2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第2页

The problem of identification parameter data saturation in repetitive control and its solution 重复控制中辨识参数数据饱和的问题及解决方法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455609

Yong-Gyu Song, S. Zeng, Yutao Zhang

{"title":"The problem of identification parameter data saturation in repetitive control and its solution","authors":"Yong-Gyu Song, S. Zeng, Yutao Zhang","doi":"10.1109/DDCLS52934.2021.9455609","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455609","url":null,"abstract":"In the repetitive control of tracking periodic signals based on the principle of internal model, the control effect has a great relationship with the parameters of the controlled system. If the system is affected by noise and causes the internal parameters to change, failure to obtain the repeated control of the internal parameters in time will cause the system to lose stability. Therefore, how to quickly identify the parameters of the controlled system is particularly important in the field of repetitive control. In the actual process, the traditional least square method is often used to identify the parameters of the controlled system. However, the convergence of the algorithm to parameter identification is very slow. Once the controlled system parameters are changed, the parameter information provided by the new data cannot be updated in time, and the convergence of the identification results is very slow. In order to overcome the data saturation phenomenon of the least squares algorithm, this paper uses three methods of forgetting factor algorithm, variable gain matrix algorithm, and introducing additional matrix R algorithm to improve the traditional least squares identification algorithm, and verified these three through MATLAB simulation. Effectiveness of the method. Compared with traditional methods, the improved three identification methods can speed up the convergence of parameter identification and improve the accuracy of parameter identification.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126383315","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: 0

Parameter Estimation of the Hammerstein Output Error Model Using Multi-signal Processing 基于多信号处理的Hammerstein输出误差模型参数估计

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455525

Xinjian Zhu, Feng Li, Chenghao Li, L. Jia, Qingfeng Cao

引用次数: 3

Electrical Insulator Defects Detection Method Based on YOLOv5 基于YOLOv5的电绝缘子缺陷检测方法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455519

Zhiqiang Feng, Li Guo, Darong Huang, Runze Li

引用次数: 25

A Data-Driven Intelligent Medical Management System via Neural Networks 基于神经网络的数据驱动智能医疗管理系统

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455708

Jinhui Yang, Jianhui Wang, Xuhong Cheng, Zhiwei Guo, Yu Shen, Xu Gao

引用次数: 0

A new three-dimensional guidance law based on reduced-order extended state observer for highly maneuvering targets 基于降阶扩展状态观测器的高机动目标三维制导律

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455533

Pengjuan Ma, Sen Chen, Zhi-liang Zhao

{"title":"A new three-dimensional guidance law based on reduced-order extended state observer for highly maneuvering targets","authors":"Pengjuan Ma, Sen Chen, Zhi-liang Zhao","doi":"10.1109/DDCLS52934.2021.9455533","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455533","url":null,"abstract":"This paper proposes a new three-dimensional guidance law in order to achieve high-precision interception against the highly maneuvering targets in terminal guidance phase. Firstly, a reduced-order extended state observer is proposed to estimate the line-of-sight angle rate and the total disturbances composed of internal nonlinear dynamics and external disturbances. Secondly, a three-dimensional guidance law based on the reduced-order extended state observer is designed to actively compensate for the total disturbances and guarantee high-precision interception. The convergence and stability of the closed-loop interception system are analyzed rigorously. Different from the existing extended state observer based method, this paper only uses the line-of-sight angle as the output signal in the guidance law design. At the same time, the upper bounds of the system states are carefully analyzed to avoid the singularity of the closed-loop interception systems, which is not considered in existing results. Simulation results illustrate the effectiveness of the proposed method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131553587","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: 0

Denoising Control of Temperature Tracking for LiBr-H2O Absorption Refrigeration System 溴化锂- h2o吸收式制冷系统温度跟踪的去噪控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455498

Na Dong, Jianfang Chang, Wenjing Lv, Shuo Zhu

引用次数: 0

Passivity Analysis of Markov Jump Inertial Neural Networks Subject to Reaction-Diffusion 反应扩散作用下马尔可夫跳变惯性神经网络的无源性分析

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455517

Lingyun Sun, Xuelian Wang, Yuqing Qin, Lei Su, Hao Shen, Jing Wang

引用次数: 0

Operation State Assessment and Prediction of Distribution Transformer Based on Data Driven 基于数据驱动的配电变压器运行状态评估与预测

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455610

Min Fan, Gang Peng, Bo Zhang, Meng Zhou, Shitao Jia

{"title":"Operation State Assessment and Prediction of Distribution Transformer Based on Data Driven","authors":"Min Fan, Gang Peng, Bo Zhang, Meng Zhou, Shitao Jia","doi":"10.1109/DDCLS52934.2021.9455610","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455610","url":null,"abstract":"With the rapid development of Power Internet of Things, power grid monitoring data and analysis methods are increasing, so real-time dynamic monitoring of power equipment becomes possible. This paper presents a data driven method for evaluation and trend prediction of distribution transformer operation state. The key features reflecting dynamic change of operation state are extracted from voltage and current data of distribution transformer, and characteristic data flow is input into dynamic evaluation model to make real-time portrait description of distribution transformer operation state. According to time order and change trend of characteristic data flow, Long Short-Term Memory network (LSTM) is used to analysis regulation of characteristic data, and Support Vector Regression model (SVR) for its prediction. The future characteristic data flow is obtained, which is input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the feasibility, advanced nature and applicability of the method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131328497","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: 0

Estimation of the Unreported Infections of COVID-19 based on an Extended Stochastic Susceptible-Exposed-Infective-Recovered Model 基于扩展的随机易感-暴露-感染-恢复模型的COVID-19未报告感染估计

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455548

Lingyun Zhu, Wei Dong, Qingyun Sun, Esteban Vargas, Xin Du

引用次数: 2

3D Shape Descriptor by Principal Component Analysis Embedding for Non-rigid 3D Shape Retrieval in A Learning Framework 基于主成分分析嵌入的三维形状描述子在非刚性三维形状检索中的应用

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455676

Chunmei Duan, Meizhen Liu

引用次数: 0