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Energy-Aware Consensus Control for Multi-Agent Systems With Guaranteed Battery Safety via H-Infinity LMI Design 基于H-Infinity LMI设计的保证电池安全的多智能体系统能量感知共识控制
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-08-21 DOI: 10.1109/LCSYS.2026.3726108
Sounghwan Hwang;Guanlin Wu;Minhyun Cho;Inseok Hwang
{"title":"Energy-Aware Consensus Control for Multi-Agent Systems With Guaranteed Battery Safety via H-Infinity LMI Design","authors":"Sounghwan Hwang;Guanlin Wu;Minhyun Cho;Inseok Hwang","doi":"10.1109/LCSYS.2026.3726108","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3726108","url":null,"abstract":"In this letter, we propose an energy-aware consensus control framework for multi-agent systems (MASs) with guaranteed battery safety through linear matrix inequalities (LMIs). Since battery-powered agents such as unmanned aerial vehicles (UAVs) have limited energy, conventional consensus strategies may induce aggressive control inputs, risking over-discharging and operational failures. To address this issue, we introduce state-of-charge (SOC)-dependent interaction weights that reduce the control burden on energy-depleted agents, resulting in a SOC-dependent Laplacian matrix. An observer-based control framework is then developed, where the observer and controller gains are synthesized using <inline-formula> <tex-math>$H_{infty } $ </tex-math></inline-formula> and polytopic LMI formulations, respectively. Based on these gains, sufficient conditions for battery safety are established, providing a computable safe operation horizon. The proposed framework is validated via a MAS formation control example.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2293-2298"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On the Design of Sparse Hybrid Control for Switched Linear Systems 切换线性系统的稀疏混合控制设计
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-09-02 DOI: 10.1109/LCSYS.2026.3730145
Darsana U;Atreyee Kundu
{"title":"On the Design of Sparse Hybrid Control for Switched Linear Systems","authors":"Darsana U;Atreyee Kundu","doi":"10.1109/LCSYS.2026.3730145","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3730145","url":null,"abstract":"We present a new algorithm for the design of maximum hands-off hybrid control sequences for discrete-time switched linear systems that satisfy pre-specified restrictions on the admissible actions and steer a given initial state of the switched system to the origin of the state-space in a pre-specified duration of time. The key apparatuses for our analysis are state-space abstraction and graph theory. A numerical example is presented to demonstrate our results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2335-2340"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Control Laguerre Tessellation: Semi-Discrete Optimal Transport Over Control Systems 控制拉盖尔镶嵌：控制系统上的半离散最优传输
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-10-08 DOI: 10.1109/LCSYS.2026.3741511
Ripon C. Sarker;Abhishek Halder
{"title":"Control Laguerre Tessellation: Semi-Discrete Optimal Transport Over Control Systems","authors":"Ripon C. Sarker;Abhishek Halder","doi":"10.1109/LCSYS.2026.3741511","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3741511","url":null,"abstract":"We study the optimal transport of optimally controlled agents from a compactly supported absolutely continuous source to a discrete target measure. The ground cost for the transport is induced by the optimal cost of the agents’ motion. When this ground cost satisfies the twist condition, the optimal transport map is given almost everywhere in terms of a Laguerre tessellation of the state space. We refer to this control-theoretic generalization of Laguerre tessellation as Control Laguerre Tessellation (CLT), and illustrate it for two ground costs induced by linear controlled agents with minimum energy and minimum time objectives.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2509-2514"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149249528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Federated Behavioral Identification via Grassmann Manifold Aggregation 基于格拉斯曼流形聚合的联邦行为识别
IF 2
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-01 DOI: 10.1109/LCSYS.2026.3698430
Shiang Cao;YangQuan Chen
{"title":"Federated Behavioral Identification via Grassmann Manifold Aggregation","authors":"Shiang Cao;YangQuan Chen","doi":"10.1109/LCSYS.2026.3698430","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3698430","url":null,"abstract":"This letter studies the identification of linear systems using behaviors generated by multiple similar systems. Instead of estimating a parametric model, we adopt a framework rooted in behavioral systems theory, where restricted behaviors correspond to fixed-dimensional linear subspaces represented as points on the Grassmann manifold. Motivated by federated learning, we develop a manifold-based aggregation mechanism that combines information from multiple systems while preserving data privacy. The method aggregates locally identified behavior subspaces via geometric operations on the Grassmann manifold, enabling collaborative system identification without sharing raw data. We show that, under mild assumptions, the aggregated identification is stable in the sense that its error is bounded by a constant multiple of the local identification error. Under stronger conditions, we further establish that the identification accuracy improves as the number of participating systems increases. Numerical simulations demonstrate that the proposed approach accurately reconstructs the system dynamics using data from multiple similar systems.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"517-522"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Meta-Federated Learning for Robust Adaptive Control in Non-Stationary Environments: A Lyapunov-Guided Approach 非平稳环境下鲁棒自适应控制的元联邦学习：lyapunov引导方法
IF 2
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1109/LCSYS.2026.3676121
Vaibhav Sharma;Kamal Raj Pardasani
{"title":"Meta-Federated Learning for Robust Adaptive Control in Non-Stationary Environments: A Lyapunov-Guided Approach","authors":"Vaibhav Sharma;Kamal Raj Pardasani","doi":"10.1109/LCSYS.2026.3676121","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3676121","url":null,"abstract":"Traditional Federated Learning approaches often fail in control settings due to the instability of the average and lack of rigorous safety guarantees. This letter introduces Lyapunov-Guided Meta-Federated Optimization (LGM-FO), a framework synergizing Model-Agnostic Meta-Learning with Lyapunov control theory to address heterogeneity and non-stationarity in Cyber-Physical Systems. Our framework formulates the problem as meta-learning a robust control initialization integrated with a neural Lyapunov certificate to facilitate rapid adaptation while maintaining verifiable stability guarantees. To mitigate communication overhead, we employ an event-triggered aggregation protocol. Theoretically, we provide convergence proofs for the bilevel optimization, derive dynamic regret bounds of <inline-formula> <tex-math>$mathcal {O}(sqrt {T})$ </tex-math></inline-formula> for time-varying environments and establish asymptotic stability under asynchronous delays using the Razumikhin theorem. The framework demonstrates superior robustness and efficiency compared to standard federated baselines.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"175-180"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Priority-Ordered Swapping Algorithm for Submodular Maximization Problems 子模最大化问题的一种优先顺序交换算法
IF 2
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-03-05 DOI: 10.1109/LCSYS.2026.3670932
Xianlun Peng;Xiangyu Meng;Fangfei Li
{"title":"A Priority-Ordered Swapping Algorithm for Submodular Maximization Problems","authors":"Xianlun Peng;Xiangyu Meng;Fangfei Li","doi":"10.1109/LCSYS.2026.3670932","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3670932","url":null,"abstract":"In this letter, we delve into the monotone submodular maximization problem with a cardinality constraint. We propose a priority-ordered swapping algorithm that iteratively improves the solution by swapping the lowest priority elements in the current solution set with elements from the ground set. This algorithm can effectively complement the greedy approach, especially when starting from the greedy solution. Moreover, we quantify the performance gap between our algorithm’s solution and the optimal solution, providing a theoretical guarantee for its effectiveness. We also identify conditions under which our algorithm outperforms standard greedy approaches in terms of approximation quality. In addition, the effectiveness of the proposed algorithm is further demonstrated by applying it to the multi-agent coverage problem.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"133-138"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PFC-Based Output-Feedback Adaptive Control for Heterogeneous Nonlinear Multi-Agent Systems 基于pfc的异构非线性多智能体系统输出反馈自适应控制
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-07-10 DOI: 10.1109/LCSYS.2026.3712002
Takao Sato;Ryuto Kobayashi
{"title":"PFC-Based Output-Feedback Adaptive Control for Heterogeneous Nonlinear Multi-Agent Systems","authors":"Takao Sato;Ryuto Kobayashi","doi":"10.1109/LCSYS.2026.3712002","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3712002","url":null,"abstract":"This letter proposes an output-feedback adaptive control scheme for heterogeneous nonlinear multi-agent systems (MAS) based on the output feedback strictly passive (OFSP) property. For a network consisting of a leader with a known trajectory and followers with unknown dynamics, a parallel feedforward compensator (PFC) is introduced to each follower to ensure the OFSP property of the augmented system. A distributed adaptive control law is then constructed based on the relative output errors among agents. By using mathematical induction, it is theoretically proven that all signals in the closed-loop system remain bounded and the tracking errors of the augmented outputs are uniformly bounded. Numerical simulations involving agents with diverse system orders and nonlinearities validate the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"1849-1854"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Duality-Based Optimization Formulation of Safe Control Design With State Uncertainties 一种状态不确定安全控制设计的二元优化公式
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-24 DOI: 10.1109/LCSYS.2026.3706916
Xiao Tan;Rahal Nanayakkara;Paulo Tabuada;Aaron D. Ames
{"title":"A Duality-Based Optimization Formulation of Safe Control Design With State Uncertainties","authors":"Xiao Tan;Rahal Nanayakkara;Paulo Tabuada;Aaron D. Ames","doi":"10.1109/LCSYS.2026.3706916","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3706916","url":null,"abstract":"State estimation uncertainty is prevalent in real-world applications, hindering the application of safety-critical control. Existing methods address this by strengthening a Control Barrier Function (CBF) condition either to handle actuation errors induced by state uncertainty, or to enforce stricter, more conservative sufficient conditions. In this work, we take a more direct approach and formulate a robust safety filter by analyzing the image of the set of all possible states under the CBF dynamics. We first prove that convexifying this image set does not change the set of possible inputs. Then, by leveraging duality, we propose an equivalent and tractable reformulation for cases where this convex hull can be expressed as a polytope or ellipsoid. Simulation results show the approach in this letter to be less conservative than existing alternatives.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"1837-1842"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Performance Analysis of Linear Parameter-Varying Systems Using Data-Driven Integral Quadratic Constraints 基于数据驱动积分二次约束的线性变参数系统鲁棒性能分析
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-30 DOI: 10.1109/LCSYS.2026.3708566
Emily Burgin;Peter Seiler;Harald Pfifer
{"title":"Robust Performance Analysis of Linear Parameter-Varying Systems Using Data-Driven Integral Quadratic Constraints","authors":"Emily Burgin;Peter Seiler;Harald Pfifer","doi":"10.1109/LCSYS.2026.3708566","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3708566","url":null,"abstract":"Data-driven approaches for assessing robustness to uncertainty are gaining popularity as systems become more complex and harder to model accurately. This letter proposes a robustness analysis method using the integral quadratic constraint (IQC) framework. It is developed for linear parameter-varying (LPV) systems using time-domain IQCs. These time-domain IQCs are formulated directly using measured input-output data from the uncertainty. With one optimisation in the form of a semi-definite program (SDP), the optimal performance with respect to measured input-output behaviour of the uncertainty is determined, as well as the corresponding optimal IQC multiplier.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"1531-1536"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Balancing Robotic Search and Survival: A Game-Theoretic Framework for Ergodic Search in Contested Domains 平衡机器人搜索和生存：竞争领域遍历搜索的博弈论框架
IF 1.9
IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/LCSYS.2026.3703796
Darwin Mick;Howie Choset;Bhaskar Vundurthy
{"title":"Balancing Robotic Search and Survival: A Game-Theoretic Framework for Ergodic Search in Contested Domains","authors":"Darwin Mick;Howie Choset;Bhaskar Vundurthy","doi":"10.1109/LCSYS.2026.3703796","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3703796","url":null,"abstract":"Autonomous search in contested environments presents a fundamental conflict: agents must find targets as quickly as possible while minimizing exposure to intelligent adversaries. Existing coverage algorithms, while efficient, generate trajectories that are easily predicted and exploited by hostile actors. Conversely, standard pursuit-evasion games typically optimize for capture or escape, neglecting the agents’ need to persist in the environment to search. This paper presents a unified game-theoretic framework for multi-agent ergodic search in adversarial environments. We formulate the problem as a general-sum differential game where agents minimize a composite cost of spectral ergodic error and a penalty on adversary proximity, while adversaries minimize distance to the nearest agent. We compute local open-loop Nash equilibria strategies within a receding horizon control framework, synthesizing reactive behaviors that balance long-term coverage with immediate survival. Extensive numerical experiments reveal two key strategic insights: 1) communication topologies determine efficiency of player behaviors, and 2) in resource-constrained scenarios, adversaries achieve higher disruption by delaying capture rather than capturing immediately, fundamentally altering the optimal defensive policy.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"829-834"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148484739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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