IEEE open journal of control systems最新文献_第6页

Collaborative Safety-Critical Control in Coupled Networked Systems 耦合网络系统中的协同安全关键控制

IEEE open journal of control systems Pub Date : 2025-09-24 DOI: 10.1109/OJCSYS.2025.3614070

Brooks A. Butler;Philip E. Paré

引用次数: 0

Cautious Optimization via Data Informativity 基于数据信息性的谨慎优化

IEEE open journal of control systems Pub Date : 2025-09-22 DOI: 10.1109/OJCSYS.2025.3612784

Jaap Eising;Jorge Cortés

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Mutual Support by Sensor-Attacker Team for a Passive Target 传感器攻击小组对被动目标的相互支持

IEEE open journal of control systems Pub Date : 2025-09-19 DOI: 10.1109/OJCSYS.2025.3612246

Prajakta Surve;Shaunak D. Bopardikar;Alexander Von Moll;Isaac Weintraub;David W. Casbeer

{"title":"Mutual Support by Sensor-Attacker Team for a Passive Target","authors":"Prajakta Surve;Shaunak D. Bopardikar;Alexander Von Moll;Isaac Weintraub;David W. Casbeer","doi":"10.1109/OJCSYS.2025.3612246","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3612246","url":null,"abstract":"We introduce a pursuit game played between a team of a sensor and an attacker and a mobile target in the unbounded Euclidean plane. The target is faster than the sensor, but slower than the attacker. The sensor’s objective is to keep the target within a sensing radius so that the attacker can capture the target, whereas the target seeks to escape by reaching beyond the sensing radius from the sensor without getting captured by the attacker. We assume that as long as the target is within the sensing radius from the sensor, the sensor-attacker team is able to measure the target’s instantaneous position and velocity. We pose and solve this problem as a <italic>Game of Kind</i> in which the target uses an open-loop strategy (passive target). Aside from the novel formulation, our contributions are four-fold. First, we present optimal strategies for both the sensor and the attacker, according to their respective objectives. Specifically, we design a sensor strategy that maximizes the duration for which the target remains within its sensing range, while the attacker uses proportional navigation to capture the target. Second, we characterize the <italic>sensable region</i> – the region in the plane in which the target remains within the sensing radius of the sensor during the game – and show that capture is guaranteed if and only if the Apollonius circle between the attacker and the target is fully contained within this region. Third, we derive a lower bound on the target’s speed below which capture is guaranteed, and an upper bound on the target speed above which there exists an escape strategy for the target, from an arbitrary initial orientation between the agents. Fourth, for a given initial orientation between the agents, we present a sharper upper bound on the target speed above which there exists an escape strategy for the target.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"418-432"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352100","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

Learning-Enabled Iterative Convex Optimization for Safety-Critical Model Predictive Control 安全关键模型预测控制的可学习迭代凸优化

IEEE open journal of control systems Pub Date : 2025-09-19 DOI: 10.1109/OJCSYS.2025.3612245

Shuo Liu;Zhe Huang;Jun Zeng;Koushil Sreenath;Calin A. Belta

{"title":"Learning-Enabled Iterative Convex Optimization for Safety-Critical Model Predictive Control","authors":"Shuo Liu;Zhe Huang;Jun Zeng;Koushil Sreenath;Calin A. Belta","doi":"10.1109/OJCSYS.2025.3612245","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3612245","url":null,"abstract":"Safety remains a central challenge in control of dynamical systems, particularly when the boundaries of unsafe sets are complex (e.g., nonconvex, nonsmooth) or unknown. This paper proposes a learning-enabled framework for safety-critical Model Predictive Control (MPC) that integrates Discrete-Time High-Order Control Barrier Functions (DHOCBFs) with iterative convex optimization. Unlike existing methods that primarily address CBFs of relative degree one with fully known unsafe set boundaries, our approach generalizes to arbitrary relative degrees and addresses scenarios where only samples are available for the unsafe set boundaries. We extract pixels from unsafe set boundaries and train a neural network to approximate local linearizations. The learned models are incorporated into the linearized DHOCBF constraints at each time step within the MPC framework. An iterative convex optimization procedure is developed to accelerate computation while maintaining formal safety guarantees. The benefits of computational performance and safe avoidance of obstacles with diverse shapes are examined and confirmed through numerical results. By bridging model-based control with learning-based environment modeling, this framework advances safe autonomy for discrete-time systems operating in complex and partially known settings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"482-500"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11174009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352025","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

Compositional Shield Synthesis for Safe Reinforcement Learning in Partial Observability 面向部分可观察性安全强化学习的组合屏蔽综合

IEEE open journal of control systems Pub Date : 2025-09-18 DOI: 10.1109/OJCSYS.2025.3611725

Steven Carr;Georgios Bakirtzis;Ufuk Topcu

{"title":"Compositional Shield Synthesis for Safe Reinforcement Learning in Partial Observability","authors":"Steven Carr;Georgios Bakirtzis;Ufuk Topcu","doi":"10.1109/OJCSYS.2025.3611725","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3611725","url":null,"abstract":"Agents controlled by the output of reinforcement learning (RL) algorithms often transition to unsafe states, particularly in uncertain and partially observable environments. Partially observable Markov decision processes (POMDPs) provide a natural setting for studying such scenarios with limited sensing. <italic>Shields</i> filter undesirable actions to ensure safe RL by preserving safety requirements in the agents’ policy. However, synthesizing holistic shields is computationally expensive in complex deployment scenarios. We propose the <italic>compositional</i> synthesis of shields by modeling safety requirements by parts, thereby improving scalability. In particular, problem formulations in the form of POMDPs using RL algorithms illustrate that an RL agent equipped with the resulting compositional shielding, beyond being safe, converges to higher values of expected reward. By using subproblem formulations, we preserve and improve the ability of shielded agents to require fewer training episodes than unshielded agents, especially in sparse-reward settings. Concretely, we find that compositional shield synthesis allows an RL agent to remain safe in environments two orders of magnitude larger than other state-of-the-art model-based approaches.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"373-384"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11172329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352117","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

Deception in Turret Defense Game: Information Limiting Strategy to Induce Dilemma 炮塔防御博弈中的欺骗：诱导困境的信息限制策略

IEEE open journal of control systems Pub Date : 2025-09-17 DOI: 10.1109/OJCSYS.2025.3611726

Daigo Shishika;Alexander Von Moll;Dipankar Maity;Michael Dorothy

引用次数: 0

On the Representation of Piecewise Quadratic Functions by Neural Networks 用神经网络表示分段二次函数

IEEE open journal of control systems Pub Date : 2025-09-09 DOI: 10.1109/OJCSYS.2025.3607844

Dieter Teichrib;Moritz Schulze Darup

{"title":"On the Representation of Piecewise Quadratic Functions by Neural Networks","authors":"Dieter Teichrib;Moritz Schulze Darup","doi":"10.1109/OJCSYS.2025.3607844","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3607844","url":null,"abstract":"Neural networks (NNs) are commonly used to approximate functions based on data samples, as they are a universal function approximator for a large class of functions. However, choosing a suitable topology in terms of depth, width and activation function for NNs that allow for low error approximations is a non-trivial task. For the approximation of continuous piecewise affine (PWA) functions, this task has been solved by showing that for every PWA function, there exist NNs with rectified linear unit (relu) and maxout activation that allow an exact representation of the PWA function. This connection between PWA functions and NNs has led to some valuable insights into the representation capabilities of NNs. Moreover, the connection was used in control for approximating the PWA optimal control law of model predictive control (MPC) for linear systems. We show that a similar connection exists between NNs and continuous piecewise quadratic (PWQ) functions by deriving topologies for NNs that allow an exact representation of arbitrary PWQ functions with a polyhedral domain partition. Furthermore, we demonstrate that the proposed NNs can efficiently approximate the PWQ optimal value function for linear MPC.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"447-462"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153811","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352169","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

Leveraging Analytic Gradients in Provably Safe Reinforcement Learning 在可证明安全的强化学习中利用分析梯度

IEEE open journal of control systems Pub Date : 2025-09-09 DOI: 10.1109/OJCSYS.2025.3607845

Tim Walter;Hannah Markgraf;Jonathan Külz;Matthias Althoff

{"title":"Leveraging Analytic Gradients in Provably Safe Reinforcement Learning","authors":"Tim Walter;Hannah Markgraf;Jonathan Külz;Matthias Althoff","doi":"10.1109/OJCSYS.2025.3607845","DOIUrl":"https://doi.org/10.1109/OJCSYS.2025.3607845","url":null,"abstract":"The deployment of autonomous robots in safety-critical applications requires safety guarantees. Provably safe reinforcement learning is an active field of research that aims to provide such guarantees using safeguards. These safeguards should be integrated during training to reduce the sim-to-real gap. While there are several approaches for safeguarding sampling-based reinforcement learning, analytic gradient-based reinforcement learning often achieves superior performance from fewer environment interactions. However, there is no safeguarding approach for this learning paradigm yet. Our work addresses this gap by developing the first effective safeguard for analytic gradient-based reinforcement learning. We analyse existing, differentiable safeguards, adapt them through modified mappings and gradient formulations, and integrate them into a state-of-the-art learning algorithm and a differentiable simulation. Using numerical experiments on three control tasks, we evaluate how different safeguards affect learning. The results demonstrate safeguarded training without compromising performance. Additional visuals are provided at timwalter.github.io/safe-agb-rl.github.io.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"463-481"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351918","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

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

引用次数: 0