Annual Reviews in Control最新文献

{"title":"Is normoglycemia a reachable state in safe, effective glycemic control in critical care? A review and steady-state analysis showing the need for dual insulin and nutrition control","authors":"J. Geoffrey Chase,&nbsp;Marie Seret,&nbsp;Vincent Uyttendaele,&nbsp;Jennifer L. Knopp,&nbsp;Helen Shannon,&nbsp;Thomas Desaive","doi":"10.1016/j.arcontrol.2026.101072","DOIUrl":"10.1016/j.arcontrol.2026.101072","url":null,"abstract":"<div><div>Critically ill patients frequently experience stress-induced hyperglycemia, leading to increased morbidity and mortality. Safe, effective glycemic control (GC) with insulin therapy alone has proven difficult to achieve consistently. One successful, personalized and model-based GC system overcomes this issue by modulating nutrition, as well as insulin, to control glucose levels. However, no other published protocol takes this approach. Thus, the question arises on whether both control inputs are required to be able to reach normoglycemia safely and effectively.</div><div>This work uses a well-validated metabolic system model to show how nonlinear insulin saturation dynamics, and the interaction of insulin sensitivity with nutrition and insulin inputs, combine to require modulation of nutrition as well as insulin to achieve a desired outcome glycemic level – effectively analysing the ability of glycemic control to reach normoglycemia. In doing so, it also shows without nutrition control, insulin resistant patients will be uncontrollable to a given glycemic level in a theoretical sense and derives the mathematical conditions when this situation occurs. Next, it derives how insulin sensitivity variability, common in intensive care unit (ICU) patients as condition evolves, leads to changes in glucose levels, increasing hyper- and hypo- glycemic risk if nutrition is not controlled to allow lower insulin doses and thus a reduced range of glycemic variability. The analysis is augmented by a shared visual analysis, illustrating the metabolic dynamics to provide better insight and understanding.</div><div>The primary result is a mathematical, clinical, and control centred review and analysis of the clinical glycemic control problem in the context of assessing the conditions for normoglycemia to be reached safely and consistently. It mathematically demonstrates the necessity of using nutrition modulation, along with controlling insulin, to achieve safe, effective GC for all patients when nonlinear clinical and physiological limits are considered. This main outcome is contrary to a lot of current clinical practice and thinking, and contrary to a strictly mathematical analysis without consideration of known, but often ignored, nonlinear physiological and clinical limitations.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101072"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148995208","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":"Data-driven integration of wheel force sensing and yaw stability control in distributed drive electric vehicle: A review and perspective","authors":"Wenjie Wang ,&nbsp;Ran Shu ,&nbsp;Yijie Yu ,&nbsp;Zhigang Chu ,&nbsp;Hongyu Shu","doi":"10.1016/j.arcontrol.2026.101051","DOIUrl":"10.1016/j.arcontrol.2026.101051","url":null,"abstract":"<div><div>With the rapid advancement of distributed drive electric vehicles (DDEVs), ensuring yaw stability under extreme conditions has become a critical challenge. Although electrification and intelligence have reshaped chassis architectures, the principles of wheel force transmission are unchanged. Current yaw stability control strategies, reliant on estimating rather than measuring tire-road forces, encounter a fundamental bottleneck. The lack of direct wheel force sensing (WFS) primarily causes well-known challenges: (1) insufficient state estimation accuracy under varying road conditions, and (2) limited algorithm adaptability to nonlinear force transmission. While prior reviews cover yaw stability control or sensor technologies separately, few integrate them for DDEVs. To bridge this gap, this paper first analyzes the bottleneck of model dependency. It then reviews yaw stability control strategies for DDEVs, highlighting physical limits. Subsequently, the current status of WFS technologies is summarized, focusing on accuracy, robustness, cost, and real-time constraints. Finally, a comprehensive framework for utilizing wheel force data is presented, illustrating how WFS can (A) empower model-based control, (B) enable advanced data-driven control strategies, and (C) facilitate data applications for offline design. The review identifies unresolved challenges and explores future research direction, emphasizing the potential of treating WFS as a data source for data-driven control frameworks. This paper provides researchers and engineers with an integrated perspective on the convergence of WFS and yaw stability control, offering insights into advancing the safety and reliability of next-generation DDEVs.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101051"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395203","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":"Systematic survey on model predictive control schemes applied to offshore deep water wells in oil and gas industry","authors":"João Bernardo Aranha Ribeiro ,&nbsp;José Dolores Vergara Dietrich ,&nbsp;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":"2026-01-01","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}

{"title":"An overview of Koopman-based control: From error bounds to closed-loop guarantees","authors":"Robin Strässer ,&nbsp;Karl Worthmann ,&nbsp;Igor Mezić ,&nbsp;Julian Berberich ,&nbsp;Manuel Schaller ,&nbsp;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":"2026-01-01","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}

{"title":"Ultra-local model-free control of power converters: A comprehensive survey on methodologies, applications, and challenges","authors":"Mahdi S. Mousavi,&nbsp;S. Alireza Davari,&nbsp;Moslem Dehghani,&nbsp;Behnam Nikmaram,&nbsp;Jose Rodriguez","doi":"10.1016/j.arcontrol.2026.101079","DOIUrl":"10.1016/j.arcontrol.2026.101079","url":null,"abstract":"<div><div>The model-free control (MFC) theory, which employs an ultra-local model to replace the conventional model of a plant, has recently become a research trend in the field of power electronics and electrical drives. The idea is to control a system just by having the measured data without using the parameters of the plant in order to avoid disturbances and parameter mismatch effects. Therefore, the MFC considers the inner dynamics, disturbances, and unmodeled parts of the system as a lumped unknown term. Then, identifies it and rejects its effect in the control process. Due to its powerful performance and straightforward implementation routine, the MFC has been extensively applied to power converters and has also been integrated with other popular control methods in the field, such as model predictive control and sliding mode control. This article gives a comprehensive survey of the MFC methods applied to power converters. A classification is made for various types of MFC strategies, and their different applications are explored. Then, the design and implementation challenges are discussed. Finally, some notes are given about the future trends of this method.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101079"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211150","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":"Differential flatness in optimization-based motion planning and control","authors":"Huu-Thinh Do,&nbsp;Ionela Prodan,&nbsp;Florin Stoican,&nbsp;Florentina Nicolau","doi":"10.1016/j.arcontrol.2026.101080","DOIUrl":"10.1016/j.arcontrol.2026.101080","url":null,"abstract":"<div><div>Differential flatness has demonstrated numerous advantages and diverse applications in motion planning and control and while traditionally associated with robotic and mechanical systems, its applicability extends beyond these domains. By employing a change in the coordinate system, differential flatness transforms complex, nonlinear system dynamics into a linear representation. In essence, it provides a model inversion strategy that proves particularly useful for solving problems related to optimal trajectory generation and tracking under various constraints.</div><div>This work offers an overview of past and current advancements in optimization-based motion planning problems that employ differential flatness. To the best of our knowledge, no prior review has been conducted from this perspective. We begin with an in-depth analysis of the foundational principles of flatness, examining its key properties and its intrinsic connection to feedback linearization. Particular emphasis is placed on the available choices for flat parameterization, which facilitate the characterization of costs and constraints in optimization problems. Furthermore, we explore tools for curve parameterization, which are essential for addressing trajectory generation problems involving constraints such as saturation limits, collision avoidance or obstacle navigation. Our review is structured around the general framework of motion planning, which comprises three main components: guidance, control, and adaptation. For each of these components, we analyze and synthesize the use of differential flatness.</div><div>Rather than aiming for an exhaustive catalog or focusing narrowly on specific applications, this review seeks to highlight the benefits of differential flatness and aims to inspire new research directions in motion planning and control.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101080"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149252014","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":"Model Predictive Control via Probabilistic Inference: A tutorial and survey","authors":"Kohei Honda","doi":"10.1016/j.arcontrol.2026.101052","DOIUrl":"10.1016/j.arcontrol.2026.101052","url":null,"abstract":"<div><div>This paper presents a tutorial and survey on Probabilistic Inference-based Model Predictive Control (PI-MPC). PI-MPC reformulates finite-horizon optimal control as inference over an optimal control distribution expressed as a Boltzmann distribution weighted by a control prior, and generates actions through variational inference. In the tutorial part, we derive this formulation and explain action generation via variational inference, highlighting Model Predictive Path Integral (MPPI) control as a representative algorithm with a closed-form sampling update. In the survey part, we organize existing PI-MPC research around key design dimensions, including prior design, multi-modality, constraint handling, scalability, hardware acceleration, and theoretical analysis. This paper provides a unified conceptual perspective on PI-MPC and a practical entry point for researchers and practitioners in robotics and other control applications.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101052"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750655","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":"Distributed differentiation and exact dynamic average consensus: High-order sliding modes over networks","authors":"Rodrigo Aldana-Lopez,&nbsp;Irene Perez-Salesa,&nbsp;Rosario Aragues,&nbsp;Carlos Sagues,&nbsp;David Gomez-Gutierrez","doi":"10.1016/j.arcontrol.2026.101074","DOIUrl":"10.1016/j.arcontrol.2026.101074","url":null,"abstract":"<div><div>This paper addresses the problem of distributed differentiation over networks, where a collection of agents, each with access only to a local time-varying signal, must cooperatively estimate the derivatives of a global aggregate using neighbor-to-neighbor communication. This problem arises naturally in decentralized control, distributed estimation, and networked signal processing, where derivative information is required for feedback design, prediction, active disturbance rejection and coordination. Prior techniques in this context face important structural limitations. Linear architectures cannot achieve exact tracking under persistently varying inputs, while cascaded consensus-and-differentiation designs are intrinsically sensitive to residual consensus errors, measurement noise, and implementation effects, which are amplified by the differentiation stage. As a result, distributed differentiation is more naturally addressed within a nonlinear framework, where consensus and differentiation are designed as a coupled mechanism rather than as separate stages. This paper develops a unified framework based on homogeneous high-order sliding-mode dynamics. Within this setting, we describe a class of distributed differentiators that achieve finite-time exact reconstruction of the average signal and its derivatives of arbitrary order using only local interactions. The proposed design matches the fundamental accuracy limits of differentiation under measurement noise, achieving the correct error scaling with respect to noise amplitude. Beyond the continuous-time setting, we show that the same structural properties extend to discrete-time and asynchronous implementations. Structure-preserving discretizations and asynchronous communication schemes retain stability and maintain favorable accuracy scaling with respect to sampling and communication intervals. Variants for standalone operation, open networks, and leader–follower architectures are also developed within the same framework. The main contribution is both constructive and conceptual. The paper provides distributed differentiator algorithms with provable guarantees, and establishes homogeneity and nonsmooth coupling as the structural principles that characterize the proposed class of exact decentralized solutions. This positions distributed differentiation as a fundamental primitive in networked systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101074"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149050609","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 of grid-connected DFIG wind turbines based on artificial intelligence: From conventional intelligent controllers to deep reinforcement learning - a comprehensive review","authors":"Elmostafa Chetouani,&nbsp;Oumaima Echab,&nbsp;Mustapha El Haroussi,&nbsp;Abdellfattah Ba-Razzouk,&nbsp;Elhassane Abdelmounim,&nbsp;Said Mahfoud","doi":"10.1016/j.arcontrol.2026.101070","DOIUrl":"10.1016/j.arcontrol.2026.101070","url":null,"abstract":"<div><div>In the shift to sustainable and renewable energy generation, wind energy conversion systems (WECSs) based on the Doubly-Fed Induction Generator (DFIG) have gained significant attention due to their lower converter rating, excellent efficiency, and controlled active and reactive power. However, classical control strategies for DFIG systems face significant challenges, including high nonlinear dynamics, parametric uncertainties, external disturbances, wind speed variability, and growing demand for electrical grid integration. Conventional Artificial Intelligence (AI) techniques, including Neural Networks (NNs) and Fuzzy Logic Controllers (FLCs), have been widely adopted to improve control robustness and system efficiency under severe conditions. Furthermore, optimization algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been widely used to tune controller parameters and enhance dynamic performance. Besides, machine learning approaches, such as Support Vector Machines and Random Forests, have demonstrated promising capabilities in terms of wind speed prediction and system performance enhancement. More recently, Deep Reinforcement Learning has emerged as a promising approach for intelligent and adaptive control in challenging and complicated situations. This paper presents a comparative review of AI-based control techniques for grid-connected DFIG wind turbines. A unified taxonomy of AI-based DFIG control methods is proposed, and the reviewed approaches are comparatively analyzed in terms of control objectives, computational complexity, implementation feasibility, robustness, dynamic performance, and real-time applicability. Particular attention is given to research developments reported between 2022 and 2026 to determine emerging trends in these AI-based methods. Key challenges and future research directions are discussed to support the development of high-performance and reliable DFIG-based WECSs.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101070"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148770512","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 bibliographical review on event-triggered control of autonomous robots","authors":"Ziquan Yu,&nbsp;Chuan Yu,&nbsp;Youmin Zhang,&nbsp;Bin Jiang","doi":"10.1016/j.arcontrol.2026.101069","DOIUrl":"10.1016/j.arcontrol.2026.101069","url":null,"abstract":"<div><div>In recent years, event-triggered control (ETC) technology has received widespread attention for autonomous robots in both theory and engineering fields. Such a mechanism significantly reduces the communication consumption and computing resources by triggering control updates only when specific events occur, while ensuring the system stability and performance. This review aims to systematically review the latest research progress in the ETC field for three types of autonomous robots. Firstly, the basic architectures of various ETC methods, as well as the advantages and disadvantages are discussed. Then, the practical engineering problems of ETC are analyzed, and the existing methods are summarized. Additionally, three application scenarios are outlined to demonstrate the engineering potential of ETC strategy in the fields of flying robots, mobile robots, and marine robots. Finally, some challenging issues and future research directions are raised to promote the ETC research for autonomous robots.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"62 ","pages":"Article 101069"},"PeriodicalIF":14.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148634694","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}