Annual Reviews in Control最新文献

{"title":"AI-empowered modeling and control for automotive propulsion system: Challenges, solutions and prospects","authors":"Jian Tang ,&nbsp;Tianyi He ,&nbsp;Wenpeng Wei ,&nbsp;Guoming Zhu","doi":"10.1016/j.arcontrol.2026.101048","DOIUrl":"10.1016/j.arcontrol.2026.101048","url":null,"abstract":"<div><div>This paper comprehensively reviews the modeling and control of automotive propulsion systems, with a particular focus on the integration of emerging artificial intelligence (AI) techniques. The highlights of this review paper include: (1) This review specially focuses on AI-empowered modeling and control for automotive propulsion systems, filling the blank in existing surveys; (2) Different approaches to leverage AI in propulsion applications are summarized, categorized and analyzed. Prospects for possible future directions are discussed based on state-of-the-art researches; and (3) Case studies and examples are given to demonstrate the effectiveness of AI techniques applied to automotive propulsion systems.</div><div>The review starts with the introduction of the fundamental principles of propulsion systems for Internal Combustion engine Vehicles (ICVs), Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs), along with discussions of traditional modeling developments and their advantages and disadvantages. Motivations and prospects of leveraging AI techniques for automotive propulsion systems are highlighted. Subsequently, this paper summarizes the modeling approaches into white-, gray- and black-box methods and discusses their respective strengths and limitations. More importantly, this paper defines the AI approaches, outlines its typical methodologies, and highlights its peculiar significance in automotive propulsion systems, where challenges in modeling and control are identified, and solutions provided by emerging AI techniques are discussed. Particularly, various AI applications in propulsion systems are addressed, including direct and indirect data-driven modeling and control techniques such as Gaussian process-based models, neural networks etc. The effectiveness of these techniques are demonstrated through several representative examples. In the sequel, the unique challenges and limitations of AI applications to propulsion systems are discussed, and future research directions are envisioned. Finally, this paper concludes by summarizing the key points and emphasizing the pivotal role of AI in advancing automotive propulsion systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101048"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023044","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":"Control theory in the era of intelligent systems: Applications, trends, and future directions","authors":"Maria Trigka,&nbsp;Elias Dritsas","doi":"10.1016/j.arcontrol.2026.101049","DOIUrl":"10.1016/j.arcontrol.2026.101049","url":null,"abstract":"<div><div>Control theory has established itself as a fundamental discipline for the analysis and design of dynamical systems. From its classical foundations, including proportional–integral–derivative (PID) control, state–space representations, and stability analysis, it has progressively expanded toward advanced, robust, and predictive control frameworks. The increasing complexity of modern systems, characterized by large-scale integration, data-driven operations, and stringent safety requirements, is reshaping their methodological foundations. However, despite extensive progress, the field of control remains fragmented, and an integrative viewpoint connecting the different topics is still lacking. This survey provides a unified cross-domain perspective that consolidates established principles and emerging intelligent paradigms. Specifically, it addresses this research gap by synthesizing classical control theory with contemporary artificial intelligence (AI)-driven and cyber–physical systems (CPS) methodologies within a mathematically consistent framework. To the best of our knowledge, no prior survey has provided a unified analytical framework that jointly treats all the aforementioned topics. A distinctive contribution is the mathematically rigorous treatment of nonlinear observer design through Laguerre polynomial approximations, positioned in direct comparison with extended Kalman filters (EKF), high-gain observers (HGO), and hybrid estimation methods. Beyond the classical scope, this survey addresses the convergence of control with AI, machine learning (ML), deep learning (DL), Internet of Things (IoT) infrastructures, digital twins, and quantum-edge computing, emphasizing their implications for scalability, adaptability, and resilience. The novelty of this review lies in articulating an integrative framework that bridges robust analytical tools with intelligent, data-driven architectures, highlighting both methodological coherence and cross-domain applicability. Key challenges include managing nonlinear complexity, ensuring robustness under uncertainty, embedding ethical and governance-aware mechanisms, and bridging the gap between theoretical innovation and practical deployment. Future trends indicate a shift towards reinforcement learning (RL)-augmented control, hybrid physics–AI architectures, distributed CPS architectures, and sustainability-driven designs. By combining historical depth with forward-looking integration, this survey serves as both a consolidated reference and a roadmap for the next generation of intelligent and resilient control systems.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101049"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037260","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 in-depth review of dual-arm robot learning methods with advances and challenges in variable compliance-aware control for uncertain tasks","authors":"Md Shofiqul Islam ,&nbsp;Zoran Najdovski ,&nbsp;Mohammad Anwar Hosen ,&nbsp;Van Thanh Huynh","doi":"10.1016/j.arcontrol.2026.101050","DOIUrl":"10.1016/j.arcontrol.2026.101050","url":null,"abstract":"<div><div>Advancements in haptic teleoperation enable robots to perform complex tasks with precision. Key progress lies in compliance control strategies that adjust robot stiffness based on task requirements. Despite notable achievements, challenges remain, including the need for robust algorithms capable of learning from sparse data, managing uncertainties, and ensuring stability during dynamic interactions. Furthermore, integrating compliance control with teleoperation systems introduces issues such as real-time feedback, latency, and the fidelity of haptic sensations. These challenges are covered in depth through our contributions in this review. This paper provides an in-depth analysis of the current state of learning variable compliance control in haptic teleoperation with limited demonstrations, outlining key challenges, research gaps, and future research directions. The initial section offers background information, followed by learning from demonstrations and methodologies for various compliance control techniques. The review covers recent research, including methodologies, algorithms, simulations, data generation processes, findings, and research gaps. This critical review of methods and experimental analysis is conducted on leading approaches, recent applications, advancements, particularly challenges, limitations, and future directions. We also conducted an analytical evaluation of different learning methods using robot task based simulation data derived from demonstrated tasks, training models show that imitation learning methods, particularly Imitation Behaviour Cloning (IBC) and LSTM models, achieved the highest accuracies (99.84%), outperforming reinforcement learning models that demonstrated lower accuracy and greater sensitivity to hyperparameter tuning. We also provided the recommendations of optimal approaches are provided for learning compliance control in haptic teleoperation.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101050"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023046","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":"Synthesis of model predictive control and reinforcement learning: Survey and classification","authors":"Rudolf Reiter ,&nbsp;Jasper Hoffmann ,&nbsp;Dirk Reinhardt ,&nbsp;Florian Messerer ,&nbsp;Katrin Baumgärtner ,&nbsp;Shambhuraj Sawant ,&nbsp;Joschka Bödecker ,&nbsp;Moritz Diehl ,&nbsp;Sebastien Gros","doi":"10.1016/j.arcontrol.2026.101045","DOIUrl":"10.1016/j.arcontrol.2026.101045","url":null,"abstract":"<div><div>model predictive control (MPC) and reinforcement learning (RL) are two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities, MPC and RL follow distinct paradigms that emerged from diverse communities and different requirements. Various technical discrepancies, particularly the role of an environment model as part of the algorithm, lead to methodologies with nearly complementary advantages. Due to their orthogonal benefits, research interest in combination methods has recently increased significantly, resulting in a large and growing set of complex ideas that leverage MPC and RL. This work illuminates the differences, similarities, and fundamentals that enable various combination algorithms and categorizes existing work accordingly. Particularly, we focus on the versatile actor–critic RL approach as a basis for our categorization and examine how the online optimization approach of MPC can be used to improve the overall closed-loop performance of a policy.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101045"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187395","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":"Bearing-based multi-agent formation control: A survey and taxonomy","authors":"Haifan Su ,&nbsp;Ziwen Yang ,&nbsp;Shanying Zhu ,&nbsp;Cailian Chen ,&nbsp;Xinping Guan ,&nbsp;Lihua Xie","doi":"10.1016/j.arcontrol.2025.101043","DOIUrl":"10.1016/j.arcontrol.2025.101043","url":null,"abstract":"<div><div>Formation control of multi-agent systems (MASs) using bearing measurements has attracted considerable attention in recent years. Compared to positions and displacements, bearing measurements contain the least information, which cost-effective sensors in many practical applications can easily measure. However, a critical challenge arises from the conflict between the achieved formation maneuvers and the incomplete observability of bearings, which lack distance information. Past surveys have primarily focused on methods based on static formation shapes, such as bearing rigidity, but lack a systematic overview of approaches through persistence of excitation (PE) variations in bearings over time. Thus, in this paper, we present a comprehensive survey and introduce a two-level taxonomy for bearing-based formation control methods of MASs. The taxonomy reveals that to achieve shape-invariant maneuvers or both shape-invariant and shape-varying maneuvers, the control methods need to steer the formation frameworks to satisfy rigidity (including bearing rigidity and angle rigidity) and PE (including agent-to-center PE, neighboring-pair PE, and entire-MAS PE) conditions, respectively. This guarantees the observability of the agents’ positions and hence the convergence of the formation tracking errors. We also review approaches that combine bearings with other measurements, organized by increasing flexibility of the achieved maneuvers. The survey also includes an overview of experimental platforms for bearing observation and discusses future challenges and directions in this field.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101043"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924514","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":"Advanced feedforward control techniques: Comprehensive review and a real-time industrial application","authors":"Hoang Minh Nguyen-Khac ,&nbsp;Fadi Alyoussef ,&nbsp;Alexander Bech ,&nbsp;Andrew Barr ,&nbsp;Steve Sapsford ,&nbsp;Chris Gerada ,&nbsp;Alasdair Cairns","doi":"10.1016/j.arcontrol.2025.101044","DOIUrl":"10.1016/j.arcontrol.2025.101044","url":null,"abstract":"<div><div>Feedforward control (FFC), often paired with feedback, is widely applied in industry to improve accuracy under transient conditions and reject disturbances. Despite this, academic attention has been limited, with few comprehensive reviews connecting algorithms to practical applications. This work bridges that gap by surveying classical and advanced FFC strategies, their advantages and drawbacks, and their implementation across diverse sectors.</div><div>Classical FFC improves tracking and disturbance rejection through methods such as inverse modelling and input shaping, but faces challenges from instability, intensive tuning, and model inaccuracies. Hybrid techniques, including model predictive and active disturbance rejection control, broaden capability yet remain constrained by complexity and sensitivity to nonlinearities.</div><div>Recent advances introduce look-ahead, adaptive, optimization-based, and data-driven methods. Preview and predictive designs enhance responsiveness but depend on accurate future estimation, while iterative and intelligent approaches reduce modelling requirements at the expense of stability and training demands. Adaptive and optimization-based controllers strengthen robustness but add computational burden and parameter sensitivity.</div><div>FFC has been deployed across process control, electrical drives, fuel cells, engines, robotics, motion systems, power electronics, and energy systems. These applications consistently show improved tracking and robustness, though adoption is limited by reliance on models, calibration effort, and a lack of hardware validation.</div><div>Signal acquisition and processing shape the stability and robustness of feedforward-augmented control architectures. A discussion is had reviewing recent advances in noise-aware estimation, delay-robust control, and filtered/learned inverse models to identify practical design strategies for maintaining reliable performance, focusing on medium and high frequency.</div><div>To demonstrate practical benefits, a feedforward-augmented PID (i.e., feedforward control added to a PID closed loop system) with preview and anti-windup enabled transient testing on absorbing dynamometers, traditionally restricted to steady-state use. Combined with reinforcement learning controllers, this approach reduced error, expanded applicability, and offered a cost-effective alternative to motored dynamometers.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101044"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883470","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":"Trajectory tracking control for autonomous vehicles: A systematic PRISMA review of models and strategies","authors":"Mohammed S. Albhaisi,&nbsp;Michal Prauzek,&nbsp;Tri Tran Huu Minh,&nbsp;Stepan Ozana,&nbsp;Jaromir Konecny","doi":"10.1016/j.arcontrol.2026.101047","DOIUrl":"10.1016/j.arcontrol.2026.101047","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) are poised to redefine future mobility by offering enhanced safety, energy efficiency, and intelligent adaptability. A fundamental component enabling this transformation is trajectory tracking control, which ensures precise path-following despite environmental uncertainties, dynamic road conditions, and sensor noise. This systematic review follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to analyze state-of-the-art trajectory tracking control strategies, categorizing them into traditional, adaptive, and learning-based methods. The study provides a comprehensive assessment of trajectory tracking models, highlighting their strengths, limitations, and applicability in real-world scenarios. Additionally, the review discusses key challenges, such as scalability, real-time adaptability, and the integration of multi-sensor data. By bridging theoretical advancements with practical implementations, this review contributes to the development of more robust, adaptive, and efficient trajectory tracking systems for autonomous mobility.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"61 ","pages":"Article 101047"},"PeriodicalIF":10.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023045","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":"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":"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":"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}