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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Hu, Changbin Huang, Haiyang Luo, Shanna Wang, Huiru Li, Xuecheng Lu, Heng North China University of Technology School of Electrical and Control Engineering Beijing China Beijing Kedong Electric Power Control System Co. Ltd Beijing China University of Duisburg-Essen Department of Automatic Control and Complex Systems Germany

ISBN: (纸本)9798350345094

To solve the power imbalance problem caused by the difference between the equivalent impedance of the inverter and the line impedance, and the power quality problem caused by the disturbance of load mutation, unbalanced load and nonlinear load, this paper proposes a closed-loop feedback dynamic compensation strategy based on the equivalent virtual impedance of the residual generator. Firstly, based on the parallel topology of inverters, a quasi-steady-state model is established on the dq axis. Based on the model, the power imbalance caused by the difference of output impedance and line impedance of different inverters and the power quality problems caused by load switching, unbalanced load, nonlinear load and other disturbances in parallel inverters are ***, a residual generator is established based on the inverter model. A virtual impedance equivalent controller Qvir(S) based on the residual generator is designed by the Luenberger observer principle. The disturbance information is fed back in real time through the feedback structure, and then the virtual impedance is adjusted to realize the power sharing of the inverter parallel system. Then, based on Euler parameterization and coprime factorization theory, a dynamic compensation architecture based on residual generator is designed. The transfer function of the dynamic compensation controller Qcom (S) is obtained by model matching to realize the steady-state operation of parallel inverters and the improvement of system power quality. Finally, the effectiveness of the proposed architecture is verified on the experimental platform. © 2023 IEEE.

关键词： Power quality

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Multi-channel quantum parameter estimation

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Science China(Information Sciences) 2022年第10期65卷 53-65页

作者： Liying BAO Bo QI Yabo WANG Daoyi DONG Rebing WU Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences School of Engineering and Information Technology University of New South Wales Department of Automation Tsinghua University

The aim of quantum metrology is to exploit quantum effects to improve the precision of parameter estimation beyond its classical limit. In this paper, we investigate the quantum parameter estimation problem with multiple channels. It is related but not limited to the following two important and practical quantum metrology problems:(i) Quantum enhanced metrology with control, whose aim is to improve the precision of quantum sensing by utilizing feedback or open-loop control;(ii) Practical quantum metrology where the underlying evolution of quantum probes may change from a unitary dynamics to an open system dynamics,owing to the inevitable decoherence during the quantum sensing operation. For various kinds of quantum multiple channels, the corresponding quantum channel Fisher information is derived. To demonstrate the results, some illustrative examples are given.

关键词： quantum metrology quantum parameter estimation multi-channel quantum Fisher information

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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Rintaro, Akashi Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (纸本)9798350389210

In recent years, there has been a considerable amount of research conducted on the topic of road damage detection using deep learning techniques, with the aim of supporting the safe driving of mobility vehicles. To date, convolutional neural networks and supervised learning with large datasets have been employed for road damage detection, demonstrating high classification accuracy. However, all images in the dataset must be manually labeled. Moreover, images cannot be classified into classes that are not included in the training dataset. The real world presents a vast array of damage types, and the unlabeled input images encompass both known and unknown classes. This renders the system unsuitable for use in providing safe driving support for mobility. Considering this background, Generalized Category Discovery (GCD) has been put forth as a potential solution, aiming to achieve accurate classification in a comprehensive setting where both known and unknown classes are present in unlabeled data. The purpose of this study is to propose a novel model for GCD with the aim of providing safe driving assistance for mobility. The model is based on the pre-trained MobileViT. To validate its performance, this paper examines its performance on the general CIFAR-10 and CIFAR-100 datasets and on a road damage-specific dataset, and discusses the challenges encountered. © 2024 IEEE.

关键词： Convolutional neural networks

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RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

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IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. © 1999-2012 IEEE.

关键词： Robot vision

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Uncertainty-aware Monocular 3D Object Detection for Safe Driving Assistance

Uncertainty-aware Monocular 3D Object Detection for Safe Dri...

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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Ida, Masahiko Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Fukuoka Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (纸本)9798350389210

Safety is of utmost importance when utilizing mobile vehicles. The introduction of safe driver assistance and automated driving systems will not only solve this problem, but also reduce road accidents and make driving smoother. To achieve these goals, 3D object detection is an essential technology and various methods have been proposed. Among these, monocular 3D object detection is an important technique and it is very difficult to identify the dimensions, direction and position of an object from a monocular image. Previous methods have predicted the dimensions directly from monocular images, resulting in poor accuracy of the dimensions in the depth direction. However, this paper proposes an indirect method for estimating the dimensions in the depth direction by predicting the volume of 3D bounding box. Detection accuracy for distant and partially visible objects is improved by using a loss function that uses uncertainty to account for the variability in predictions that occurs during learning. The proposed method has received an excellent evaluation on KITTI dataset. © 2024 IEEE.

关键词： 3D modeling

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Direct Shaping of Minimum and Maximum Singular Values: An H−/H∞ Synthesis Approach for Fault Detection Filters

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arXiv 2023年第2期56卷 11311-11316页

作者： Classens, Koen Heemels, W.P.M.H. Oomen, Tom Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Dept. 3mE Delft University of Technology Netherlands

The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances. The aim of this paper is to develop an approach to directly shape these sensitivities, expressed in terms of minimum and maximum singular values. The developed method offers an alternative solution to the H−/H∞ synthesis problem, building upon traditional multiobjective synthesis results. The result is an optimal filter synthesized via iterative convex optimization and the approach is particularly useful for fault diagnosis as illustrated by a numerical example. © 2023, CC BY-NC-ND.

关键词： Linear matrix inequalities

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Usage of Continuous Medical Monitoring in Human-Machine systems to Improve Their Reliability and Emergency Safety 16

Usage of Continuous Medical Monitoring in Human-Machine Syst...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Turovskiy, Yaroslav Surovtsev, Alexander V.A. Trapeznikov Institute of Control Science of Ras Laboratory of Control Based on Incomplete Data Moscow Russia Voronezh State University Department of Digital Technologies Voronezh Russia Voronezh State University of Engineering Technologies Department of Information and Control Systems Voronezh Russia

ISBN: (纸本)9798350337907

A method of conducting continuous medical monitoring of human operators of human-machine systems is proposed. This method assumes building a decision support system that selects the control mode of the technological process. This paper has simulation modeling results of the technological process conditions changing from a potentially faulty state to a proper state. © 2023 IEEE.

关键词： Decision support systems

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Deep Reinforcement Learning Method for control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Liu, Xingyu Apriaskar, Esa Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang50229 Indonesia

ISBN: (纸本)9798350368031

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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Vehicle string using spacing strategies for cooperative adaptive cruise control system

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2021年第1期83卷 91-106页

作者： Luu, Duc Lich Lupu, Ciprian Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

The Co-operative Adaptive Cruise control (CACC) is one of the key subsystems of Connected and Automated Vehicles (CAVs) that have the potential to increase the road traffic capacity and improve traffic safety with shorter inter-vehicle spacing between vehicles. The spacing strategies are the core of all CACC control algorithms which are chosen based on design purposes for the system. This work presents a comparative research to evaluate the effectiveness of two spacing strategies, consisting of the constant spacing strategy and, constant time headway strategy, which is suitable for information flow topologies. The main focus of this research analyzes the benefits of operating mechanisms as well as the characteristics of each spacing strategy. The results of the simulations using software Matlab/Simulink have demonstrated the performances and applicability of each spacing policy. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Adaptive cruise control

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