Annual Reviews in Control最新文献

{"title":"A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas","authors":"Iqra Shafeeq Mughal ,&nbsp;Luca Patanè ,&nbsp;Riccardo Caponetto","doi":"10.1016/j.arcontrol.2024.100937","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100937","url":null,"abstract":"<div><p>An autoimmune disease known as type 1 diabetes occurs when the immune system mistakenly attacks and destroys the beta cells in the pancreas, impairing their ability to produce insulin. An artificial pancreas is a device that is able to analyze information from sensors, such as continuous glucose monitoring, to deliver the correct amount of insulin by subcutaneous injection via a pump. The design and development of such an artificial pancreas poses several challenges. One of these is the need for an appropriate mathematical model of the patient’s physiology in order to develop a suitable controller. Over the past three decades, a number of artificial pancreas control techniques have been investigated in simulation and clinical research. This review aims to advance the knowledge of artificial pancreas system development by providing a comprehensive overview of recent advances in modeling the biological processes involved and in developing nonlinear control strategies. Real-time parameter estimation and effective uncertainty management as well as in-depth clinical studies and long-term investigations are relevant aspects that need to be evaluated for assessing the efficacy and safety of the artificial pancreas in practice. Further perspectives on control techniques that address patient-specific conditions and enable effective and individualized diabetes management will also be discussed.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100937"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1367578824000063/pdfft?md5=f8895dc41b0d3656cd091ce4169cfc18&pid=1-s2.0-S1367578824000063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139733172","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":"A review of physics-based learning for system health management","authors":"Samir Khan ,&nbsp;Takehisa Yairi ,&nbsp;Seiji Tsutsumi ,&nbsp;Shinichi Nakasuka","doi":"10.1016/j.arcontrol.2024.100932","DOIUrl":"10.1016/j.arcontrol.2024.100932","url":null,"abstract":"<div><p><span>The monitoring process for complex infrastructure requires collecting various data sources with varying time scales, resolutions, and levels of abstraction. These data sources include data from human inspections, historical failure records, cost data, high-fidelity physics-based simulations, and online health monitoring. Such heterogeneity presents significant challenges in implementing a diagnostic and prognostic framework for decision-making regarding maintenance (and other life cycle actions). The core challenge lies in the effective integration of physical information and data-driven models, aiming to synergize their strengths to overcome individual limitations. One possible solution is to propose an approach that considers the strengths and limitations of each data source, as well as their compatibility with each other. The flexibility and efficacy of contemporary learning approaches can be used with more systematic and informative physics-based models that draw on domain expertise. This represents an inherent desire to base all inferences on both our engineering knowledge and monitoring data that is at our disposal. In this context, the article reviews recent advances in this field, particularly in physics-based and deep learning techniques. It looks at new theories and models developed in the last five years, especially those used in </span>system health monitoring, predicting damage, and planning maintenance. These new methods are proving to be more accurate and efficient than older, more traditional techniques. However, there are still challenges to be addressed. These include the need for high-quality data, finding the right balance between accuracy and the time it takes to compute, and effectively combining physical models with data-driven models. The paper calls for further research into methods that can handle large amounts of complex data and consider uncertainties in both the models and the data. Finally, it highlights the need to explore how these models can be adapted for different systems and used in real-time applications.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100932"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139483883","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":"Cognitive systems and interoperability in the enterprise: A systematic literature review","authors":"Jana Al Haj Ali ,&nbsp;Ben Gaffinet ,&nbsp;Hervé Panetto ,&nbsp;Yannick Naudet","doi":"10.1016/j.arcontrol.2024.100954","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100954","url":null,"abstract":"<div><p>The transition from automated processes to mechanisms that manifest intelligence through cognitive abilities such as memorisation, adaptability and decision-making in uncertain contexts, has marked a turning point in the field of industrial systems, particularly in the development of cyber–physical systems and digital twins. This evolution, supported by advances in cognitive science and artificial intelligence, has opened the way to a new era in which systems are able to adapt and evolve autonomously, while offering more intuitive interaction with human users. This article proposes a systematic literature review to gather and analyse current research on Cognitive Cyber–Physical Systems (CCPS), Cognitive Digital Twins (CDT), and cognitive interoperability, which are pivotal in a contemporary Cyber–Physical Enterprise (CPE). From this review, we first seek to understand how cognitive capabilities that are traditionally considered as human traits have been defined and modelled in cyber–physical systems and digital twins in the context of Industry 4.0/5.0, and what cognitive functions they implement. We explore their theoretical foundations, in particular in relation to cognitive psychology and humanities definitions and theories. Then we analyse how interoperability between cognitive systems has been considered, leading to cognitive interoperability, and we highlight the role of knowledge representation and reasoning.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100954"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1367578824000233/pdfft?md5=3917edec97a49bf2483d54fdbcb38abd&pid=1-s2.0-S1367578824000233-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180646","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":"Recursive identification methods for general stochastic systems with colored noises by using the hierarchical identification principle and the filtering identification idea","authors":"Feng Ding ,&nbsp;Ling Xu ,&nbsp;Xiao Zhang ,&nbsp;Yihong Zhou ,&nbsp;Xiaoli Luan","doi":"10.1016/j.arcontrol.2024.100942","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100942","url":null,"abstract":"<div><p>This article reviews and investigates several basic recursive parameter identification methods for a general stochastic system with colored noise (i.e., output-error autoregressive moving average system or Box–Jenkins system). These recursive identification methods are derived by means of the hierarchical identification principle and the filtering identification idea, including a filtered auxiliary-model hierarchical generalized extended stochastic gradient algorithm, a filtered auxiliary-model hierarchical multi-innovation generalized extended stochastic gradient algorithm, a filtered auxiliary-model hierarchical recursive generalized extended gradient algorithm, a filtered auxiliary-model hierarchical multi-innovation recursive generalized extended gradient algorithm, a filtered auxiliary-model hierarchical generalized extended least squares algorithm, and a filtered auxiliary-model hierarchical multi-innovation generalized extended least squares algorithm by using the auxiliary-model identification idea. The presented filtered auxiliary-model hierarchical generalized extended identification algorithms can be extended to other linear and nonlinear systems with colored noises.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100942"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140296016","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":"Fault tolerant control of an octorotor UAV using sliding mode for applications in challenging environments","authors":"Ahmed Khattab,&nbsp;Ibrahim Mizrak,&nbsp;Halim Alwi","doi":"10.1016/j.arcontrol.2024.100952","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100952","url":null,"abstract":"<div><p>This paper presents the development of fault-tolerant controller and their application for multirotor unmanned aerial vehicles – specifically an octorotor – in challenging environments e.g. nuclear power plant inspection or other dull, dirty and dangerous applications. This paper considers a combination of sliding mode control robustness properties (to deal with actuator faults) and control allocation (to automatically redistribute the control signals to healthy actuators, especially in the event of actuator failures). The resultant controller has the ability to operate in both fault-free and fault/failure conditions without reconfiguring the main baseline controller. The proposed controller also has the ability to operate for up to six rotor failures which represent an under-actuation condition i.e., a case when only two rotors are available. The under-actuation scenarios are conditions when most FTC schemes are not able to operate due to the lack of redundancy. The simulation results conducted on the nonlinear model with wind/gusts and sensor noise, show a good tracking performance under various fault-free and fault/failure scenarios (over-actuation, sufficient actuation and under-actuation conditions).</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100952"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S136757882400021X/pdfft?md5=5dc92f43946084eb1a8f20784a6582e2&pid=1-s2.0-S136757882400021X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344661","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":"Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review","authors":"Mohamad Al Bannoud ,&nbsp;Carlos Alexandre Moreira da Silva ,&nbsp;Tiago Dias Martins","doi":"10.1016/j.arcontrol.2024.100973","DOIUrl":"10.1016/j.arcontrol.2024.100973","url":null,"abstract":"<div><div>The growing competitiveness of the chemical industry, along with sustainability demands and regulatory requirements, calls for optimized and well-controlled operations. Chemical engineering processes are often characterized by non-linearity, strong variable coupling, dead times, multiple inputs and outputs, and operational constraints, making control strategies challenging. Model predictive control is widely used for its advantages in optimal control, flexibility, robustness, and ability to handle multi-objective tasks. However, precise tuning and optimization are essential for implementing this strategy in real-time applications. Metaheuristic optimization algorithms offer an alternative to traditional optimization methods, as they can quickly reach near-optimal solutions and avoid local minima, making them well-suited for use with model predictive control. This study aims to analyze the application of metaheuristic optimization algorithms in conjunction with model predictive control in chemical engineering processes through a systematic review. The review considers three eligibility criteria: applying model predictive control for process control, utilizing metaheuristic optimization algorithm, and chemical engineering-related processes. A total of 46 studies were analyzed, revealing three main application areas for metaheuristic optimization algorithms in model predictive control: improving dynamic models used in the receding horizon, tuning model predictive control parameters, and serving as optimizers in the model predictive control formulation. Over 20 different metaheuristic optimization algorithms and various process models were identified, with typical applications including continuous stirred tank reactors, tank-level control, and column distillation. Genetic algorithms and particle swarm optimization were the most frequently used algorithms. This review concludes that metaheuristic optimization algorithms have been successfully applied to enhance model predictive control in several processes. It also highlights the benefits, weaknesses, and limitations of metaheuristic optimization algorithms applications in chemical engineering processes and provides recommendations for future research. We hope this study will be valuable to professionals and researchers in chemical engineering and process control.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100973"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142551921","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":"Modelling and control of manipulators for inspection and maintenance in challenging environments: A literature review","authors":"Alessandro Pistone ,&nbsp;Daniele Ludovico ,&nbsp;Lorenzo De Mari Casareto Dal Verme ,&nbsp;Sergio Leggieri ,&nbsp;Carlo Canali ,&nbsp;Darwin G. Caldwell","doi":"10.1016/j.arcontrol.2024.100949","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100949","url":null,"abstract":"<div><p>Nowadays, the use of robotic systems for inspection and maintenance is gaining importance due to the number of scenarios in which robots can operate. Indeed, robotic systems provide many advantages in harsh and hostile environments, improving workers’ safety and overall efficiency. Given their ability to perform different tasks, robotic manipulators constitute a significant proportion of the possible robotic systems employed in these environments. The category of manipulators is a heterogeneous group that comprises many different types of robots: non-redundant, redundant, and hyper-redundant manipulators, the latter being subdivided into discrete-joint manipulators and continuum manipulators. Among these types of robots, hyper-redundant manipulators play a crucial role in operating in challenging environments due to their ability to perform auxiliary tasks, such as obstacle avoidance and joint limits satisfaction. Furthermore, manipulators can be made of rigid or soft mechanisms and can be mobile, operating in aerial, ground, and underwater environments. The objective of this review article is to provide a reference point for researchers interested in modelling and controlling manipulators for inspection and maintenance in challenging environments.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100949"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S136757882400018X/pdfft?md5=3f1f22153adc5bd441be695f4053d5a4&pid=1-s2.0-S136757882400018X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141709","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: Finite-dimensional implementations, Part II","authors":"Andrew J. Kurdila ,&nbsp;Andrea L’Afflitto ,&nbsp;John A. Burns ,&nbsp;Haoran Wang","doi":"10.1016/j.arcontrol.2024.100968","DOIUrl":"10.1016/j.arcontrol.2024.100968","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, also called a reproducing kernel Hilbert space (RKHS). As discussed in the first paper of this two-part work, the proposed framework relies on a limiting distributed parameter system (DPS). To allow implementations of this framework in finite dimensions, this paper shows how several techniques developed in parametric MRAC, such as the <span><math><mi>σ</mi></math></span>-modification method, the deadzone modification, adaptive error bounding methods, and projection methods, can be generalized to the proposed nonparametric setting. Some of these techniques assure uniform ultimate boundedness of the trajectory tracking error, while others guarantee its asymptotic convergence to zero. This paper introduces nonparametric metrics of performance that are cast in terms of the functional uncertainty classes in the native space. These performance metrics are relative to the best offline approximation error of the functional uncertainty. All the provided performance bounds are explicit in the dimension of the approximations of the functional uncertainty. Numerical examples show the applicability of the proposed theoretical results.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100968"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325947","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":"Shaping the future of advanced automation and control systems for society strategic directions and multidisciplinary collaborations of IFAC's social systems coordinating committee","authors":"Larry Stapleton ,&nbsp;Fei-Yue Wang ,&nbsp;Mariana Netto ,&nbsp;Qing-Shan Jia ,&nbsp;Antonio Visioli ,&nbsp;Peter Kopacek","doi":"10.1016/j.arcontrol.2024.100967","DOIUrl":"10.1016/j.arcontrol.2024.100967","url":null,"abstract":"<div><div>In an era of rapid advancements in highly intelligent digital systems, blockchain, and other transformative technologies, the role of control and automation in shaping human civilization is of paramount, even critical, importance. This paper examines the strategic significance of IFAC's Social Systems Coordinating Committee (CC), a unique multidisciplinary global community of researchers and practitioners comprising leading universities, research centers, industry partners and international agencies at the forefront of integrating technological and societal progress.</div><div>This paper reports the results of a strategic \"milestone\" review, including an extensive meta-analysis of the Social Systems CC's five Technical Committees (TCs) and their activities. It uncovers key themes emphasizing this CC's contributions to models, systems, infrastructures, and operations. Using content analysis and word clouds, 272 keywords were refined to elucidate the main themes of the CC, revealing significant current and future collaborations with other IFAC communities and external organizations. The paper identifies high-potential new cooperation opportunities between this CC and the other IFAC CCs and their TCs, suggesting ways to achieve these collaborations. The findings highlight the Social Systems CC's unique position at the heart of the global automation and control community, where it offers practical applications in planning, management, and sustainability as well as fostering cross-sector cooperation crucial for human progress and effective humanitarian and environmental responses. This paper underscores the Social Systems CC's role in advancing control science and automation systems engineering to tackle pressing societal challenges, advocating for a future where technology and human systems synergize for the global well-being of all living systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"58 ","pages":"Article 100967"},"PeriodicalIF":7.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311655","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":"A real-time interactive decision-making and control framework for complex cyber-physical-human systems","authors":"Chen-Lian Hu,&nbsp;Lei Wang,&nbsp;Mei-Ling Chen,&nbsp;Cheng Pei","doi":"10.1016/j.arcontrol.2024.100938","DOIUrl":"https://doi.org/10.1016/j.arcontrol.2024.100938","url":null,"abstract":"<div><p>Over the past decade, the advancement of digital technology has significantly enhanced operations management in complex cyber-physical systems (CPSs), especially in the production and manufacturing sectors. In such systems, the physical and cyber spaces are generally connected through sensors, networking, and control actions. With the surge in available real-time data, automation and intelligence have become increasingly prevalent. However, full automation and sophisticated intelligence often remain challenging to achieve in real-world CPSs. Currently, many practical tasks in CPSs are best tackled through the integration of human cognitive skills with autonomous systems, highlighting the indispensable role that humans play in these settings. In this study, we present a framework for real-time decision-making and control in complex cyber-physical-human systems. The framework consists of three main modules: intelligent data processing, intelligent decision-making and control, and human-computer interaction. It is designed to provide a practical and implementable framework for supporting real-time decision-making and control in cyber-physical-human system applications. To demonstrate the applicability of the framework, we build a comprehensive decision support tool to manage several important real-time decision-making and control tasks at a container terminal. The tool is seamlessly integrated into the main operating system of the container terminal and aids decision-makers in making optimal decisions and generating appropriate control actions. The effectiveness of the tool is confirmed by observed improvements in several key operational efficiency indicators at the container terminal.</p></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"57 ","pages":"Article 100938"},"PeriodicalIF":9.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113830","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}