IEEE open journal of control systems最新文献

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

Resilient Multi-Agent Systems Against Denial of Service Attacks via Adaptive and Activatable Network Layers 通过自适应和可激活网络层抵御拒绝服务攻击的弹性多代理系统

IEEE open journal of control systems Pub Date : 2024-10-09 DOI: 10.1109/OJCSYS.2024.3477091

Deepalakshmi Babu Venkateswaran;Zhihua Qu

引用次数: 0

Resiliency Through Collaboration in Heterogeneous Multi-Robot Systems 通过异构多机器人系统中的协作提高复原力

IEEE open journal of control systems Pub Date : 2024-09-25 DOI: 10.1109/OJCSYS.2024.3467991

Alexander A. Nguyen;Faryar Jabbari;Magnus Egerstedt

{"title":"Resiliency Through Collaboration in Heterogeneous Multi-Robot Systems","authors":"Alexander A. Nguyen;Faryar Jabbari;Magnus Egerstedt","doi":"10.1109/OJCSYS.2024.3467991","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3467991","url":null,"abstract":"This paper examines pairwise collaborations in heterogeneous multi-robot systems. In particular, we focus on how individual robots, with different functionalities and dynamics, can enhance their resilience by forming collaborative arrangements that result in new capabilities. Control barrier functions are utilized as a mechanism to encode the safe operating regions of individual robots, with the idea being that a robot may be able to operate in new regions that it could not traverse alone by working with other robots. We explore answers to three questions: “Why should robots collaborate?”, “When should robots collaborate?”, and “How can robots collaborate?” To that end, we introduce the safely reachable set – capturing the regions that individual robots can reach safely, either with or without help, while considering their initial states and dynamics. We then describe the conditions under which a help-providing robot and a help-receiving robot can engage in collaboration. Next, we describe the pairwise collaboration framework, modeled through hybrid automata, to show how collaborations can be structured within a heterogeneous multi-robot team. Finally, we present case studies that are conducted on a team of mobile robots.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"461-471"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595023","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

Resilient Synchronization of Pulse-Coupled Oscillators Under Stealthy Attacks 隐形攻击下脉冲耦合振荡器的弹性同步

IEEE open journal of control systems Pub Date : 2024-09-10 DOI: 10.1109/OJCSYS.2024.3458593

Yugo Iori;Hideaki Ishii

{"title":"Resilient Synchronization of Pulse-Coupled Oscillators Under Stealthy Attacks","authors":"Yugo Iori;Hideaki Ishii","doi":"10.1109/OJCSYS.2024.3458593","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3458593","url":null,"abstract":"This paper studies a clock synchronization problem for wireless sensor networks employing pulse-based communication when some of the nodes are faulty or even adversarial. The objective is to design resilient distributed algorithms for the nonfaulty nodes to keep the influence of the malicious nodes minimal and to arrive at synchronization in a safe manner. Compared with conventional approaches, our algorithms are more capable in the sense that they are applicable to networks taking noncomplete graph structures. Our approach is to extend the class of mean subsequence reduced (MSR) algorithms from the area of multi-agent consensus. First, we provide a simple detection method to find malicious nodes that transmit pulses irregularly. Then, we demonstrate that in the presence of adversaries avoiding to be detected, the normal nodes can reach synchronization by ignoring suspicious pulses. Two extensions of this algorithm are further presented, which can operate under more adversarial attacks and also with relaxed conditions on the initial phases. We illustrate the effectiveness of our results by numerical examples.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"429-444"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430771","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

Pareto-Optimal Event-Based Scheme for Station and Inter-Station Control of Electric and Automated Buses 基于帕累托最优事件的电动和自动公交车车站和站间控制方案

IEEE open journal of control systems Pub Date : 2024-09-09 DOI: 10.1109/OJCSYS.2024.3456633

Cecilia Pasquale;Simona Sacone;Silvia Siri;Antonella Ferrara

{"title":"Pareto-Optimal Event-Based Scheme for Station and Inter-Station Control of Electric and Automated Buses","authors":"Cecilia Pasquale;Simona Sacone;Silvia Siri;Antonella Ferrara","doi":"10.1109/OJCSYS.2024.3456633","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3456633","url":null,"abstract":"This paper considers electric and automated buses required to follow a given line and respect a given timetable in an inter-city road. The main goal of this work is to design a control scheme in order to optimally decide, in real time, the speed profile of the bus along the line, as well as the dwell and charging times at stops. This must be done by accounting for the traffic conditions encountered in the road and by jointly minimizing the deviations from the timetable and the lack of energy in the bus battery compared with a desired level. For the resulting multi-objective optimal control problem a Pareto front analysis is performed in the paper, also considering a real test case. Relying on the analysis outcomes, an event-based control scheme is proposed, which allows, every time a bus reaches a stop, to find the most suitable Pareto-optimal solution depending on a set of state and scenario conditions referred to the expected departure time at stops, the predicted traffic conditions in the road and the state of charge of the bus battery. The performance of the proposed control scheme is tested on a real case study, thoroughly discussed in the paper.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"445-460"},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447138","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

A Control-Theoretical Zero-Knowledge Proof Scheme for Networked Control Systems 网络控制系统的控制理论零知识证明方案

IEEE open journal of control systems Pub Date : 2024-09-06 DOI: 10.1109/OJCSYS.2024.3455899

Camilla Fioravanti;Christoforos N. Hadjicostis;Gabriele Oliva

{"title":"A Control-Theoretical Zero-Knowledge Proof Scheme for Networked Control Systems","authors":"Camilla Fioravanti;Christoforos N. Hadjicostis;Gabriele Oliva","doi":"10.1109/OJCSYS.2024.3455899","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3455899","url":null,"abstract":"Networked Control Systems (NCS) are pivotal for sectors like industrial automation, autonomous vehicles, and smart grids. However, merging communication networks with control loops brings complexities and security vulnerabilities, necessitating strong protection and authentication measures. This paper introduces an innovative Zero-Knowledge Proof (ZKP) scheme tailored for NCSs, enabling a networked controller to prove its knowledge of the dynamical model and its ability to control a discrete-time linear time-invariant (LTI) system to a sensor, without revealing the model. This verification is done through the controller's capacity to produce suitable control signals in response to the sensor's output demands. The completeness, soundness, and zero-knowledge properties of the proposed approach are demonstrated. The scheme is subsequently extended by considering the presence of delays and output noise. Additionally, a dual scenario where the sensor proves its model knowledge to the controller is explored, enhancing the method's versatility. Effectiveness is shown through numerical simulations and a case study on distributed agreement in multi-agent systems.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"416-428"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430752","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

Control of Linear-Threshold Brain Networks via Reservoir Computing 通过水库计算控制线性阈值大脑网络

IEEE open journal of control systems Pub Date : 2024-08-29 DOI: 10.1109/OJCSYS.2024.3451889

Michael McCreesh;Jorge Cortés

引用次数: 0

Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey 强化学习中的汉密尔顿-雅各比可达性：调查

IEEE open journal of control systems Pub Date : 2024-08-23 DOI: 10.1109/OJCSYS.2024.3449138

Milan Ganai;Sicun Gao;Sylvia L. Herbert

{"title":"Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey","authors":"Milan Ganai;Sicun Gao;Sylvia L. Herbert","doi":"10.1109/OJCSYS.2024.3449138","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3449138","url":null,"abstract":"Recent literature has proposed approaches that learn control policies with high performance while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has become an effective tool for verifying safety and supervising the training of reinforcement learning-based control policies for complex, high-dimensional systems. Previously, HJ reachability was restricted to verifying low-dimensional dynamical systems primarily because the computational complexity of the dynamic programming approach it relied on grows exponentially with the number of system states. In recent years, a litany of proposed methods addresses this limitation by computing the reachability value function simultaneously with learning control policies to scale HJ reachability analysis while still maintaining a reliable estimate of the true reachable set. These HJ reachability approximations are used to improve the safety, and even reward performance, of learned control policies and can solve challenging tasks such as those with dynamic obstacles and/or with lidar-based or vision-based observations. In this survey paper, we review the recent developments in the field of HJ reachability estimation in reinforcement learning that would provide a foundational basis for further research into reliability in high-dimensional systems.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"310-324"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10645063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246525","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

Stable Inverse Reinforcement Learning: Policies From Control Lyapunov Landscapes 稳定的逆强化学习：来自控制 Lyapunov 景观的策略

IEEE open journal of control systems Pub Date : 2024-08-21 DOI: 10.1109/OJCSYS.2024.3447464

SAMUEL TESFAZGI;Leonhard Sprandl;Armin Lederer;Sandra Hirche

{"title":"Stable Inverse Reinforcement Learning: Policies From Control Lyapunov Landscapes","authors":"SAMUEL TESFAZGI;Leonhard Sprandl;Armin Lederer;Sandra Hirche","doi":"10.1109/OJCSYS.2024.3447464","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3447464","url":null,"abstract":"Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborative control settings. A common method to solve this problem is inverse reinforcement learning (IRL), where the observed agent, e.g., a human demonstrator, is assumed to behave according to the optimization of an intrinsic cost function that reflects its intent and informs its control actions. While the framework is expressive, the inferred control policies generally lack convergence guarantees, which are critical for safe deployment in real-world settings. We therefore propose a novel, stability-certified IRL approach by reformulating the cost function inference problem to learning control Lyapunov functions (CLF) from demonstrations data. By additionally exploiting closed-form expressions for associated control policies, we are able to efficiently search the space of CLFs by observing the attractor landscape of the induced dynamics. For the construction of the inverse optimal CLFs, we use a Sum of Squares and formulate a convex optimization problem. We present a theoretical analysis of the optimality properties provided by the CLF and evaluate our approach using both simulated and real-world, human-generated data.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"358-374"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316493","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

Learning to Boost the Performance of Stable Nonlinear Systems 通过学习提升稳定非线性系统的性能

IEEE open journal of control systems Pub Date : 2024-08-12 DOI: 10.1109/OJCSYS.2024.3441768

Luca Furieri;Clara Lucía Galimberti;Giancarlo Ferrari-Trecate

{"title":"Learning to Boost the Performance of Stable Nonlinear Systems","authors":"Luca Furieri;Clara Lucía Galimberti;Giancarlo Ferrari-Trecate","doi":"10.1109/OJCSYS.2024.3441768","DOIUrl":"https://doi.org/10.1109/OJCSYS.2024.3441768","url":null,"abstract":"The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. However, maintaining closed-loop stability while boosting the performance of nonlinear control systems using data-driven and deep-learning approaches stands as an important unsolved challenge. In this paper, we tackle the performance-boosting problem with closed-loop stability guarantees. Specifically, we establish a synergy between the Internal Model Control (IMC) principle for nonlinear systems and state-of-the-art unconstrained optimization approaches for learning stable dynamics. Our methods enable learning over specific classes of deep neural network performance-boosting controllers for stable nonlinear systems; crucially, we guarantee \u0000<inline-formula><tex-math>$mathcal {L}_{p}$</tex-math></inline-formula>\u0000 closed-loop stability even if optimization is halted prematurely. When the ground-truth dynamics are uncertain, we learn over robustly stabilizing control policies. Our robustness result is tight, in the sense that all stabilizing policies are recovered as the \u0000<inline-formula><tex-math>$mathcal {L}_{p}$</tex-math></inline-formula>\u0000 -gain of the model mismatch operator is reduced to zero. We discuss the implementation details of the proposed control schemes, including distributed ones, along with the corresponding optimization procedures, demonstrating the potential of freely shaping the cost functions through several numerical experiments.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"3 ","pages":"342-357"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316492","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