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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Iterative Learning Hybrid Robust Predictive control for Multi-Phase Batch Processes with Asynchronous Switching via a Lyapunov-Razumikhin Approach

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 345-358页

作者： Li, Hui Wang, Shiqi Shi, Huiyuan Li, Ping Su, Chengli School of Electronics and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Information Engineering Liaodong University Dandong118001 China

For a class of the industrial cyber-physical system, i.e., multi-phase batch processes with small time-delays and asynchronous switching, this study develops a two-dimensional iterative learning hybrid robust predictive control method. Compared with the previous Lyapunov-Krasovsky function-based method, stability conditions given by the Lyapunov-Razumikhin function-based method is less conservative intensive in dealing with small time-delays. Not only that, compared to the traditional iterative learning method, the rolling-optimized gains of the control law make it possible for the designed controller to have fewer learning cycles, and the control performance is even better. Meanwhile, the minimum and the maximum dwelling times are obtained in conjunction with the exponential stability analysis of the system. Since the obtained dwelling times, an advanced switching signal is provided to avoid asynchronous switching situations. The simulation results for the injection molding process are finally utilized to illustrate the effectiveness and feasibility of the developed approach. © 2023 The Authors.

关键词： Time delay

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Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1666-1675页

作者： Xinran Zhang Chao Lu Ying Wang Qiantu Ruan Hongbo Ye Weihong Wang the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China. the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of Technology Beijing Forestry UniversityBeijing 100083China the State Grid Shanghai Electric Power Company Shanghai 200122China

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and *** practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of *** this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is *** load model structure and the combined optimization and regression method to identify the parameters are first ***,the definition and analysis method of identifiability are *** CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate ***,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power *** results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed ***,the proposed PII can provide guidance in data length selection in practical load model identification situations.

关键词： Confidential interval Load modeling parameter estimation practical identifiability

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Power-Hardware-in-the-Loop Motor Emulator under Four-Quadrant Operation Condition 10

Power-Hardware-in-the-Loop Motor Emulator under Four-Quadran...

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10th IEEE International Power Electronics and Motion control Conference, IPEMC 2024 ECCE Asia

作者： Zhang, Gengchen Guo, Hong Xu, Jinquan Wang, Gaoxiang Zhang, Li Jin, Wenbo Zhao, Xiaopeng Liu, Ying Beihang University School of Automation Science and Electrical Engineering Bejing China Southwest China Research Institute of Electronic Equipment Chengdu China Xian Flight Automatic Control Research Institute Xi'an China National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9798350351330

In order to solve the problems of long cycle and the difficulty of test, this paper proposes a method of power-in-the-loop motor simulator. Firstly, the topology of the motor simulator is proposed, composed of an interface filtering unit, a motor simulation unit and an energy management unit. Secondly, a discretization method for permanent magnet synchronous motor(PMSM) is designed for DSP control. Thirdly, this paper proposes a method combining quasi-proportional resonance control(QPR) and direct current control to ensure the control accuracy. Finally, the motor simulator is verified by experiments. © 2024 IEEE.

关键词： Permanent magnets

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Unified Addressing Algorithm for Tasks in Cloud control Systems 41

Unified Addressing Algorithm for Tasks in Cloud Control Syst...

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第41届中国控制会议

作者： Guan Wang Yufeng Zhan Yuanqing Xia Liping Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information Science and Engineering University of ZaoZhuang

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of cloud computing and control theory,a new paradigm of networked control systems named cloud control systems has been proposed to satisfy the requirements of large-scale and complex *** is critical to manage large-scale tasks with personalized needs on the large number of heterogeneous ***,current TCP/IPbased addressing approach does not consider the runtime constraints of tasks,resulting highly concurrent service search,and communication between tasks will become the system *** this paper,we propose a task property encoding method which takes multi-dimensional properties into *** on the proposed encoding method,we design a uniform addressing method for tasks in the ***,we experimentally demonstrate the effectiveness of task property encoding,and the unified addressing method for tasks is more effective than other methods in the case of high task concurrency.

关键词： Cloud control systems address encoding cloud-edge-device cooperation

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A model reduction approach for discrete-time linear time-variant systems with delayed inputs

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science China(Information sciences) 2024年第4期67卷 197-217页

作者： Ai-Guo WU Guang-Ren DUAN Yu WANG Jie ZHANG School of Mechanical Engineering and Automation Harbin Institute of Technology(Shenzhen) HIT Wuhu Robot Technology Research Institute Center for Control Science and Technology Southern University of Science and Technology

A model reduction approach is presented for discrete-time linear time-variant input-delayed systems. According to this proposed approach, a dynamical variable is constructed by taking advantage of the current state and historical information of input. It is revealed that the behavior of this dynamical variable is governed by a discrete-time linear delay-free system. It is worth noting that the presented variable transformation does not require the system matrix to be invertible. Based on the reduced delay-free models,stabilizing control laws can be easily obtained for the original delayed system. For the case with a single input delay, the constructed variable is an exact prediction for the future state, and thus the stabilizing control law could be designed by replacing the future state with its prediction. Finally, three discrete-time periodic systems with delayed input are employed to illustrate how to utilize the presented model reduction approaches.

关键词： reduction approach predictor-based feedback time-varying systems delayed input periodic system

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System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:the optimality of attack strategy

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control Theory and Technology 2022年第1期20卷 127-138页

作者： Jin Guo Xuebin Wang Yanling Zhang Wenchao Xue Yanlong Zhao School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the *** the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering *** packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is *** the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with *** explicit expression of the optimal attack strategy is *** examples are presented to verify the effectiveness of the main conclusions.

关键词： System identification Binary-valued observations Denial-of-service attacks Data tampering attacks

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Finite-Time Stabilization of Markov Jump Systems via a Probabilistic Event-Triggered Strategy

Finite-Time Stabilization of Markov Jump Systems via a Proba...

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2022 Chinese automation Congress, CAC 2022

作者： Chen, Haiyang Zong, Guangdeng Gao, Fangzheng Nanjing Institute of Technology School of Automation Nanjing China Tiangong University School of Control Science and Engineering Tianjin China

ISBN: (纸本)9781665465335

This paper investigates the finite-time stabilization of a class of Markov jump systems (MJSs). To regulate the limited network resource, a probabilistic event-triggered mechanism (PETM) is initially proposed. Based on the output of PETM, mode-dependent state feedback controllers are designed to obtain the closed-loop system (CLS). A set of novel Lyapunov functional are created to analyze the finite-time stability of the CLS. Afterward, the design algorithm is established to calculate the controller gains. Finally, simulations are provided to verify the proposed results. © 2022 IEEE.

关键词： Closed loop systems

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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