Annual Reviews in Control最新文献

{"title":"Survey of distributed algorithms for resource allocation over multi-agent systems","authors":"Mohammadreza Doostmohammadian ,&nbsp;Alireza Aghasi ,&nbsp;Mohammad Pirani ,&nbsp;Ehsan Nekouei ,&nbsp;Houman Zarrabi ,&nbsp;Reza Keypour ,&nbsp;Apostolos I. Rikos ,&nbsp;Karl H. Johansson","doi":"10.1016/j.arcontrol.2024.100983","DOIUrl":"10.1016/j.arcontrol.2024.100983","url":null,"abstract":"<div><div>Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multi-agent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100983"},"PeriodicalIF":7.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning safe control for multi-robot systems: Methods, verification, and open challenges","authors":"Kunal Garg,&nbsp;Songyuan Zhang,&nbsp;Oswin So,&nbsp;Charles Dawson,&nbsp;Chuchu Fan","doi":"10.1016/j.arcontrol.2024.100948","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100948","url":null,"abstract":"<div><p>In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focusing on learning-based methods with safety considerations. We start by reviewing various notions of safety and liveness properties, and modeling frameworks used for problem formulation of MAS. Then we provide a comprehensive review of learning-based methods for safe control design for multi-robot systems. We start with various shielding-based methods, such as safety certificates, predictive filters, and reachability tools. Then, we review the current state of control barrier certificate learning in both a centralized and distributed manner, followed by a comprehensive review of multi-agent reinforcement learning with a particular focus on safety. Next, we discuss the state-of-the-art verification tools for the correctness of learning-based methods. Based on the capabilities and the limitations of the state-of-the-art methods in learning and verification for MAS, we identify various broad themes for open challenges: how to design methods that can achieve good performance along with safety guarantees; how to decompose single-agent-based centralized methods for MAS; how to account for communication-related practical issues; and how to assess transfer of theoretical guarantees to practice.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100948"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive control and reinforcement learning for vehicle suspension control: A review","authors":"Jeremy B. Kimball,&nbsp;Benjamin DeBoer,&nbsp;Kush Bubbar","doi":"10.1016/j.arcontrol.2024.100974","DOIUrl":"10.1016/j.arcontrol.2024.100974","url":null,"abstract":"<div><div>The growing adoption of electric vehicles has drawn a renewed interest in intelligent vehicle subsystems, including active suspension. Control methods for active suspension systems have been a research focus for many years, and with recent advances in machine learning, learning-based active suspension control strategies have emerged. Classically, suspension controllers have been model-based and thus limited by necessarily simplified models of complex suspension dynamics. Learning-based methods address these limitations by leveraging system response measurements to improve the system model or controller itself. Previous surveys have reviewed conventional and preview-based active suspension controllers, but a detailed examination of newer learning-based methods is lacking. This article addresses this gap by presenting the mathematical foundations of these controllers and categorizing existing implementations. The review classifies learning-based suspension control literature into two main categories: adaptive control, which emphasizes stability through online learning, and reinforcement learning, which aims for optimality through extensive system interactions. Within these broader domains, various sub-categories are identified, allowing practitioners and researchers to quickly find relevant work within a specific branch of learning-based suspension control. Furthermore, this article discusses current trends in the field and proposes directions for future investigations. These contributions can serve as a comprehensive guide for the future research and development of learning-based suspension controllers.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100974"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization algorithms as robust feedback controllers","authors":"Adrian Hauswirth,&nbsp;Zhiyu He,&nbsp;Saverio Bolognani,&nbsp;Gabriela Hug,&nbsp;Florian Dörfler","doi":"10.1016/j.arcontrol.2024.100941","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100941","url":null,"abstract":"<div><p>Mathematical optimization is one of the cornerstones of modern engineering research and practice. Yet, throughout all application domains, mathematical optimization is, for the most part, considered to be a numerical discipline. Optimization problems are formulated to be solved numerically with specific algorithms running on microprocessors. An emerging alternative is to view optimization algorithms as dynamical systems. Besides being insightful in itself, this perspective liberates optimization methods from specific numerical and algorithmic aspects and opens up new possibilities to endow complex real-world systems with sophisticated self-optimizing behavior. Towards this goal, it is necessary to understand how numerical optimization algorithms can be converted into feedback controllers to enable robust “closed-loop optimization”. In this article, we focus on recent control designs under the name of “feedback-based optimization” which implement optimization algorithms directly in closed loop with physical systems. In addition to a brief overview of selected continuous-time dynamical systems for optimization, our particular emphasis in this survey lies on closed-loop stability as well as the robust enforcement of physical and operational constraints in closed-loop implementations. To bypass accessing partial model information of physical systems, we further elaborate on fully data-driven and model-free operations. We highlight an emerging application in autonomous reserve dispatch in power systems, where the theory has transitioned to practice by now. We also provide short expository reviews of pioneering applications in communication networks and electricity grids, as well as related research streams, including extremum seeking and pertinent methods from model predictive and process control, to facilitate high-level comparisons with the main topic of this survey.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100941"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1367578824000105/pdfft?md5=d4a670f2ad6b6bb7a73deda712726dae&pid=1-s2.0-S1367578824000105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140163807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control for cognitive systems","authors":"Qing Li ,&nbsp;Arturo Molina Gutiérrez ,&nbsp;Hervé Panetto","doi":"10.1016/j.arcontrol.2024.100955","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100955","url":null,"abstract":"","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100955"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in controller design of pacemakers for pacing control: A comprehensive review","authors":"Rijhi Dey ,&nbsp;Naiwrita Dey ,&nbsp;Rudra Sankar Dhar ,&nbsp;Ujjwal Mondal ,&nbsp;Sudhakar Babu Thanikanti ,&nbsp;Nnamdi Nwulu","doi":"10.1016/j.arcontrol.2023.100930","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2023.100930","url":null,"abstract":"<div><p>This paper provides an extensive literature review focusing on the modeling of artificial pacemakers and the various mechanisms employed for their pacing control. In this survey, we initially gone through the fundamental concept of artificial pacemakers. Subsequently, we expound on their modeling techniques. Additionally, we furnish a holistic overview of diverse control methodologies tailored for the continuous pace tracking and control of pacemaker signals. Our discussion extensively reviews and scrutinizes various control algorithms and deployment approaches. Moreover, we spotlight the application of the IMP-based Repetitive Control (RC) technique for ensuring uninterrupted pace tracking in pacemakers. Conclusively, we address the spectrum of research challenges inherent in controller design advancements, underscoring the journey towards achieving precise and accurate pace control in pacemakers.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100930"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139436213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonparametric adaptive control in native spaces: A DPS framework (Part I)","authors":"Andrew J. Kurdila ,&nbsp;Andrea L’Afflitto ,&nbsp;John A. Burns ,&nbsp;Haoran Wang","doi":"10.1016/j.arcontrol.2024.100969","DOIUrl":"10.1016/j.arcontrol.2024.100969","url":null,"abstract":"<div><div>This two-part work presents a novel theory for model reference adaptive control (MRAC) of deterministic nonlinear ordinary differential equations (ODEs) that contain functional, nonparametric uncertainties that reside in a native space. The approach is unique in that it relies on interpreting the closed-loop control problem for the ODE as a simple type of distributed parameter system (DPS), from which implementable controllers are subsequently derived. A thorough comparative analysis between the proposed framework and classical MRAC is performed. The limiting distributed parameter system, which underlies the proposed adaptive control framework, is derived and discussed in detail in this first part of the paper. The second part of this work will detail numerous finite-dimensional implementations of the proposed native space-based approach.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100969"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Broad-deep network-based fuzzy emotional inference model with personal information for intention understanding in human–robot interaction","authors":"Min Li ,&nbsp;Luefeng Chen ,&nbsp;Min Wu ,&nbsp;Kaoru Hirota ,&nbsp;Witold Pedrycz","doi":"10.1016/j.arcontrol.2024.100951","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100951","url":null,"abstract":"<div><p>A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human–robot interaction. It aims to understand students’ intentions in the university teaching scene. Initially, we employ convolution and maximum pooling for feature extraction. Subsequently, we apply the ridge regression algorithm for emotional behavior recognition, which effectively mitigates the impact of complex network structures and slow network updates often associated with deep learning. Moreover, we utilize multivariate analysis of variance to identify the key personal information factors influencing intentions and calculate their influence coefficients. Finally, a fuzzy inference method is employed to gain a comprehensive understanding of intentions. Our experimental results demonstrate the effectiveness of the BDFEI model. When compared to existing models, namely FDNNSA, ResNet-101+GFK, and HCFS, the BDFEI model achieved superior accuracy on the FABO database, surpassing them by 12.21%, 1.89%, and 0.78%, respectively. Furthermore, our self-built database experiments yielded an impressive 82.00% accuracy in intention understanding, confirming the efficacy of our emotional intention inference model.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100951"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless control: Retrospective and open vistas","authors":"Matthias Pezzutto ,&nbsp;Subhrakanti Dey ,&nbsp;Emanuele Garone ,&nbsp;Konstantinos Gatsis ,&nbsp;Karl Henrik Johansson ,&nbsp;Luca Schenato","doi":"10.1016/j.arcontrol.2024.100972","DOIUrl":"10.1016/j.arcontrol.2024.100972","url":null,"abstract":"<div><div>The convergence of wireless networks and control engineering has been a technological driver since the beginning of this century. It has significantly contributed to a wide set of emerging applications, such as smart homes, robot swarms, connected autonomous vehicles, and wireless process automation. Envisioning further integration and developments in wireless control, in this paper we provide an overview of past results and present some perspective on the future of the area. Rather than extensively reviewing existing results, we provide a handbook for practitioners who want to tackle and contribute to wireless control. First, we introduce the key types of wireless networks for control applications pointing out their main strengths and their main bottlenecks. Then, we introduce the main technical approaches for the analysis and the design of wireless control showing both their basic ideas and their applicability. Finally, we provide a vision for the future of wireless control and we try to outline the main directions and research questions of the next decade.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100972"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviewer Acknowledgement 2024","authors":"","doi":"10.1016/j.arcontrol.2024.100975","DOIUrl":"10.1016/j.arcontrol.2024.100975","url":null,"abstract":"","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100975"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}