Annual Reviews in Control最新文献

{"title":"A harmonious synergy between robotic performance and well-being in human-robot collaboration: A vision and key recommendations","authors":"Nicole Berx ,&nbsp;Wilm Decré ,&nbsp;Joris De Schutter ,&nbsp;Liliane Pintelon","doi":"10.1016/j.arcontrol.2024.100984","DOIUrl":"10.1016/j.arcontrol.2024.100984","url":null,"abstract":"<div><div>The transition from Industry 4.0 to Industry 5.0 marks a significant shift towards human-centric manufacturing processes, emphasizing the integration of collaborative robots with advanced sensory and cognitive abilities. Unlike previous industrial revolutions, Industry 5.0 prioritizes the integration of human workers alongside advanced technologies, emphasizing collaboration, and acknowledging the unique strengths of both humans and machines, with a focus on human well-being. However, this transition presents significant challenges to adopting cobots in industries due to safety concerns compromising efficiency. While robotics and automation traditionally focus on maximizing performance and minimizing human intervention, the latter no longer applies to human-robot collaboration. There is a need for developing approaches and technologies that can seamlessly combine high-level robotic performance with safety, as well as pursue operator well-being. This paper presents a vision and specific recommendations for a harmonious synergy between robot performance and operator well-being in human-robot collaboration.</div><div>Our vision includes the need to develop cobots that are contextually intelligent, capable of meaningful conversation, and adaptable to changing conditions. The paper identifies current challenges, such as safety concerns impacting performance, a narrow safety focus and overlooked system-wide impacts, limited guidance on well-being, and insufficient interdisciplinary approaches. To overcome the identified challenges, key recommendations essential for achieving the vision are outlined, and pathways to overcome remaining obstacles are presented. These recommendations include designing context-aware, cognitively embodied, and socially proficient cobots; balancing autonomy and control in task allocation; and adopting a socio-technical systems perspective. Although numerous technical obstacles remain, the rapid advances in Artificial Intelligence (AI), particularly in generative AI, provide an extraordinary and previously unattainable catalyst for realizing our vision, serving as a fundamental enabler.</div><div>Our methodology combines expert synthesis and a narrative literature review, drawing on diverse academic domains such as robotics, industrial manufacturing, safety, and human factors. This paper advances human-robot collaboration research by adopting a holistic approach that integrates engineering and non-engineering perspectives, emphasizing technical performance, safety, well-being, and socio-technical systems to optimize collaboration. We aim to inspire and guide both the engineering and robotics community and the human factors and safety community toward developing more holistic, safer, and human-centered collaborative robotic systems. By embracing the interdisciplinary approach, we advocate in this paper, both technical and non-technical experts can benefit from the insights provided.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100984"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136878","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":"Lyapunov stability tests for integral delay systems","authors":"Sabine Mondié ,&nbsp;Alexey Egorov ,&nbsp;Reynaldo Ortiz","doi":"10.1016/j.arcontrol.2024.100985","DOIUrl":"10.1016/j.arcontrol.2024.100985","url":null,"abstract":"<div><div>An overview of stability conditions in terms of the Lyapunov matrix for linear integral delay equations is presented. Several examples in the analysis, control and modeling motivate their study. In the framework of Lyapunov–Krasovskii functionals with prescribed derivatives, we review the stability theorems for these functionals and prove a stability criterion (necessary and sufficient condition) in terms of the system delay Lyapunov matrix. The organization of the paper and the detailed developments have the purpose of serving as a tutorial. As a new result, we prove that the stability criterion can be tested in a finite number of operations. Finally, we suggest future directions of research in the field, in particular, the reduction of the bound for which sufficiency is guaranteed and the extension to more general classes of systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100985"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137413","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":"Collision avoidance strategies in autonomous vehicles and on-ramp scenario: A review","authors":"M.F. Yasak ,&nbsp;P.M. Heerwan ,&nbsp;V.R. Aparow","doi":"10.1016/j.arcontrol.2025.100986","DOIUrl":"10.1016/j.arcontrol.2025.100986","url":null,"abstract":"<div><div>Collision avoidance (CA) in autonomous vehicles (AVs) is essential for the safety and efficiency of modern transportation systems. This paper delves into various strategies and methodologies for CA, categorizing them to improve clarity and comprehension. The research primarily reviews peer-reviewed journals and conference proceedings from the past five years, though notable older studies are also considered. Non-ground AVs research was excluded from this analysis. The CA strategies identified are grouped into six categories: combination of path planning and path tracking control (PP + PTC), path planning (PP), steering, braking, combination of steering and braking, and other methods. Among these, the PP + PTC strategy was the most common, used in 44 cases (38.9%), followed by PP in 16 cases (14.2%), steering in 15 cases (13.3%), other methods and combination of steering and braking in 13 cases each (11.5%), and braking in 12 cases (10.6%). Additionally, the study highlights the on-ramp scenario as an area needing more research. For this scenario, connected AVs (CAV) was the most frequently studied strategy, with 11 cases, followed by machine learning approaches with 9 cases, and other methods with 3 cases. The results underscore the importance of the PP + PTC strategy for effective CA, as it combines PP with PTC to execute planned trajectories efficiently. These insights aim to aid in developing more robust and reliable CA systems in AVs, contributing to safer and more efficient transportation.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100986"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428022","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":"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":"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":"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}