Pub Date : 2025-12-16DOI: 10.1016/j.arcontrol.2025.101042
João Bernardo Aranha Ribeiro , José Dolores Vergara Dietrich , Julio Elias Normey-Rico
To date, several ingenious MPC schemes for oil production have been developed, but most of these are scattered in the issued publications. Thus, in this paper, an extensive literature review focused on the application of model predictive control policies to enhance oil production in deep water rigs was conducted. The research centers on typical extraction scenarios where artificial elevation methods are necessary to achieve economically viable flow rates. Specifically, the study highlights gas-lift production and Electric Submersible Pumps (ESP). In this context, MPCs have the capability to maximize profits from wells, regulate their functioning, and stabilize their operations effectively. In this regard, this review paper provides an overview of the advancements and outcomes in this technology and the current state-of-the-art. The key goal consists of a resume assessing the maturity of MPC strategies — focusing on practical aspects. For all the examined papers, we inspect the main features related to the systems themselves and detail the characteristics of the control systems used including the model, prediction horizon length, and control objective. Furthermore, the review outlines how to tackle computational issues often associated with advanced controllers. A discussion on the advantages and disadvantages of each MPC approach is also presented, emphasizing its struggle to handle complex models. Finally, suggestions for future research avenues are given, aiming to expand the applicability of these MPCs for the aforementioned systems.
{"title":"Systematic survey on model predictive control schemes applied to offshore deep water wells in oil and gas industry","authors":"João Bernardo Aranha Ribeiro , José Dolores Vergara Dietrich , Julio Elias Normey-Rico","doi":"10.1016/j.arcontrol.2025.101042","DOIUrl":"10.1016/j.arcontrol.2025.101042","url":null,"abstract":"<div><div>To date, several ingenious MPC schemes for oil production have been developed, but most of these are scattered in the issued publications. Thus, in this paper, an extensive literature review focused on the application of model predictive control policies to enhance oil production in deep water rigs was conducted. The research centers on typical extraction scenarios where artificial elevation methods are necessary to achieve economically viable flow rates. Specifically, the study highlights gas-lift production and Electric Submersible Pumps (ESP). In this context, MPCs have the capability to maximize profits from wells, regulate their functioning, and stabilize their operations effectively. In this regard, this review paper provides an overview of the advancements and outcomes in this technology and the current state-of-the-art. The key goal consists of a resume assessing the maturity of MPC strategies — focusing on practical aspects. For all the examined papers, we inspect the main features related to the systems themselves and detail the characteristics of the control systems used including the model, prediction horizon length, and control objective. Furthermore, the review outlines how to tackle computational issues often associated with advanced controllers. A discussion on the advantages and disadvantages of each MPC approach is also presented, emphasizing its struggle to handle complex models. Finally, suggestions for future research avenues are given, aiming to expand the applicability of these MPCs for the aforementioned systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101042"},"PeriodicalIF":10.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789938","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}
Pub Date : 2025-11-11DOI: 10.1016/j.arcontrol.2025.101035
Robin Strässer , Karl Worthmann , Igor Mezić , Julian Berberich , Manuel Schaller , Frank Allgöwer
Controlling nonlinear dynamical systems remains a central challenge in a wide range of applications, particularly when accurate first-principle models are unavailable. Data-driven approaches offer a promising alternative by designing controllers directly from observed trajectories. A wide range of data-driven methods relies on the Koopman-operator framework that enables linear representations of nonlinear dynamics via lifting into higher-dimensional observable spaces. Finite-dimensional approximations, such as extended dynamic mode decomposition (EDMD) and its controlled variants, make prediction and feedback control tractable but introduce approximation errors that must be accounted for to provide rigorous closed-loop guarantees. This survey provides a systematic overview of Koopman-based control, emphasizing the connection between data-driven surrogate models, approximation errors, controller design, and closed-loop guarantees. We review theoretical foundations, error bounds, and both linear and bilinear EDMD-based control schemes, highlighting robust strategies that ensure stability and performance. Finally, we discuss open challenges and future directions at the interface of operator theory, approximation theory, and nonlinear control.
{"title":"An overview of Koopman-based control: From error bounds to closed-loop guarantees","authors":"Robin Strässer , Karl Worthmann , Igor Mezić , Julian Berberich , Manuel Schaller , Frank Allgöwer","doi":"10.1016/j.arcontrol.2025.101035","DOIUrl":"10.1016/j.arcontrol.2025.101035","url":null,"abstract":"<div><div>Controlling nonlinear dynamical systems remains a central challenge in a wide range of applications, particularly when accurate first-principle models are unavailable. Data-driven approaches offer a promising alternative by designing controllers directly from observed trajectories. A wide range of data-driven methods relies on the Koopman-operator framework that enables linear representations of nonlinear dynamics via lifting into higher-dimensional observable spaces. Finite-dimensional approximations, such as extended dynamic mode decomposition (EDMD) and its controlled variants, make prediction and feedback control tractable but introduce approximation errors that must be accounted for to provide rigorous closed-loop guarantees. This survey provides a systematic overview of Koopman-based control, emphasizing the connection between data-driven surrogate models, approximation errors, controller design, and closed-loop guarantees. We review theoretical foundations, error bounds, and both linear and bilinear EDMD-based control schemes, highlighting robust strategies that ensure stability and performance. Finally, we discuss open challenges and future directions at the interface of operator theory, approximation theory, and nonlinear control.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101035"},"PeriodicalIF":10.7,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145479289","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}
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.
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.
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.
{"title":"A harmonious synergy between robotic performance and well-being in human-robot collaboration: A vision and key recommendations","authors":"Nicole Berx , Wilm Decré , Joris De Schutter , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2025.101015
Andrew R. Teel , Ricardo G. Sanfelice , Rafal K. Goebel
This paper provides a framework for analyzing numerical simulations of a hybrid system when the system’s flow map is approximated stochastically. In this way, the paper makes one of the first substantive connections between hybrid systems theory and the vast literature on stochastic approximations of non-stochastic dynamical systems. The results include conditions for almost sure boundedness and a characterization of the asymptotic behavior of solutions to the stochastic approximation. Namely, under certain conditions on the expected value and variance of the noisy flow map, and with appropriate step sizes, the sample paths of the solutions of the stochastic approximation are shown to have desired boundedness properties and converge to the chain-recurrent part of the global attractor, when one exists, of the underlying hybrid system. The results are established using martingale methods, Morse decompositions of attractors, and Morse–Lyapunov functions. Since the hybrid system’s flow map and its approximation may be set-valued, stochastic approximations of differential inclusions are covered as a special case. Several examples and simulations are provided to illustrate hybrid systems, their stochastic approximations, and their convergence properties.
{"title":"Stochastic approximation of hybrid systems: Boundedness and asymptotic behavior","authors":"Andrew R. Teel , Ricardo G. Sanfelice , Rafal K. Goebel","doi":"10.1016/j.arcontrol.2025.101015","DOIUrl":"10.1016/j.arcontrol.2025.101015","url":null,"abstract":"<div><div>This paper provides a framework for analyzing numerical simulations of a hybrid system when the system’s flow map is approximated stochastically. In this way, the paper makes one of the first substantive connections between hybrid systems theory and the vast literature on stochastic approximations of non-stochastic dynamical systems. The results include conditions for almost sure boundedness and a characterization of the asymptotic behavior of solutions to the stochastic approximation. Namely, under certain conditions on the expected value and variance of the noisy flow map, and with appropriate step sizes, the sample paths of the solutions of the stochastic approximation are shown to have desired boundedness properties and converge to the chain-recurrent part of the global attractor, when one exists, of the underlying hybrid system. The results are established using martingale methods, Morse decompositions of attractors, and Morse–Lyapunov functions. Since the hybrid system’s flow map and its approximation may be set-valued, stochastic approximations of differential inclusions are covered as a special case. Several examples and simulations are provided to illustrate hybrid systems, their stochastic approximations, and their convergence properties.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101015"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048561","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}
Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control – the prototypical mechanism for managing resource scarcity – is not always well-accepted in socio-technical resource contexts. In this vision article, we advocate for karma economies as an emerging non-monetary mechanism for socio-technical control. Karma leverages the repetitive nature of many socio-technical resources to jointly attain trustworthy, fair, and efficient allocations; by budgeting resource consumption over time and letting resource users “play against their future selves.” To motivate karma, we review related concepts in economics through a control systems lens, and make a case for (a) shifting the viewpoint of resource allocations from single-shot and static to repeated and dynamic games; and (b) adopting Long-run Nash welfare (LNW) as the formalization of “fairness and efficiency” in socio-technical contexts. We show that in many dynamic resource settings, karma Nash equilibria maximize LNW. Moreover, we discuss implications for a future smart city built on multi-karma economies: by choosing whether to combine different socio-technical resources, e.g., electricity and transportation, in a single karma economy, or separate into resource-specific economies, karma provides new flexibility to design the scope of fairness and efficiency.
{"title":"A vision for trustworthy, fair, and efficient socio-technical control using karma economies","authors":"Ezzat Elokda , Andrea Censi , Emilio Frazzoli , Florian Dörfler , Saverio Bolognani","doi":"10.1016/j.arcontrol.2025.101026","DOIUrl":"10.1016/j.arcontrol.2025.101026","url":null,"abstract":"<div><div>Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control – the prototypical mechanism for managing resource scarcity – is not always well-accepted in socio-technical resource contexts. In this vision article, we advocate for <em>karma economies</em> as an emerging non-monetary mechanism for socio-technical control. Karma leverages the repetitive nature of many socio-technical resources to jointly attain trustworthy, fair, and efficient allocations; by budgeting resource consumption over time and letting resource users “play against their future selves.” To motivate karma, we review related concepts in economics through a control systems lens, and make a case for (a) shifting the viewpoint of resource allocations from <em>single-shot and static</em> to <em>repeated and dynamic</em> games; and (b) adopting <em>Long-run Nash welfare (LNW)</em> as the formalization of “fairness and efficiency” in socio-technical contexts. We show that in many dynamic resource settings, karma Nash equilibria maximize LNW. Moreover, we discuss implications for a future smart city built on <em>multi-karma economies</em>: by choosing whether to combine different socio-technical resources, e.g., electricity and transportation, in a single karma economy, or separate into resource-specific economies, karma provides new flexibility to design the <em>scope of fairness and efficiency</em>.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101026"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415423","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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2025.101033
Mohammad Ahmadasas , Emirhan Inanc , Efe Ozkara , Mate Siket , Mudassir M. Rashid , Mustafa Bilgic , Laurie Quinn , Ali Cinar
Cyber–physical–human systems (CPHS) hold the potential to transform healthcare delivery and patient outcomes for numerous chronic diseases, such as diabetes, through remote patient monitoring, automatic control, and precision medicine. Expedited by the recent advances in artificial intelligence, including model development, control systems, data assimilation, network infrastructure, and cybersecurity, the application of digital twins has proliferated across various industry sectors, and have recently been applied to medical settings. Digital twins of people with type 1 diabetes (T1D) and the pancreas can well represent the complex metabolic, physiologic, and pharmacologic processes underlying the chronic disease. This enables intelligent CPHSs that can automate insulin delivery without any manual user announcements to mitigate the effects of various disturbances to glucose homeostasis such as meals, physical activities, acute psychological stress, and sleep pattern variations. Automated insulin delivery in people with T1D, also called artificial pancreas, is a successful application of digital twins in medicine that advances T1D treatment, reducing the burden of the chronic condition and improving the lives of people with T1D. We present a hybrid modeling framework that integrates mechanistic physiological models with data-driven empirical models to develop accurate digital twins of people with T1D, which is then used in an artificial intelligence-enabled automated insulin delivery system. Simulation and clinical experiments integrating virtual patients or individuals with T1D with an artificial pancreas system illustrate the performance of the CPHS, demonstrating the capabilities of rendering fully-automated real-time treatment decisions for precision medicine. Future avenues of research and development in CPHS for precision medicine are also highlighted, including online learning algorithms, adaptive fault-tolerant systems, and robust cybersecurity.
{"title":"Cyber–physical–human systems in precision medicine: Advances in artificial pancreas for treatment of diabetes","authors":"Mohammad Ahmadasas , Emirhan Inanc , Efe Ozkara , Mate Siket , Mudassir M. Rashid , Mustafa Bilgic , Laurie Quinn , Ali Cinar","doi":"10.1016/j.arcontrol.2025.101033","DOIUrl":"10.1016/j.arcontrol.2025.101033","url":null,"abstract":"<div><div>Cyber–physical–human systems (CPHS) hold the potential to transform healthcare delivery and patient outcomes for numerous chronic diseases, such as diabetes, through remote patient monitoring, automatic control, and precision medicine. Expedited by the recent advances in artificial intelligence, including model development, control systems, data assimilation, network infrastructure, and cybersecurity, the application of digital twins has proliferated across various industry sectors, and have recently been applied to medical settings. Digital twins of people with type 1 diabetes (T1D) and the pancreas can well represent the complex metabolic, physiologic, and pharmacologic processes underlying the chronic disease. This enables intelligent CPHSs that can automate insulin delivery without any manual user announcements to mitigate the effects of various disturbances to glucose homeostasis such as meals, physical activities, acute psychological stress, and sleep pattern variations. Automated insulin delivery in people with T1D, also called artificial pancreas, is a successful application of digital twins in medicine that advances T1D treatment, reducing the burden of the chronic condition and improving the lives of people with T1D. We present a hybrid modeling framework that integrates mechanistic physiological models with data-driven empirical models to develop accurate digital twins of people with T1D, which is then used in an artificial intelligence-enabled automated insulin delivery system. Simulation and clinical experiments integrating virtual patients or individuals with T1D with an artificial pancreas system illustrate the performance of the CPHS, demonstrating the capabilities of rendering fully-automated real-time treatment decisions for precision medicine. Future avenues of research and development in CPHS for precision medicine are also highlighted, including online learning algorithms, adaptive fault-tolerant systems, and robust cybersecurity.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101033"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360981","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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2024.100985
Sabine Mondié , Alexey Egorov , Reynaldo Ortiz
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.
{"title":"Lyapunov stability tests for integral delay systems","authors":"Sabine Mondié , Alexey Egorov , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2025.100986
M.F. Yasak , P.M. Heerwan , V.R. Aparow
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.
{"title":"Collision avoidance strategies in autonomous vehicles and on-ramp scenario: A review","authors":"M.F. Yasak , P.M. Heerwan , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2025.100992
Boris Houska , Matthias A. Müller , Mario Eduardo Villanueva
In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured in the design of set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant polytopes. Moreover, we survey recent methods for constructing control Lyapunov functions with polyhedral epigraphs as well as the extensive literature on robust model predictive control. The article concludes with a discussion of both the complexity and potential of polyhedral computing methods that rely on large-scale convex optimization.
{"title":"Polyhedral control design: Theory and methods","authors":"Boris Houska , Matthias A. Müller , Mario Eduardo Villanueva","doi":"10.1016/j.arcontrol.2025.100992","DOIUrl":"10.1016/j.arcontrol.2025.100992","url":null,"abstract":"<div><div>In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured in the design of set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant polytopes. Moreover, we survey recent methods for constructing control Lyapunov functions with polyhedral epigraphs as well as the extensive literature on robust model predictive control. The article concludes with a discussion of both the complexity and potential of polyhedral computing methods that rely on large-scale convex optimization.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 100992"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116077","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}
Pub Date : 2025-01-01DOI: 10.1016/j.arcontrol.2025.101017
Abderrahmane Adel, Rachid Malti, Olivier Briat
The main contribution of this paper is to present two distinct algorithms for fractional system identification using non-zero initial conditions, by assuming the input signal prior to and the input/output signals after known. Addressing this problem is particularly important, in the context of short-time data acquisition, mainly because the effect of the free response is important compared to the forced one and because the time response of fractional systems converge polynomially, as compared to the exponential convergence of rational systems. The first developed algorithm uses a two-stage iterative procedure that computes system forced response at the upper stage, and system parameters at the lower stage using the forced response. The second one uses the simultaneous contribution of system free and forced responses. The efficacy of both algorithms is first assessed using Monte Carlo simulations with significant signal to noise ratios. The proposed algorithms allow solving a technical issue on commercial battery cells: their identification using input–output data whatever their history, i.e. the battery cells need not be in a completely relaxed state (with zero initial conditions) prior to collecting system identification data, contrary to the actual practice.
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