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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for intelligent Vehicles

IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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A Novel Gas Classification Method Based on The Improved GasNet 43

A Novel Gas Classification Method Based on The Improved GasN...

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43rd Chinese Control Conference, CCC 2024

作者： Qin, Xiaolin Cheng, Lei Tao, Xiang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan430081 China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

ISBN: (纸本)9789887581581

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas samples. The experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Contrastive Learning

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Smart Manufacturing Integration of Heterogeneous information systems via OPC UA. A Case Study

Smart Manufacturing Integration of Heterogeneous Information...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Vasilev, Plamen Boneva, Ani Ivanova, Veronika Tsvetanov, Tsetso University of Chemical Technology and Metallurgy Department of Industrial Automation Sofia Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Communication and Computer Systems Sofia Bulgaria Institute of Robotics Bulgaria Academy of Sciences Department of Robotized and Mechatronics Intelligent Systems Sofia Bulgaria

ISBN: (纸本)9798350353907

Modern automation, digitalization, virtualization, and intelligent production impose new criteria and approaches to process management. The operation and performance of any system and device depend on how it is managed. The article describes Smart Manufacturing Integration of Heterogeneous information systems via OPC UA. The main goal is the development of heterogeneous information systems with real applications, with which reliable and up-to-date information can be exchanged between systems of different hierarchical levels. The goal was achieved with the following steps i) building the research infrastructure ii) analyzing the information compatibility and challenges in integrating systems from different hierarchical levels, and iii) implementing the integration by means of the OPC-UA standard communication protocol. The setup has been tested under laboratory conditions. The obtained results are shown. © 2024 IEEE.

关键词： Smart manufacturing

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Integration of Partially Prior Physics and Data for Longitudinal Vehicle Dynamics Restoration 24

Integration of Partially Prior Physics and Data for Longitud...

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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Yongsheng Xu, Bingrong Chu, Duanfeng Ding, Naikan Yin, Jianhua School of Information Engineering Wuhan Univ. of Technology Wuhan China School of Automation Wuhan Univ. of Technology Wuhan China Intelligent Transportation Systems Research Center Wuhan Univ. of Technology Wuhan China Chongqing Research Institute Wuhan Univ. of Technology Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Vehicle dynamics model is critical for autonomous vehicles since it provides certain rules of motion in response to the surrounding rapidly changing environment. However, current pure physics-based models and data-driven approaches are set against each other in constructing the dynamics model. In this work, we propose a vehicle dynamics restoration approach by integrating physics principles and data based on physics-informed machine learning. The vehicle dynamics model parameters are estimated by combining data and limited prior physical knowledge. The results of a case study demonstrate that (1) the proposed methods can accurately identify the dynamics model parameters and restore the vehicle dynamics model and (2) the neural network with physical knowledge can accurately predict the state of vehicle. The proposed method has the potential to be applied in quantifying the dynamics model uncertainty due to parameter uncertainty, and stochastic motion planning and control due to uncertainty, which will be further investigated in the future. © ASCE.

关键词： Stochastic programming

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A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1792-1800页

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei the Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the School of Electronic and Information Engineering Tongji UniversityShanghai 200082 the Peng Cheng Laboratory Shenzhen 518055China

This paper develops a fully distributed hybrid control framework for distributed constrained optimization *** individual cost functions are non-differentiable and *** on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization *** proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump *** the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different *** simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关键词： Distributed algorithm hybrid framework multiagent network non-differentiable optimization

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Mainline Lane Management Strategies for Mixed Traffic with Connected Autonomous Vehicles on Urban Expressway Merging Areas 27

Mainline Lane Management Strategies for Mixed Traffic with C...

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27th IEEE International Conference on intelligent Transportation systems, ITSC 2024

作者： Ran, Changhao Wu, Chaozhong Zhang, Hui Ding, Naikan Tian, Kai Wu, Huiyu Huang, Zhen Wang, Xu Wuhan University of Technology Intelligent Transportation Systems Research Center Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan China School of Automation Wuhan University of Technology Wuhan430070 China School of Qilu Transportation Shandong University Jinan250002 China

ISBN: (纸本)9798331505929

With the rapid advancement of autonomous driving technology, the impact of connected autonomous vehicles (CAVs) on traffic safety and stability has garnered significant attention. This study aims to evaluate different mainline lane management strategies and CAV penetration rates on mixed traffic safety at urban expressway merging areas. Utilizing measured vehicle trajectory data from an urban expressway merging area, a deep learning-based mandatory lane changing model is established for human-driven vehicles' (HDVs) merging behavior. Considering the overall effect of CAV dedicated lanes or priority lanes layout and CAV penetration rates on mixed traffic distribution on the mainline, a series of simulations are conducted in SUMO with secondary development. Compared to various mainline lane management strategies, designating the outer mainline lane as the CAV dedicated lane results in fewer conflicts between vehicles. In this scenario, as the CAV penetration rate increases from 20% to 80%, the number of conflicts decreases by 3.47% to 9.81 % compared to no lane management strategy. Implementing a proper mainline lane management strategy can enhance the safety of mixed traffic at urban expressway merging areas. © 2024 IEEE.

关键词： Highway administration

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Enhanced Multi-Agent Proximal Policy Optimization for Multi-UAV Target Offensive-Defensive Decision 42

Enhanced Multi-Agent Proximal Policy Optimization for Multi-...

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42nd Chinese Control Conference, CCC 2023

作者： Zheng, Yifan Xin, Bin Jiao, Keming Zhao, Zhixin Wang, Yuyang Zhao, Yunming School of Electronics and Information Engineering Tongji University Shanghai201804 China School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789887581543

Autonomous collaborative decision-making is the key technology to achieve large-scale unmanned combat. Focus on the problem of multiple unmanned aerial vehicles' cooperative decision in target offensive and defensive combat, a multi-agent deep reinforcement learning (MADRL) based decision framework is proposed in this paper. Firstly, the simulation environment with a high-fidelity fixed-wing motion model is built. Secondly, to address the issue of high-dimension state space and credit assignment under a multi-agent environment, an enhanced multi-agent proximal policy optimization with mean-field counterfactual advantage (MAPPO - MFCOA) is proposed. Finally, the results of simulation experiments will verify the performance of the proposed approach. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Reinforcement learning

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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The System of Coordinated Operation of Two Underwater Vehicles in a Complex Environment

The System of Coordinated Operation of Two Underwater Vehicl...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Filaretov, Vladimir Katsurin, Alexey Institute of Automation and Control Processes Feb Ras Vladivostok State University Laboratory of Robotic Systems Laboratory of Automation and Robotics Vladivostok Russia Far Eastern Federal University M.D. Ageev Institute for Marine Technology Problems Feb Ras Laboratory of Intelligent Information Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia

ISBN: (纸本)9798350382457

The paper discusses methods of coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi-link manipulator and a vision system, and the second - auxiliary - only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control, and in the mode of automatic execution of operations with various objects. At the same time, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwater vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. It is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The results of experimental studies have confirmed the operability of the proposed methods. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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TJ-FlyingFish:An Unmanned Morphable Aerial-Aquatic Vehicle System

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无人系统（英文） 2024年第2期12卷 409-428页

作者： Xuchen Liu Minghao Dou Ruixin Yan Dongyue Huang Songqun Gao Biao Wang Jinqiang Cui Qinyuan Ren Lihua Dou Zhi Gao Jie Chen Ben M.Chen Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghaiP.R.China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongP.R.China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongP.R.China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingJiangsuP.R.China Peng Cheng Laboratory ShenzhenGuangdongP.R.China College of Control Science and Engineering Zhejiang UniversityHangzhouZhejiangP.R.China School of Automation Beijing Institute of TechnologyBeijingP.R.China School of Remote Sensing and Information Engineering Wuhan UniversityWuhanHubeiP.R.China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghaiP.R.China

In this paper,we present in this work a fairly complete process for developing an unmanned aerial-aquatic vehicle system,TJ-FlyingFish,which includes an innovative design methodology of the aerial-aquatic platform and the cross-medium localization,dynamics modeling,and flight control *** development faces the challenge how to manipulate locomotion effectively in both water and air which presents substantial differences in fluid ***,there are difficulties in perception and navigation because of the discontinuity of *** cope with these challenges,we designed an innovative unmanned aerial-aquatic vehicle with an optimized dual-speed and tilting propulsion *** rotors/propellers operate in different ranges of rotating speed in the two different mediums,providing sufficient thrust and ensuring output ***,thrust vectoring is achieved by rotating each propulsion unit around its mounted arm,facilitating agile underwater *** key component of our approach is a sophisticated multi-sensor-based cross-medium localization system that combines SLAM,sensor synchronization,and data capture mechanisms,enabling seamless transitions between aerial and aquatic environments,and supporting autonomous *** results are fully validated through actual flight experiments.

关键词： Unmanned aerial-vehicles cross-medium locomotion cross-medium localization

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