This article provides an overview of the design of purely reactive nonlinear feedback two-dimensional Cruise Controllers (CCs) for CAVs relying on two combined principles: (a) Lane-free traffic, whereby vehicles are not bound to fixed traffic lanes; and (b) Vehicle nudging, whereby vehicles influence other vehicles in front or on the sides of them. The design of the two-dimensional CCs is based on a control Lyapunov methodology, with the considered system being subject to various state constraints, and guarantees a number of desired features, including collision avoidance and road boundary respect. Additionally, the emerging macroscopic traffic flow models are derived, and direct relations are established between selectable CC features and the resulting macroscopic traffic flow characteristics. This allows the active design of efficient traffic flow with desired properties, i.e., the construction of artificial traffic fluids.
{"title":"From road congestion to vehicle-control enabled artificial traffic fluids","authors":"Iasson Karafyllis , Dionysios Theodosis , Markos Papageorgiou , Miroslav Krstic","doi":"10.1016/j.arcontrol.2025.100989","DOIUrl":"10.1016/j.arcontrol.2025.100989","url":null,"abstract":"<div><div>This article provides an overview of the design of purely reactive nonlinear feedback two-dimensional Cruise Controllers (CCs) for CAVs relying on two combined principles: (a) Lane-free traffic, whereby vehicles are not bound to fixed traffic lanes; and (b) Vehicle nudging, whereby vehicles influence other vehicles in front or on the sides of them. The design of the two-dimensional CCs is based on a control Lyapunov methodology, with the considered system being subject to various state constraints, and guarantees a number of desired features, including collision avoidance and road boundary respect. Additionally, the emerging macroscopic traffic flow models are derived, and direct relations are established between selectable CC features and the resulting macroscopic traffic flow characteristics. This allows the active design of efficient traffic flow with desired properties, i.e., the construction of artificial traffic fluids.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"59 ","pages":"Article 100989"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769186","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 filtering identification idea is an effective tool for handling the parameter identification of systems with colored noise. The hierarchical identification principle is an effective approach for addressing the identification of complex systems. For multivariable equation-error autoregressive moving-average-like (M-EEARMA-like) models with colored noise, which are also called multivariable controlled autoregressive autoregressive moving-average-like (M-CARARMA-like) models, this paper investigates and proposes the filtered hierarchical generalized extended stochastic gradient identification method, the filtered hierarchical multi-innovation generalized extended stochastic gradient identification method, the filtered hierarchical generalized extended recursive gradient identification method, the filtered hierarchical multi-innovation generalized extended recursive gradient identification method, the filtered hierarchical generalized extended least squares identification method, and the filtered hierarchical multi-innovation generalized extended least squares identification method by using the filtering identification idea and the hierarchical identification principle from available input–output data. These filtered hierarchical generalized extended identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noise.
{"title":"Hierarchical generalized extended parameter identification for multivariable equation-error ARMA-like systems by using the filtering identification idea","authors":"Feng Ding , Ling Xu , Xiao Zhang , Huan Xu , Yihong Zhou , Xiaoli Luan","doi":"10.1016/j.arcontrol.2025.100993","DOIUrl":"10.1016/j.arcontrol.2025.100993","url":null,"abstract":"<div><div>The filtering identification idea is an effective tool for handling the parameter identification of systems with colored noise. The hierarchical identification principle is an effective approach for addressing the identification of complex systems. For multivariable equation-error autoregressive moving-average-like (M-EEARMA-like) models with colored noise, which are also called multivariable controlled autoregressive autoregressive moving-average-like (M-CARARMA-like) models, this paper investigates and proposes the filtered hierarchical generalized extended stochastic gradient identification method, the filtered hierarchical multi-innovation generalized extended stochastic gradient identification method, the filtered hierarchical generalized extended recursive gradient identification method, the filtered hierarchical multi-innovation generalized extended recursive gradient identification method, the filtered hierarchical generalized extended least squares identification method, and the filtered hierarchical multi-innovation generalized extended least squares identification method by using the filtering identification idea and the hierarchical identification principle from available input–output data. These filtered hierarchical generalized extended identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noise.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 100993"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365942","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.2025.100995
Rodrigo Negri de Azeredo , Mohamed Hajjem , Lara Thomas , Stéphane Victor , Patrick Lanusse , Pierre Melchior
A mobile antenna system requires precise control that combines good trajectory tracking and disturbance rejection. The system has several operating scenarios, demanding a robust control design that ensures good performance and stability for a set of uncertain models. Moreover, ground stations are mainly outdoor operating systems, and they are subject to wind gusts that impact the accuracy of the position control. This paper presents a study using predictive control and robust controllers (CRONE and strategies) to assess the application feasibility of these methodologies in tracking antenna systems. The control system design of both CRONE and strategies are carried out in frequency domain. Time domain analysis demonstrates that both robust controllers yield very similar results, ensuring excellent stability and outstanding performances in tracking trajectory operation. The choice of control strategy ultimately depends on the designer’s expertise, software/hardware requirements, and the complexity of the system model, with an advantage of the CRONE controller in terms of robustness and practical implementation. Simulation results are validated on tracking satellite trajectories by comparing MPC, CRONE and controls with various robustness tests.
{"title":"Predictive and robust control strategies applied to ground antenna systems","authors":"Rodrigo Negri de Azeredo , Mohamed Hajjem , Lara Thomas , Stéphane Victor , Patrick Lanusse , Pierre Melchior","doi":"10.1016/j.arcontrol.2025.100995","DOIUrl":"10.1016/j.arcontrol.2025.100995","url":null,"abstract":"<div><div>A mobile antenna system requires precise control that combines good trajectory tracking and disturbance rejection. The system has several operating scenarios, demanding a robust control design that ensures good performance and stability for a set of uncertain models. Moreover, ground stations are mainly outdoor operating systems, and they are subject to wind gusts that impact the accuracy of the position control. This paper presents a study using predictive control and robust controllers (CRONE and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> strategies) to assess the application feasibility of these methodologies in tracking antenna systems. The control system design of both CRONE and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> strategies are carried out in frequency domain. Time domain analysis demonstrates that both robust controllers yield very similar results, ensuring excellent stability and outstanding performances in tracking trajectory operation. The choice of control strategy ultimately depends on the designer’s expertise, software/hardware requirements, and the complexity of the system model, with an advantage of the CRONE controller in terms of robustness and practical implementation. Simulation results are validated on tracking satellite trajectories by comparing MPC, CRONE and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> controls with various robustness tests.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 100995"},"PeriodicalIF":7.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312880","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.2025.101021
Ruhi Sarikaya
During the past decade, there has been remarkable progress in Artificial Intelligence (AI). More recently, the emergence of Generative AI was an inflection point in cognitive pattern understanding and generation across multiple modalities including speech, text, imagery, and vision, where AI systems are increasingly matching or surpassing human performance on a growing array of cognitive tasks. These models have been seamlessly integrated into numerous applications and products, reaching hundreds of millions of users. As a result, discussions regarding the achievement of Artificial General Intelligence (AGI) have shifted from theoretical speculation to a plausible near to mid-term objective. In this paper, we present a comprehensive review of the evolution of AI from its inception to the present day. We then examine how advances in computational infrastructure, algorithms, and large-scale modeling are converging to drive the generative AI revolution and shaping the trajectory toward AGI, potentially within the next 5-to-10 years. Specifically, we analyze recent progress in compute capabilities, learning algorithms, and model architectures across a broad spectrum of cognitive tasks. We also share our perspective on the key challenges that remain to be solved, and discuss the critical risks that must be addressed to ensure the safe and beneficial development of AI systems that may eventually exceed human-level performance in perception, reasoning, and general cognition.
{"title":"Path to Artificial General Intelligence: Past, present, and future","authors":"Ruhi Sarikaya","doi":"10.1016/j.arcontrol.2025.101021","DOIUrl":"10.1016/j.arcontrol.2025.101021","url":null,"abstract":"<div><div>During the past decade, there has been remarkable progress in Artificial Intelligence (AI). More recently, the emergence of Generative AI was an inflection point in cognitive pattern understanding and generation across multiple modalities including speech, text, imagery, and vision, where AI systems are increasingly matching or surpassing human performance on a growing array of cognitive tasks. These models have been seamlessly integrated into numerous applications and products, reaching hundreds of millions of users. As a result, discussions regarding the achievement of Artificial General Intelligence (AGI) have shifted from theoretical speculation to a plausible near to mid-term objective. In this paper, we present a comprehensive review of the evolution of AI from its inception to the present day. We then examine how advances in computational infrastructure, algorithms, and large-scale modeling are converging to drive the generative AI revolution and shaping the trajectory toward AGI, potentially within the next 5-to-10 years. Specifically, we analyze recent progress in compute capabilities, learning algorithms, and model architectures across a broad spectrum of cognitive tasks. We also share our perspective on the key challenges that remain to be solved, and discuss the critical risks that must be addressed to ensure the safe and beneficial development of AI systems that may eventually exceed human-level performance in perception, reasoning, and general cognition.</div></div>","PeriodicalId":50750,"journal":{"name":"Annual Reviews in Control","volume":"60 ","pages":"Article 101021"},"PeriodicalIF":10.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157067","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}
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