":null,"abstract":"This paper analyzes the classic game of capture-the-flag, modeled as a conflict between an Attacker and a Defender. The game unfolds in distinct phases with changing objectives: first, the Attacker tries to capture a flag while the Defender attempts to intercept; second, if successful, the Attacker tries to reach a safe zone while the Defender again seeks interception. We mathematically derive the optimal state-feedback strategies for both players and the associated Value function for each phase, rigorously proving their correctness. A key contribution is introducing the transition phase, where we analyze the Defender’s optimal repositioning strategy when flag capture becomes inevitable, preparing it for the game’s second phase. This novel transition connects the game’s stages, critically enabling us to solve the overall Game of Kind – determining the winner from any starting condition – and define the precise circumstances under which the Attacker can both capture the flag and successfully escape to the safe zone.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"271-287"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11125922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998218","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}
引用次数: 0
Generalized Concentration-Based Performance Guarantees on Sensor Selection for State Estimation 基于广义浓度的状态估计传感器选择性能保证
IEEE open journal of control systems Pub Date : 2025-08-13 DOI: 10.1109/OJCSYS.2025.3598626
Christopher I. Calle;Shaunak D. Bopardikar
{"title":"Generalized Concentration-Based Performance Guarantees on Sensor Selection for State Estimation","authors":"Christopher I. Calle;Shaunak D. Bopardikar","doi":"10.1109/OJCSYS.2025.3598626","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3598626","url":null,"abstract":"In this work, we apply concentration-based results to the problem of sensor selection for state estimation to provide us with meaningful guarantees on the properties of our selection. We consider a selection of sensors that is randomly chosen with replacement for a stochastic linear dynamical system, and we utilize the Kalman filter to perform state estimation. Our main contributions are four-fold. First, we derive novel matrix concentration inequalities (CIs) for a sum of positive semi-definite random matrices. Second, we provide two algorithms for specifying the parameters required to apply our matrix CIs, a novel statistical tool. Third, we propose two avenues for improving the sample complexity of this statistical tool. Fourth, we provide a procedure for optimizing the semi-definite bounds of our matrix CIs. When our matrix CIs are applied to the problem of sensor selection for state estimation, our final contribution is a procedure for optimizing the filtered state estimation error covariance matrix of the Kalman filter. Finally, we show through simulations that our bounds significantly outperform those of an existing matrix CI and are applicable for a larger parameter regime. Also, we demonstrate the applicability of our matrix CIs for the state estimation of nonlinear dynamical systems.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"250-270"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11123730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990018","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}
引用次数: 0
Controllability and Observability of Heterogeneous Networked Systems With Non-Uniform Node Dimensions and Distinct Inner-Coupling Matrices 具有非均匀节点维数和不同内耦合矩阵的异构网络系统的可控性和可观察性
IEEE open journal of control systems Pub Date : 2025-07-10 DOI: 10.1109/OJCSYS.2025.3587537
Aleena Thomas;Abhijith Ajayakumar;Raju K. George
{"title":"Controllability and Observability of Heterogeneous Networked Systems With Non-Uniform Node Dimensions and Distinct Inner-Coupling Matrices","authors":"Aleena Thomas;Abhijith Ajayakumar;Raju K. George","doi":"10.1109/OJCSYS.2025.3587537","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3587537","url":null,"abstract":"In this paper, controllability and observability of a heterogeneous networked system with Linear Time Invariant (LTI) nodal systems having Multiple-Inputs and Multiple-Outputs (MIMO) aligned in a weighted and directed network topology are studied. Apart from the heterogenity in nodal dynamics, the inner-coupling matrices that quantify the interactions among nodes are also different. In contrast to the existing literature, the system under consideration has distinct node dimensions, which adds to the generality. Necessary and sufficient conditions for controllability and observability as well as certain necessary conditions for controllability of a class of networked systems are established. These conditions show the dependence of network controllability and observability on various node and network-specific factors. As a practical application, a three-sector economy is modelled as a heterogeneous networked system with distinct node dimensions and its controllability is analysed. Computational time in floating point operations (flops) of the proposed methods are estimated, which indicates their efficiency on comparison with the classical conditions. This is illustrated by computational comparison of the existing and proposed schemes, applied to a randomly generated networked system. Also, robustness of the proposed schemes are analysed with the example of randomly generated networked systems. All the results are supported with illustrative numerical examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"219-235"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831949","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}
引用次数: 0
Time-Series Out-of-Distribution Data Detection in Mechanical Ventilation 机械通气时间序列非分布数据检测
IEEE open journal of control systems Pub Date : 2025-07-02 DOI: 10.1109/OJCSYS.2025.3585427
L. van de Kamp;B. Hunnekens;T. Oomen;N. van de Wouw
{"title":"Time-Series Out-of-Distribution Data Detection in Mechanical Ventilation","authors":"L. van de Kamp;B. Hunnekens;T. Oomen;N. van de Wouw","doi":"10.1109/OJCSYS.2025.3585427","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3585427","url":null,"abstract":"Safe deployment of neural networks to classify time series in safety-critical applications relies on the ability of the classifier to detect data that does not originate from the same distribution as the training data. The aim of this paper is to propose a framework for detecting whether time-series data is sampled from a different distribution than the training data, known as the problem of <italic>out-of-distribution</i> (OOD) detection. We propose a novel distance-based OOD method for time-series data using a hierarchical clustering method together with dynamic time-warping to measure the difference between a new data instance and the training set. The method is evaluated in the context of mechanical ventilation, a safety critical application, using both simulated and clinical datasets. Results of the mechanical ventilation use case demonstrate that the proposed approach effectively detects out-of-distribution data and improves classification performance in diverse settings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"236-249"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11066264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914214","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}
引用次数: 0
Optimistic Algorithms for Safe Linear Bandits Under General Constraints 一般约束下安全线性强盗的乐观算法
IEEE open journal of control systems Pub Date : 2025-04-07 DOI: 10.1109/OJCSYS.2025.3558118
Spencer Hutchinson;Arghavan Zibaie;Ramtin Pedarsani;Mahnoosh Alizadeh
{"title":"Optimistic Algorithms for Safe Linear Bandits Under General Constraints","authors":"Spencer Hutchinson;Arghavan Zibaie;Ramtin Pedarsani;Mahnoosh Alizadeh","doi":"10.1109/OJCSYS.2025.3558118","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3558118","url":null,"abstract":"The stochastic linear bandit problem has emerged as a fundamental building-block in machine learning and control, and a realistic model for many applications. By equipping this classical problem with safety constraints, the <italic>safe linear bandit problem</i> further broadens its relevance to safety-critical applications. However, most existing algorithms for safe linear bandits only consider <italic>linear constraints</i>, making them inadequate for many real-world applications, which often have non-linear constraints. To alleviate this limitation, we study the problem of safe linear bandits under general (non-linear) constraints. Under a novel constraint regularity condition that is weaker than convexity, we give two algorithms with <inline-formula><tex-math>$tilde{mathcal {O}}(d sqrt{T})$</tex-math></inline-formula> regret. We then give efficient implementations of these algorithms for several specific settings. Lastly, we give simulation results demonstrating the effectiveness of our algorithms in choosing dynamic pricing signals for a demand response problem under distribution power flow constraints.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"103-116"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10950393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896165","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}
引用次数: 0
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