Pub Date : 2025-09-09DOI: 10.1109/OJCSYS.2025.3607844
Dieter Teichrib;Moritz Schulze Darup
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.
{"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}
Pub Date : 2025-09-09DOI: 10.1109/OJCSYS.2025.3607845
Tim Walter;Hannah Markgraf;Jonathan Külz;Matthias Althoff
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.
{"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}
Pub Date : 2025-09-01DOI: 10.1109/OJCSYS.2025.3604640
Dejan Milutinović;Alexander Von Moll;Satyanarayana Gupta Manyam;David W. Casbeer;Isaac E. Weintraub;Meir Pachter
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.
{"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}
Pub Date : 2025-08-22DOI: 10.1109/OJCSYS.2025.3601836
Moritz Wenzel;Edoardo De Din;Marcel Zimmer;Andrea Benigni
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.
{"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}
Pub Date : 2025-08-21DOI: 10.1109/OJCSYS.2025.3601435
Caio Fabio Oliveira da Silva;Azita Dabiri;Bart De Schutter
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 $20times$) while maintaining high feasibility and average optimality gap lower than 1.1% .
{"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}
Pub Date : 2025-08-20DOI: 10.1109/OJCSYS.2025.3600925
Xue-Fang Wang;Jingjing Jiang;Wen-Hua Chen
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.
{"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}
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.
{"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}
Pub Date : 2025-08-14DOI: 10.1109/OJCSYS.2025.3599473
Eloy Garcia;David W. Casbeer
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.
本文分析了经典的夺旗游戏，将其建模为攻击者和防御者之间的冲突。游戏以不同的阶段展开，目标不断变化：首先，攻击者试图夺取一面旗帜，而防守者试图拦截；其次，如果成功，攻击者尝试到达安全区域，而防御者再次寻求拦截。我们从数学上推导出每个阶段参与者的最佳状态反馈策略和相关的价值函数，严格证明了它们的正确性。一个关键的贡献是引入过渡阶段，在这个阶段，我们分析了当夺旗不可避免时防守者的最佳重新定位策略，为游戏的第二阶段做准备。这种新颖的过渡连接了游戏的各个阶段，使我们能够解决整个“同类游戏”（game of Kind）——在任何起始条件下决定获胜者——并定义攻击者既能夺取旗帜又能成功逃到安全区的精确环境。
{"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}
Pub Date : 2025-08-13DOI: 10.1109/OJCSYS.2025.3598673
Mia Scoblic;Camilla Tabasso;Venanzio Cichella;Isaac Kaminer
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.
{"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}
Pub Date : 2025-08-13DOI: 10.1109/OJCSYS.2025.3598626
Christopher I. Calle;Shaunak D. Bopardikar
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.
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