IEEE Control Systems Letters最新文献_第4页

Polytopic and Interval Observers for Uncertain Linear Time-Varying Systems via Non-Square Transformation 不确定线性时变系统的非平方变换多面体和区间观测器

IF 1.9

IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-24 DOI: 10.1109/LCSYS.2026.3706914

Tarun Pati;Sze Zheng Yong

引用次数: 0

Performance Analysis of an Anomaly Detection Method Based on Distributed Spatial Filtering 一种基于分布式空间滤波的异常检测方法性能分析

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IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1109/LCSYS.2026.3712361

Shinsaku Izumi;Kazuhiro Watanabe

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ASIA: An Autonomous System Identification Agent 亚洲：自主系统识别代理

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IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-07-27 DOI: 10.1109/LCSYS.2026.3717047

Dario Piga;Marco Forgione

{"title":"ASIA: An Autonomous System Identification Agent","authors":"Dario Piga;Marco Forgione","doi":"10.1109/LCSYS.2026.3717047","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3717047","url":null,"abstract":"Over the years, research in system identification has provided a rich set of methods for learning dynamical models, together with well-established theoretical guarantees. In practice, however, the choice of model class, training algorithm, and hyperparameter tuning is still largely left to empirical trial-and-error, requiring substantial expert time and domain experience. Motivated by recent advances in agentic artificial intelligence, we present ASIA, a framework that delegates this iterative search to a large language model acting as an autonomous coding agent. Building on existing agentic platforms, ASIA closes the loop between hypothesis, implementation, and evaluation without human intervention, requiring only a plain-English description of the identification problem. We conduct an empirical study of ASIA on two system identification benchmarks and analyse the agent’s search behaviour, the architectures and training strategies it discovers, and the quality of the resulting models. We also discuss the potential of the approach and its current limitations, including implicit test leakage, reduced methodological transparency, reproducibility concerns, and sensitivity to the prompt.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"2251-2256"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836319","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 Stackelberg Framework for Product Upgrade Strategies With Forward-Looking Consumers 前瞻性消费者产品升级策略的Stackelberg框架

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IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-25 DOI: 10.1109/LCSYS.2026.3707175

Mingda Yue;Yuhu Wu;Xun Shen;Junsong Wang

引用次数: 0

Distributed Adaptive Control for Constrained Nash Equilibrium Seeking With Delays 时滞约束纳什均衡寻优的分布式自适应控制

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IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-11 DOI: 10.1109/LCSYS.2026.3702912

Dimitra Kalyva;Haris E. Psillakis

引用次数: 0

On Type Deception in Linear-Quadratic Differential Games 线性二次微分对策中的类型欺骗问题

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IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-17 DOI: 10.1109/LCSYS.2026.3704518

Jesse Milzman;Dipankar Maity

引用次数: 0

GUARDIAN: Safety Filtering for Systems With Perception Models Under Adversarial Attack 具有感知模型的系统在对抗性攻击下的安全过滤

IF 1.9

IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/LCSYS.2026.3694504

Nicholas Rober;Alex Rose;Jonathan P. How

{"title":"GUARDIAN: Safety Filtering for Systems With Perception Models Under Adversarial Attack","authors":"Nicholas Rober;Alex Rose;Jonathan P. How","doi":"10.1109/LCSYS.2026.3694504","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3694504","url":null,"abstract":"Safety filtering is an effective method for enforcing constraints in safety-critical systems, but existing methods typically assume perfect state information. This limitation is especially problematic for systems that rely on neural network (NN)-based state estimators, which can be highly sensitive to noise and adversarial input perturbations. We address these problems by introducing GUARDIAN: Guaranteed Uncertainty-Aware Reachability Defense against Adversarial INterference, a safety filtering framework that provides formal safety guarantees for systems with NN-based state estimators. At runtime, GUARDIAN uses neural network verification tools to provide guaranteed bounds on the system’s state estimate given possible perturbations to its observation. It then uses a modified Hamilton-Jacobi reachability formulation to construct a safety filter that adjusts the nominal control input based on the verified state bounds and safety constraints. The result is an uncertainty-aware filter that ensures safety despite the use of an NN estimator with noisy, possibly adversarial, observations. Theoretical and numerical results show how GUARDIAN effectively defends systems against adversarial attacks that would otherwise cause violation of safety constraints.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"385-390"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485600","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

Bio-Inspired Event-Based Visual Servoing for Ground Robots 基于仿生事件的地面机器人视觉伺服

IF 1.9

IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-06-15 DOI: 10.1109/LCSYS.2026.3703887

Maral Mordad;Kian Behzad;Debojyoti Biswas;Noah J. Cowan;Milad Siami

{"title":"Bio-Inspired Event-Based Visual Servoing for Ground Robots","authors":"Maral Mordad;Kian Behzad;Debojyoti Biswas;Noah J. Cowan;Milad Siami","doi":"10.1109/LCSYS.2026.3703887","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3703887","url":null,"abstract":"Biological sensory systems are inherently adaptive, filtering out constant stimuli and prioritizing relative changes, likely enhancing computational and metabolic efficiency. Inspired by active sensing behaviors across a wide range of animals, this paper introduces a principled 1D event-based visual servoing framework for ground robots operating in structured environments. Utilizing a Dynamic Vision Sensor (DVS), we demonstrate that by applying a fixed spatial kernel to the asynchronous event stream generated from structured logarithmic intensity-change patterns, the resulting net event flux analytically isolates specific combinations of kinematic states. We establish a generalized theoretical bound for this event rate estimator and show that linear and quadratic spatial profiles isolate the robot’s velocity and position-velocity product, respectively. Leveraging these properties, we employ a multi-pattern stimulus to directly synthesize a nonlinear state feedback term entirely without traditional state estimation. To overcome the inescapable loss of linear observability at equilibrium inherent in event sensing, we propose a bio-inspired active sensing limit-cycle controller. Experimental validation on a 1/10-scale autonomous ground vehicle confirms the efficacy, extreme low-latency, and computational efficiency of the proposed direct-sensing approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"889-894"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485672","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

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton–Jacobi–Bellman Equations Lyapunov和Hamilton-Jacobi-Bellman方程物理信息神经网络解的可验证误差界

IF 1.9

IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-03-25 DOI: 10.1109/LCSYS.2026.3696322

Jun Liu

{"title":"Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton–Jacobi–Bellman Equations","authors":"Jun Liu","doi":"10.1109/LCSYS.2026.3696322","DOIUrl":"https://doi.org/10.1109/LCSYS.2026.3696322","url":null,"abstract":"Many core problems in nonlinear systems analysis and control can be recast as solving partial differential equations (PDEs) such as Lyapunov and Hamilton–Jacobi–Bellman (HJB) equations. Physics-informed neural networks (PINNs) have emerged as a promising mesh-free approach for approximating their solutions, but in most existing works there is no rigorous guarantee that a small PDE residual implies a small solution error. This letter develops verifiable error bounds for approximate solutions of Lyapunov and HJB equations, with particular emphasis on PINN-based approximations. For both the Lyapunov and HJB PDEs, we show that a verifiable residual bound yields relative error bounds with respect to the true solutions as well as computable <italic>a posteriori</i> estimates in terms of the approximate solutions. For the HJB equation, this also yields certified upper and lower bounds on the optimal value function on compact sublevel sets and quantifies the optimality gap of the induced feedback policy. We further show that one-sided residual bounds already imply that the approximation is a valid Lyapunov or control Lyapunov function. We illustrate the results with numerical examples.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"10 ","pages":"403-408"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485775","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

Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation Under Uncertainty 不确定条件下多目标轨迹评估的风险意识规则

IF 1.9

IEEE Control Systems Letters Pub Date : 2026-01-01 Epub Date: 2026-03-25 DOI: 10.1109/LCSYS.2026.3696351

Tichakorn Wongpiromsarn

引用次数: 0