IEEE Control Systems Letters最新文献_第5页

Finite-Time Parameter Estimation of Separable Nonlinearly Parameterized Regressions With Application 可分离非线性参数化回归的有限时间参数估计及其应用

IF 2

IEEE Control Systems Letters Pub Date : 2025-08-04 DOI: 10.1109/LCSYS.2025.3595194

Renyuan Zheng;Wenrui Shi;Mingzhe Hou;Guangren Duan

引用次数: 0

Perturbation-Controlled Deep Q-Learning With Human-Teaming for Enhancing Adversarial Robustness 基于人类团队的扰动控制深度q学习增强对抗鲁棒性

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-30 DOI: 10.1109/LCSYS.2025.3594257

Sadredin Hokmi;Pegah Moushaee;Mohammad Haeri

引用次数: 0

Sequential QCQP for Bilevel Optimization With Line Search 带线搜索的双层优化的顺序QCQP

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-30 DOI: 10.1109/LCSYS.2025.3594250

Sina Sharifi;Erfan Yazdandoost Hamedani;Mahyar Fazlyab

引用次数: 0

Resilience Quantification and Its Support for Operational Resilience 弹性量化及其对业务弹性的支持

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-29 DOI: 10.1109/LCSYS.2025.3593571

Ion Matei;Maksym Zhenirovskyy

引用次数: 0

Stability Analysis of Piecewise Affine Networked Control Systems Under Aperiodic Sampling 非周期采样条件下分段仿射网络控制系统的稳定性分析

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-28 DOI: 10.1109/LCSYS.2025.3593291

Xiaofei Wang;Guoliang Chen;Zhichuang Wang;Jianwei Xia

引用次数: 0

Fully Distributed Adaptive Practical Fixed-Time Optimal Consensus for Multi-Agent Systems 多智能体系统的全分布自适应实用固定时间最优一致性

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-21 DOI: 10.1109/LCSYS.2025.3589617

Xiasheng Shi;Zhongmei Li

引用次数: 0

Compression Method for Deep Diagonal State Space Model Based on H2 Optimal Reduction 基于H2最优约简的深对角状态空间模型压缩方法

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-21 DOI: 10.1109/LCSYS.2025.3591023

Hiroki Sakamoto;Kazuhiro Sato

引用次数: 0

Inverse Optimal Control With Constraint Relaxation 约束松弛的逆最优控制

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-21 DOI: 10.1109/LCSYS.2025.3590879

Rahel Rickenbach;Amon Lahr;Melanie N. Zeilinger

{"title":"Inverse Optimal Control With Constraint Relaxation","authors":"Rahel Rickenbach;Amon Lahr;Melanie N. Zeilinger","doi":"10.1109/LCSYS.2025.3590879","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3590879","url":null,"abstract":"Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their intentions, by estimating an unknown objective function from one or more corresponding optimal control sequences. When computing estimates from demonstrations in environments with safety-preserving inequality constraints, acknowledging their presence in the chosen IOC method is crucial given their strong influence on the final control strategy. However, solution strategies capable of considering inequality constraints, such as the inverse Karush-Kuhn-Tucker approach, rely on their correct activation and fulfillment; a restrictive assumption when dealing with noisy demonstrations. To overcome this problem, we leverage the concept of exact penalty functions for IOC and show preservation of estimation accuracy. Considering noisy demonstrations, we then illustrate how the usage of penalty functions reduces the number of unknown variables and how their approximations enhance the estimation method’s capacity to account for wrong constraint activations within a polytopic-constrained environment. The proposed method is evaluated for three systems in simulation, outperforming traditional relaxation approaches for noisy demonstrations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2055-2060"},"PeriodicalIF":2.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831778","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 Human-Machine Teaming Through Reinforcement Learning From Failure via Sparse Reward Densification 基于稀疏奖励致密化的失败强化学习鲁棒人机团队

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-21 DOI: 10.1109/LCSYS.2025.3591199

Mingkang Wu;Yongcan Cao

{"title":"Robust Human-Machine Teaming Through Reinforcement Learning From Failure via Sparse Reward Densification","authors":"Mingkang Wu;Yongcan Cao","doi":"10.1109/LCSYS.2025.3591199","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3591199","url":null,"abstract":"Learning control policies in sparse reward environments is a challenging task for many robotic control tasks. The existing studies focus on designing reinforcement learning algorithms that take human inputs in the form of demonstrations such that control policies are learned via uncovering the value of these demonstrations. One typical approach is to learn an inherent reward function that can explain why demonstrations are better than other randomly generated samples. Albeit powerful, the use of human demonstrations is typically costly and difficult to collect, indicating the lack of robustness in these studies. To enhance robustness, we here propose to use failed experiences, namely, failure, due to the easiness of obtaining failure dataset, requiring only common sense rather than domain knowledge needed to generate expert demonstrations. In particular, this letter proposes a new reward densification technique that trains a discriminator to evaluate the similarity between the agent’s current behavior and failure dataset provided by humans. This reward densification technique provides an effective mechanism to obtain state-action values for environments with sparse rewards, via quantifying their (dis)similarity with failure. Additionally, the value of the current behavior, formulated as advantage function, is employed based on the densified reward to refine the control policy’s search direction. We finally conduct several experiments to demonstrate the effectiveness of the proposed approach by comparing with state-of-art methods.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2315-2320"},"PeriodicalIF":2.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255812","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

Distributed Control of Flexible Chained Multiagent Formations 柔性链式多智能体编队的分布式控制

IF 2

IEEE Control Systems Letters Pub Date : 2025-07-18 DOI: 10.1109/LCSYS.2025.3590428

Miguel Aranda;Ignacio Cuiral-Zueco;Gonzalo López-Nicolás

引用次数: 0