IEEE open journal of control systems最新文献_第2页

Generalizing Robust Control Barrier Functions From a Controller Design Perspective 从控制器设计的角度推广鲁棒控制屏障函数

IEEE open journal of control systems Pub Date : 2025-01-13 DOI: 10.1109/OJCSYS.2025.3529364

Anil Alan;Tamas G. Molnar;Aaron D. Ames;Gábor Orosz

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2024 Index IEEE Open Journal of Control Systems Vol. 3 2024索引IEEE控制系统开放杂志卷3

IEEE open journal of control systems Pub Date : 2025-01-10 DOI: 10.1109/OJCSYS.2025.3528596

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IEEE Control Systems Society Publication Information IEEE控制系统协会出版信息

IEEE open journal of control systems Pub Date : 2025-01-07 DOI: 10.1109/OJCSYS.2024.3360366

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IEEE Open Journal of Control Systems Publication Information IEEE控制系统公开杂志出版信息

IEEE open journal of control systems Pub Date : 2025-01-07 DOI: 10.1109/OJCSYS.2024.3360362

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Dynamic Watermarking for Finite Markov Decision Processes 有限马尔可夫决策过程的动态水印

IEEE open journal of control systems Pub Date : 2025-01-06 DOI: 10.1109/OJCSYS.2025.3526003

Jiacheng Tang;Jiguo Song;Abhishek Gupta

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Initial Undershoot in Discrete-Time Input–Output Hammerstein Systems 离散时间输入输出Hammerstein系统的初始欠冲

IEEE open journal of control systems Pub Date : 2025-01-06 DOI: 10.1109/OJCSYS.2025.3525983

Hyung Jun Kim;Mohammadreza Kamaldar;Dennis S. Bernstein

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Concurrent Learning for Cooperative UAV Transportation of Unknown Payloads 未知载荷协同无人机运输的并行学习

IEEE open journal of control systems Pub Date : 2024-12-12 DOI: 10.1109/OJCSYS.2024.3517317

Chi-An Lee;Teng-Hu Cheng

{"title":"Concurrent Learning for Cooperative UAV Transportation of Unknown Payloads","authors":"Chi-An Lee;Teng-Hu Cheng","doi":"10.1109/OJCSYS.2024.3517317","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3517317","url":null,"abstract":"In this work, the transportation problem is addressed by directly attaching the payload to a team of unmanned aerial vehicles (UAVs). The proposed flight controller for cooperative transportation offers a solution by eliminating the need for prior knowledge of payload details, such as the center of gravity (CoG), mass, and moment of inertia (MoI). Typically, the formation for transporting the payload is evenly distributed along the payload boundary. However, this formation can lead to inefficiencies, especially when the CoG of the system is not aligned with the geometric center of the system. In such circumstances, it can result in steady-state error and shorter endurance. The developed controller incorporates a concurrent learning estimator to estimate the mass and CoG simultaneously during flight. This estimation is leveraged to balance power consumption among all UAV agents, resulting in a significant extension of flight time. The system's stability is mathematically proven through the Lyapunov theorem, ensuring a reliable combination of the estimator and adaptive controller. To validate the performance and effectiveness of the proposed approach, simulations and real-world experiments have been conducted, demonstrating the controller's capability to enhance cooperative transportation operations. The results highlight its potential to improve the field of UAV-based payload transportation and provide more efficient and cost-effective transport solutions.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"187-198"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10797683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481797","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

Quantization Effects on Zero-Dynamics Attacks to Closed-Loop Sampled-Data Control Systems 量化对闭环采样数据控制系统零动态攻击的影响

IEEE open journal of control systems Pub Date : 2024-11-28 DOI: 10.1109/OJCSYS.2024.3508396

Xile Kang;Hideaki Ishii

{"title":"Quantization Effects on Zero-Dynamics Attacks to Closed-Loop Sampled-Data Control Systems","authors":"Xile Kang;Hideaki Ishii","doi":"10.1109/OJCSYS.2024.3508396","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3508396","url":null,"abstract":"This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-data systems. Quantization of control inputs adds quantization error to the plant input, resulting in certain variation in the plant output. On the other hand, sampling can introduce non-minimum phase zeros in discretized systems. We consider zero-dynamics attacks, which is a class of false data injection attacks utilizing such unstable zeros. Although non-quantized zero-dynamics attacks are undetectable from the plant output side, quantized attacks may be revealed by larger output variation. Our setting is that the attack signal is applied with the same uniform quantizer used for the control input. We evaluate the attack stealthiness in the closed-loop system setting by quantifying the output variation. Specifically, we characterize the cases for static and dynamic quantization in the attack signal, while keeping the control input statically quantized. Then we demonstrate that the attacker can reduce such output variation with a modified approach, by compensating the quantization error of the attack signal inside the attack dynamics. We provide numerical examples to illustrate the effectiveness of the proposed approaches. We show that observing the quantized control input value by a mirroring model can reveal the zero-dynamics attacks.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"18-29"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993349","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

Exact Recovery for System Identification With More Corrupt Data Than Clean Data 准确恢复系统识别与更多的腐败数据比干净的数据

IEEE open journal of control systems Pub Date : 2024-11-27 DOI: 10.1109/OJCSYS.2024.3507452

Baturalp Yalcin;Haixiang Zhang;Javad Lavaei;Murat Arcak

{"title":"Exact Recovery for System Identification With More Corrupt Data Than Clean Data","authors":"Baturalp Yalcin;Haixiang Zhang;Javad Lavaei;Murat Arcak","doi":"10.1109/OJCSYS.2024.3507452","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3507452","url":null,"abstract":"This paper investigates the system identification problem for linear discrete-time systems under adversaries and analyzes two lasso-type estimators. We examine non-asymptotic properties of these estimators in two separate scenarios, corresponding to deterministic and stochastic models for the attack times. We prove that when the system is stable and attacks are injected periodically, the sample complexity for exact recovery of the system dynamics is linear in terms of the dimension of the states. When adversarial attacks occur at each time instance with probability \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000, the required sample complexity for exact recovery scales polynomially in the dimension of the states and the probability \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000. This result implies almost sure convergence to the true system dynamics under the asymptotic regime. As a by-product, our estimators still learn the system correctly even when more than half of the data is compromised. We emphasize that the attack vectors are allowed to be correlated with each other in this work. This paper provides the first mathematical guarantee in the literature on learning from correlated data for dynamical systems in the case when there is less clean data than corrupt data.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"4 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938399","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

Optimal Control of Endemic Epidemic Diseases With Behavioral Response 通过行为响应优化地方流行病控制

IEEE open journal of control systems Pub Date : 2024-10-30 DOI: 10.1109/OJCSYS.2024.3488567

Francesco Parino;Lorenzo Zino;Alessandro Rizzo

{"title":"Optimal Control of Endemic Epidemic Diseases With Behavioral Response","authors":"Francesco Parino;Lorenzo Zino;Alessandro Rizzo","doi":"10.1109/OJCSYS.2024.3488567","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3488567","url":null,"abstract":"Behavioral factors play a crucial role in the emergence, spread, and containment of human diseases, significantly influencing the effectiveness of intervention measures. However, the integration of such factors into epidemic models is still limited, hindering the possibility of understanding how to optimally design interventions to mitigate epidemic outbreaks in real life. This paper aims to fill in this gap. In particular, we propose a parsimonious model that couples an epidemic compartmental model with a population game that captures the behavioral response, obtaining a nonlinear system of ordinary differential equations. Grounded on prevalence-elastic behavior—the empirically proven assumption that the disease prevalence affects the adherence to self-protective behavior—we consider a nontrivial negative feedback between contagions and adoption of self-protective behavior. We characterize the asymptotic behavior of the system, establishing conditions under which the disease is quickly eradicated or a global convergence to an endemic equilibrium is attained. In addition, we elucidate how the behavioral response affects the endemic equilibrium. Then, we formulate and solve an optimal control problem to plan cost-effective interventions for the model, accounting for their healthcare and social-economical implications. Numerical simulations on a case study calibrated on sexually transmitted diseases demonstrate and validate our findings.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"483-496"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694671","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