2021 33rd Chinese Control and Decision Conference (CCDC)最新文献_第10页

Optimal Security Charging Service for Electric Vehicles by Mobile Charging Stations: Dynamic Contract-based Approach 移动充电站最优安全充电服务:基于动态契约的方法

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9602348

Huwei Chen, Yong Zhao, Rongbin Zhang, Zhihui Chen, Shijun Chen

{"title":"Optimal Security Charging Service for Electric Vehicles by Mobile Charging Stations: Dynamic Contract-based Approach","authors":"Huwei Chen, Yong Zhao, Rongbin Zhang, Zhihui Chen, Shijun Chen","doi":"10.1109/CCDC52312.2021.9602348","DOIUrl":"https://doi.org/10.1109/CCDC52312.2021.9602348","url":null,"abstract":"Electric vehicles (EVs) are designed to improve the power efficiency as well as it prevents the environment from being polluted, and thus they are widely and reasonably used in the power market. Thanks to mobile charging stations (MCSs), it provides the charging service to EVs more easily with higher payoff and lower consumption, compared with the fixed charging stations (FCSs). In this paper, we propose a dynamic contract-based strategy to apply the secured charging service from MCS in Internet of things (IoTs), considering incomplete information of EV users. Firstly, considering this charging system composed of multiple EV users and MCSs, we design the utility function of each one, where non-contract power are supplied to EV users besides the power in contract items in IoTs. Then, in order to maximize MCS's profits, an optimization problem is formulated to obtain the optimal contract items satisfying EV users' power demand. Thirdly, the optimal solution of this problem is efficiently achieved through the presented iterative search algorithm, considering the different types of EV users with different contract items. Finally, simulation results validate the effectiveness of our proposal and each of them in this charging system can be benefited.","PeriodicalId":143976,"journal":{"name":"2021 33rd Chinese Control and Decision Conference (CCDC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129984227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Design and Implementation of Control System for Four-Axis Picking Manipulator 四轴拾取机械手控制系统的设计与实现

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601761

C. Mu, Wenxiao Gao, Haiping An, Xing Ma

引用次数: 0

A Graphical Convolutional Network-based Method for 3D Point Cloud Classification 基于图形卷积网络的三维点云分类方法

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601582

Liang Wang, Jianshu Li, Deqiao Fan

{"title":"A Graphical Convolutional Network-based Method for 3D Point Cloud Classification","authors":"Liang Wang, Jianshu Li, Deqiao Fan","doi":"10.1109/CCDC52312.2021.9601582","DOIUrl":"https://doi.org/10.1109/CCDC52312.2021.9601582","url":null,"abstract":"Point cloud data classification has been widely used in autonomous driving, robot perception, and virtual/augmented reality. Due to its irregularity and disorder, the classification task of point clouds needs to transform the point cloud into a multi-view or voxel grid, and then use the traditional convolution neural network processing. However, this process is not only complex in operation but also low in classification accuracy. To solve this problem, a new point cloud classification method based on the graphical convolutional neural network (GCN) is proposed. Firstly, based on PointNet, KNN graph is introduced to obtain global deep features. Then the 3D point cloud is represented as a directed graph, local features are extracted by edge convolution. Finally, the extracted global and local features are aggregated to represent and classify point clouds. The proposed network is evaluated on the open dataset ModelNet40 and 3DMNIST. Experimental results show that the proposed network can achieve on par or better performance than state-of-the-art, such as PointNet, PointNet++, DGCNN, and PointCNN, for point cloud classification.","PeriodicalId":143976,"journal":{"name":"2021 33rd Chinese Control and Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of ROV Adaptive Sliding Mode Control System for Underwater Vehicle Based on RBF Neural Network 基于RBF神经网络的水下机器人自适应滑模控制系统设计

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9602771

Wei Chen, S. Hu, Qingyu Wei

引用次数: 0

Simplified Model Predictive Control for Permanent Magnet Synchronous Machine based Back-to-Back Converter 基于背靠背变换器的永磁同步电机简化模型预测控制

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601820

Jianqiao Zou

引用次数: 0

Neural Network Based Nonlinear Model Predictive Control for Two-stage Turbocharged Diesel Engine Air-path System 基于神经网络的两级增压柴油机气路系统非线性模型预测控制

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9602515

Chang Ke, K. Han, Ying Huang, Xu Wang, Sichun Bai

{"title":"Neural Network Based Nonlinear Model Predictive Control for Two-stage Turbocharged Diesel Engine Air-path System","authors":"Chang Ke, K. Han, Ying Huang, Xu Wang, Sichun Bai","doi":"10.1109/CCDC52312.2021.9602515","DOIUrl":"https://doi.org/10.1109/CCDC52312.2021.9602515","url":null,"abstract":"The air-path system of the two-stage turbocharged diesel engine, the characteristics of which include strong nonlinearity, time delay, coupling and constraints, increases the difficulty in engine control. To solve the control problem of the system, a nonlinear model predictive (NMPC) controller based on nonlinear autoregressive model with exogenous input neural network (NARXNN) is developed. At first, a boost pressure predictive model, of which fuel injection quantity is the first input and bypass valve opening is the second input, and the boost pressure is the output, is established based on NARXNN. Through simulation analysis, the absolute error between the output value of the plant model and the predictive model is smaller than 0.05 bar. Then the predictive accuracy of the predictive model when the predictive horizons are different is analyzed, and the Mean Absolute Percentage Error (MAPE) is less than 2% when the predictive horizon is within 30, indicating that the predictive model has good multi-step predictive performance. At last, the NMPC controller based on the NMPC toolbox in MATLAB is established. And the the step response performance and reference-tracking performance of the controller are verified in the co-simulation platform formed by GT-Power and MATLAB/Simulink. It can be concluded from the results that the step response performance of the NMPC controller is better than that of the PID controller, and the relative error of the reference- tracking simulation is smaller than 15%.","PeriodicalId":143976,"journal":{"name":"2021 33rd Chinese Control and Decision Conference (CCDC)","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131481269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Trajectory optimization for multiple asteroid rendezvous mission through hybrid propulsion 基于混合推进的多小行星交会轨道优化

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601440

Enmi Yong, Xiao Wang

引用次数: 1

Center Extraction Algorithm of Linear Structured Light Stripe Based on Improved Gray Barycenter Method 基于改进灰色质心法的线性结构光条中心提取算法

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9602635

Hongtao Yang, Zhen Wang, Weibo Yu, Peng Zhang

引用次数: 0

Vision-Based Robotic Manipulation of Intelligent Wheelchair with Human-Computer Shared Control 基于视觉的人机共享控制智能轮椅机器人操作

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601850

Siyi Du, Fei Wang, Guilin Zhou, Jiaqi Li, Lintao Yang, Dongxu Wang

{"title":"Vision-Based Robotic Manipulation of Intelligent Wheelchair with Human-Computer Shared Control","authors":"Siyi Du, Fei Wang, Guilin Zhou, Jiaqi Li, Lintao Yang, Dongxu Wang","doi":"10.1109/CCDC52312.2021.9601850","DOIUrl":"https://doi.org/10.1109/CCDC52312.2021.9601850","url":null,"abstract":"Based on human-computer shared control, this paper introduces a novel robotic manipulation fashion combining computer vision and brain-computer interface (BCI). Designed for disabled groups, the intelligent wheelchair with our proposed method exhibits the precise robotic manipulation ability but also the human decision-making capabilities, which will bring better life quality for the disabled. The overall pipeline includes three parts: asynchronous brain-computer interface based on steady-state visual evoked potential (SSVEP), vision detection with deep network and robotic manipulation of UR5 robot. Particularly, first, the user receives the periodic visual stimulation with different frequencies and then electroencephalography (EEG) signals of the user are collected by EEG cap. Second, we preprocess the EEG signals and extract feature embedding. To judge the frequency of the stimulus signals received by the user, the canonical correlation analysis (CCA) algorithm is used to fit and compare it with the standard EEG signal. In our work, the signals with different frequencies corresponds to different types of objects item by item. Third, we apply the off-the-shelfvision detection algorithm, Mask-RCNN, to output the position of the object corresponding to the detected EEG in the image frame. UR5 robot arm plan manipulation path according to the position of objects transferred by robot operating system (ROS). Extensive experiments show that our method can achieves performance with more than 90% accuracy and the user can control the robot arm to grab the expected object accurately through BCI.","PeriodicalId":143976,"journal":{"name":"2021 33rd Chinese Control and Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132962071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modeling and simulation of maximum wind energy capture by segmented slope duty-cycle perturbation method 分段斜率占空比摄动法最大风能捕获建模与仿真

2021 33rd Chinese Control and Decision Conference (CCDC) Pub Date : 2021-05-22 DOI: 10.1109/CCDC52312.2021.9601944

Yueling Zhao, Sijia Kong, Dinghua Liu, Dong Guo

引用次数: 0