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IEEE International Conference on Networking, Sensing and control

作者： Wenxiao Yan Jiayu Wang Liqun Fan Qiusheng Liu Qingdong Jia Yihan Wang Hongjie Liu School of Automation and Intelligence Beijing Jiaotong University Beijing China Shenzhen Metro Group Co. Ltd. Shenzhen China Shenzhen Municipal Design and Research Institute Co. Ltd. Shenzhen China Traffic control Technology Co. Ltd. Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

With the rapid growth of transportation passenger flow in recent years, the research direction of Virtual Coupling (V C) in railways has become hot. V C increases capacity by tracking shorter distances. The traditional train emergency braking (EB) model can no longer meet the high operating efficiency requirements of VC. VC urgently needs a more efficient safety EB model. Therefore, to improve efficiency, a measurement of the EB model's rationality is defined considering safety and efficiency. Based on the defined model measurement, an EB model optimization method and a new EB model are proposed. The new model is closer to the actual braking process of the train and ensures safety from the distance level. That is, the braking trajectory of the train calculated by the new model is closer to the actual braking trajectory of the train, and the two braking trajectories satisfy the specific safety constraints. Finally, the safety and efficiency of the new model were verified through simulation experiments. The new model can avoid train collisions during the entire EB process. Experimental results show that compared with the traditional old EB model of VC based on space-time interval [1], the new model's minimum safe distance (MSD) between two VC train units can be reduced by 39%.

关键词： Couplings Analytical models Optimization methods Transportation Process control Safety Trajectory Sensors Brakes

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Design of Improved Linear Active Disturbance Rejection control for Non-Minimum Phase Systems 14

Design of Improved Linear Active Disturbance Rejection Contr...

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14th International Conference on Power and Energy Systems, ICPES 2024

作者： Lin, Xiang Wu, Maoqian Dong, Lijiang Liu, Wei Qian, Baiyun Yang, Chaojie Electric Power Research Institute State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Uygur Autonomous Region Urumqi China State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Uygur Autonomous Region Urumqi China Urumqi Electric Power Construction and Commissioning Institute Xinjiang Xinneng Group Co. Ltd. Xinjiang Uygur Autonomous Region Urumqi China School of Control and computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391329

Non-minimum phase systems, which are common in power generation due to their inherent instability, pose significant control challenges. To address this, an Improved Linear Active Disturbance Rejection control (ILADRC) is proposed. This method involves decomposing the system model into minimum phase and non-minimum phase components, designing a feedforward compensator based on the minimum phase component, and developing a non-minimum phase Smith predictor compensator for the non-minimum phase part. Additionally, a rule for simplified parameter tuning of the ILADRC is introduced. The effectiveness of the proposed ILADRC was demonstrated through experiments using steam valve regulation in a turbine as a case study, compared with PID and LADRC. The results show that the proposed ILADRC outperforms existing methods in set-point tracking and disturbance rejection. Monte Carlo robustness tests further confirm the robustness advantage of the ILADRC, highlighting its significant potential for practical engineering applications. © 2024 IEEE.

关键词： Disturbance rejection

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FGF3D: A Point Cloud 3D Object Detection Algorithm Based on Fine-Grained Feature Fusion

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Li, Bing Xu, Yunheng Wang, Shuoheng Wang, Xinwen Chen, Jie Li, Xinde Li, Yingsong Huang, ZhiXiang Anhui University School of Electronic and Information Engineering Hefei230601 China Southeast University School of Automation Nanjing210096 China Nanjing Center for Applied Mathematics Nanjing211135 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China 38th Research Institute of China Electronics Technology Group Corporation Hefei230088 China Southeast University Key Laboratory of Measurement and Control of CSE School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

In the existing two-stage point cloud 3D object detection task, there are problems such as the lack of fine-grained and insufficient sparsity of point cloud features extracted by the backbone network, the lack of spatial-channel fine-grained fusion of bird-eye-view (BEV) features, and the lack of multi-dimensional representation of ROI pooling features. We innovatively propose a new two-stage algorithm based on fine-grained feature fusion, called FGF3D. We construct a sparsegrained aggregation network (SGANet) to elevate the representation of point cloud features. Furthermore, we innovatively propose a spatial-channel fine-grained fusion module (SCFFM) to enrich BEV features and efficiently extract fine-grained features. Further, we propose a global-local feature pooling fusion module (GLFP), which effectively fuses local detail object features and global features, enhances multi-dimensional representations, and facilitates the refined regression of proposal boxes. Without bells and whistles, FGF3D attains excellent performance on two autonomous driving benchmarks, with detection accuracies of up to 64.10% on KITTI, 57.82% on ONCE. © 2025 IEEE. All rights reserved.

关键词： Object detection

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Small Object Detection with Improved CenterNet 2

Small Object Detection with Improved CenterNet

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2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Xu, Jie Ye, Yalan Liu, Ziqi Meng, Fanjun Zhang, Dashun Sun, Ming School of Automation Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China The Fifty-fifth Research Institute of China North Industries Group Corporation Limited China

ISBN: (纸本)9781665428767

Detection of small objects is a notorious challenge due to their properties of low resolution and less information. Most existing works aim for general object detection while ignoring the impact of the object size, resulting in high missed detection rate of small objects, making it difficult to apply object detection in real scenarios such as autonomous driving. The implementation of an efficient detection algorithm for small objects is thus worth discussing. In this paper, we propose a small object detection framework with improved CenterNet. Specifically, the proposed framework consists of four parts. The super-resolution (SR) subnetwork enhances the information contained in small objects by generating an SR version of the original image. The future pyramid subnetwork fuses different levels of features thereby retaining semantic information of small objects. The center localization header and the size regression header are combined to detect objects via prediction of object center and object size. In this way, the performance of object detection is improved especially on small objects. We have conducted comprehensive experiments on MS COCO benchmark, and the experimental results have proved the effectiveness of our framework, especially in the detection of small objects better than existing methods. © 2021 IEEE.

关键词： Object detection

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Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

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IFAC-PapersOnLine 2023年第2期56卷 4896-4903页

作者： Szilárd H. Tóth Ádám Bárdos Zsolt J. Viharos Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering and Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Centre of Excellence in Production Informatics and Control Eötvös Loránd Research Network (ELKH) Centre of Excellence of the Hungarian Academy of Sciences (MTA) Budapest Hungary Department of Management and Business Law Faculty of Economics John von Neumann University Kecskemét Hungary

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

关键词： Motion control Autonomous Vehicles Vehicle Dynamics control Reinforcement Learning Vehicle Drifting Handling Limits

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Unobtrusive and Non-Invasive Human Activity Recognition using Kinect Sensor 2

Unobtrusive and Non-Invasive Human Activity Recognition usin...

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Indo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020

作者： Nehra, Shalini Raheja, Jagdish Lal Control Automation Group Cyber Physical System Area CSIR-Central Electronics Engineering Research Institute Pilani India

ISBN: (纸本)9781728149998

The main objective of this paper is to present a depth imaging human activity recognition system that is basically used to monitor and detect normal activities of the person in the indoor environment. The Proposed system runs in real-time and is capable of detecting moving falls, sitting fall accurately and robustly without taking into account any false positive activities. Human activity recognition with high accuracy is a huge challenge in the research world. Fall detection is an important technology in health care and elderly person activity monitoring. The proposed method is feasibility and workability are illustrated throughout the experimental result that shows the perfect human tracking and activity detection. System works well with low lighting conditions;lighting does not affect the accuracy of detecting activities. Moreover, the proposed method is able to avoid false positive as: lying down, retrieve something from the floor. © 2020 IEEE.

关键词： Feature extraction

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Fast accurate position control of light-responsive liquid crystal elastomer actuator

Fast accurate position control of light-responsive liquid cr...

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Asian control Conference

作者： Xinyu Nie Jundong Wu Yawu Wang Chun-Yi Su Daiki Sato Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan The School of Engineering Tokyo University of Technology Tokyo Japan

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Light-responsive liquid crystal elastomers (LCEs) are promising for soft robot actuators due to their ability to respond to light stimuli and produce deformation. In order to realize their application in soft robots, fast and accurate position control is essential. Based on the intrinsic physical nature of LCE actuator's deformation, a terminal sliding mode controller (TSMC) is designed to efficiently adjust the temperature of the actuator, thus directly controlling the actuator deformation. In order to achieve the target temperature, a prediction model is established to predict the target temperature corresponding to the target displacement. Moreover, a proportional integration controller is applied to eliminate model errors and disturbances. The feasibility of the control strategy is experimentally verified, and compared with conventional PID controller. The results prove that the proposed control strategy can achieve fast and accurate control of displacement.

关键词： Heating systems Actuators Accuracy Deformation Position control Process control Elastomers

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An Event-Triggered Tracking control Scheme Based on Predictive control Method and its Application

An Event-Triggered Tracking Control Scheme Based on Predicti...

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Chinese control Conference (CCC)

作者： Lei Cao Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the tracking control problem for networked control system (NCS) under communication delays is investigated. In order to realize the tracking of the NCS, an event-triggered predictive control strategy is provided to compensate for the delays and achieve stability. Both tracking performance and input increments are considered in the proposed cost function. Based on the incremental inputs, an event-triggered mechanism is established to reduce the communication costs. The efforts of different parameters are illustrated through the results of the simulation, and the effectiveness of the proposed control scheme is also validated from the application of the networked lighting setup.

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Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

Continual Learning for Robust Gate Detection under Dynamic L...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhongzheng Qiao Xuan Huy Pham Savitha Ramasamy Xudong Jiang Erdal Kayacan Andriy Sarabakha ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Institute of Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

关键词： Continuing education Robot kinematics Neural networks Lighting Logic gates Robustness Real-time systems

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Coordinated control of distributed multi-agent systems using event-triggered predictive control strategy

Coordinated control of distributed multi-agent systems using...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Lei Cao Chuanguo Chi Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the coordinated control problem for a class of distributed networked multi-agent systems (NMASs) is studied. An event-triggered predictive coordinated control (ETPCC) scheme is proposed to compensate for the communication delay between controllers as well as to reduce the communication frequency. With the aim of reducing the frequency of data transfer among controllers and saving resources, a distributed event-triggered condition is designed to decide when to transfer information. Further, a cost function using predicted values is proposed to measure the cooperative performance among the agents, and the required control increment for each agent is obtained by minimizing the cost function. Simulation and experimental examples show that the proposed ETPCC strategy works effectively.

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