IEEE open journal of control systems最新文献_第7页

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

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

引用次数: 0

Vision-Based Collision Avoidance for Multi-Agent Systems With Intermittent Measurements 基于视觉的间歇测量多智能体系统避碰

IEEE open journal of control systems Pub Date : 2025-08-13 DOI: 10.1109/OJCSYS.2025.3598673

Mia Scoblic;Camilla Tabasso;Venanzio Cichella;Isaac Kaminer

{"title":"Vision-Based Collision Avoidance for Multi-Agent Systems With Intermittent Measurements","authors":"Mia Scoblic;Camilla Tabasso;Venanzio Cichella;Isaac Kaminer","doi":"10.1109/OJCSYS.2025.3598673","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3598673","url":null,"abstract":"Collision avoidance is a fundamental aspect of many applications involving autonomous vehicles. Solving this problem becomes especially challenging when the agents involved cannot communicate. In these scenarios, onboard sensors are essential for detecting and avoiding other vehicles or obstacles. However, in many practical applications, sensors have limited range and measurements may be intermittent due to external factors. With this in mind, in this work, we present a novel decentralized vision-based collision avoidance algorithm which does not require communication among the agents and has mild assumptions on the sensing capabilities of the vehicles. Once a collision is detected, the agents replan their trajectories to follow a circular path centered at the point of collision. A feedback control law is designed so that the vehicles can maintain a predefined phase shift along this circle and therefore are able to avoid collisions. A Lyapunov analysis is performed to provide performance bounds and the efficacy of the proposed method is demonstrated through both simulated and experimental results.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"349-359"},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11123838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778185","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

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

Scalable Distributed Reproduction Numbers of Network Epidemics With Differential Privacy 具有差分隐私的网络流行病的可伸缩分布再现数

IEEE open journal of control systems Pub Date : 2025-03-30 DOI: 10.1109/OJCSYS.2025.3575305

Bo Chen;Baike She;Calvin Hawkins;Philip E. Paré;Matthew T. Hale

{"title":"Scalable Distributed Reproduction Numbers of Network Epidemics With Differential Privacy","authors":"Bo Chen;Baike She;Calvin Hawkins;Philip E. Paré;Matthew T. Hale","doi":"10.1109/OJCSYS.2025.3575305","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3575305","url":null,"abstract":"Reproduction numbers are widely used to analyze epidemic spreading processes over networks. However,conventional reproduction numbers of an overall network, which require spreading information from the entire network, do not indicate where an epidemic is spreading. To address this limitation, we first propose a novel class of local distributed reproduction numbers that capture spreading behaviors at the level of individual nodes. We demonstrate how to compute these values in a distributed way and use them to derive new threshold conditions for network spreading analysis. Due to the fact that epidemic data are often collected at multiple geographic or administrative scales, we then define a class of cluster distributed reproduction numbers to describe the spread between groups of nodes such as communities, cities, or states. We further show that the local distributed reproduction numbers can be aggregated to form the cluster distributed reproduction numbers. Unlike conventional network-level reproduction numbers, these distributed measures reveal fine-grained interaction patterns that may raise privacy concerns by exposing the frequency or intensity of interactions across regions. To address this issue, we propose a privacy-enhanced distributed reproduction number framework that implements differential privacy. This framework enables scalable and privacy-preserving analysis of epidemic spreading processes in networked populations through the calculation of privacy-preserving distributed reproduction numbers. Numerical experiments show that while maintaining differential privacy, the private distributed reproduction numbers yield accurate estimates of epidemic spread while also offering more insights than conventional reproduction numbers.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"199-218"},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524373","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