2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第2页

Iterative Learning Control for Singular System with An Arbitrary Initial State 具有任意初始状态的奇异系统的迭代学习控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515976

Mengji Chen, Yinjun Zhang, Jianhuan Su

引用次数: 7

An Arm Isolation and Reconfiguration Fault Tolerant Control Method Based on Data-driven Methodology for Cascaded Seven-level Inverter 基于数据驱动的级联七电平逆变器臂隔离与重构容错控制方法

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8516073

Jiahui Zhang, Zhuo Liu, Tianzhen Wang, M. Benbouzid, Yide Wang

{"title":"An Arm Isolation and Reconfiguration Fault Tolerant Control Method Based on Data-driven Methodology for Cascaded Seven-level Inverter","authors":"Jiahui Zhang, Zhuo Liu, Tianzhen Wang, M. Benbouzid, Yide Wang","doi":"10.1109/DDCLS.2018.8516073","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516073","url":null,"abstract":"Inverts, especially multi-level inverters are widely used in many fields, such as industrial production, transportation, aviation and so on. So great significance should be attached to the diagnosis and fault tolerance of inverters to keep the stability of systems. Data-driven approaches make full use of the process data to monitor the systems, so the voltage signals are collected firstly and then preprocessed and processed by specific strategy, fault labels will be produced hereafter. When the fault labels from data-driven fault detection and diagnosis system are generated, relevant fault tolerant control method will be activated in fault tolerant control system. Some measurements are necessary to achieve the higher utilization ratio of healthy IGBTs and sinusoidal output voltage. Based on above consideration, a group isolation and reconfiguration fault tolerant control method based on data-driven methodology for cascaded seven-level inverter is proposed here to reconfigure the SPWM, in which every H-bridge is divided into two groups. The simulation of cascaded seven-level inverter is built and the result indicates that the utilization of healthy IGBTs is improved.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"31 1","pages":"939-943"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90220419","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: 5

Iterative Learning Consensus for Discrete-time Multi-Agent Systems with Measurement Saturation and Random Noises 具有测量饱和和随机噪声的离散多智能体系统的迭代学习一致性

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516009

Chen Liu, D. Shen

引用次数: 2

Based on Improved Semi-Supervise Clustering Method Training Classifier for Analog Circuit Fault Classification 基于改进半监督聚类方法的模拟电路故障分类器训练

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516105

A. Zhang, Kailun Huang, Gang Luo, Zhiqiang Zhang

{"title":"Based on Improved Semi-Supervise Clustering Method Training Classifier for Analog Circuit Fault Classification","authors":"A. Zhang, Kailun Huang, Gang Luo, Zhiqiang Zhang","doi":"10.1109/DDCLS.2018.8516105","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516105","url":null,"abstract":"In recent years, semi-supervised clustering as an important research subject has significance in dealing with lack of training sample sets. However, formerly semi-supervised clustering usually cannot attend satisfactory consequence in precision and training time at the same time. Aimed to the problem of clustering method assist training classifier to label the samples, produce the time optimization algorithm. Based on prior knowledge, mining the acquired unlabeled sample sets deeply of their potential data structure and combine semi-supervised fuzzy C-means(SS-FCM) arithmetic with similarity coefficient to sort out the samples for training time improvement. On the basis of little influence on classification result accuracy, gain the fuzzy similarity matrix from Euclidean distance and assess the maximum dependable sample point with its neighborhood for their similarity degree, will avoid searching the maximum dependable sample point one by one and optimize holistic clustering time costing from reduce the iterations of classifier to some extent. Through artificial circuit simulation experiment, using improvement SS-FCM assist SVM classifier and single SVM and SS-FCM assist SVM classifier to make a comparison, verify the algorithm from classify precision and arithmetic speed and the result of experiment can prove the validity of the improvement.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"121 1","pages":"199-203"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73108845","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

Point-to-Point Iterative Learning Control Based on Updating Reference Trajectory with Constrained Input 基于约束输入下参考轨迹更新的点对点迭代学习控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516053

Xiangfeng Shen, Z. Xiong, Yingdong Hong

引用次数: 2

Robust Stability for Nonlinear Fuzzy Network Control Systems with Time Varying Delay 时变时滞非线性模糊网络控制系统的鲁棒稳定性

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516077

Yue Hu, H. Lu, Chaoqun Guo, Xingping Liu, Renren Wang, Hongwei Chen

引用次数: 0

Sliding Mode Control of the RTAC System RTAC系统的滑模控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515977

Zhongtian Chen, Xianqing Wu, Xianhua Ou, Xiongxiong He

引用次数: 3

Big Data Mining Method of Thermal Power Based on Spark and Optimization Guidance 基于Spark和优化引导的火电大数据挖掘方法

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516098

Mingcheng Song, L. Jia

{"title":"Big Data Mining Method of Thermal Power Based on Spark and Optimization Guidance","authors":"Mingcheng Song, L. Jia","doi":"10.1109/DDCLS.2018.8516098","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516098","url":null,"abstract":"With the increasing degree of information technology in the electric-power industry, the amount of big data in thermal power has increased geometrically. To address the problem of the computational bottlenecks in traditional data mining deal with big data of thermal power, big data mining of thermal power method based on Spark is presented in this paper. According to the characteristics of the actual operation of the unit, the proposed method determines the steady-state conditions of big data of thermal power and divides the working conditions based on external constraints. In addition, data mining method based on distributed computing is used to mine big data of thermal power to get the strong association rules, thus the best value of the parameters under each working condition can be got. Lastly, the historical knowledge base is established, which can guide the operation of the unit by the proposed method. This method is applied to a 300 MW unit in a power plant in Anhui Province, and mines the operation data of the unit for 10 days in a month. The results of simulation show that the proposed method can effectively mine big data of thermal power and has the advantage of computational efficiency compared with traditional data mining for big data.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"344 1","pages":"514-520"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79747870","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: 2

Robust Repetitive Learning Control of Lower Limb Exoskeleton with Hybrid Electro-hydraulic System 基于混合电液系统的下肢外骨骼鲁棒重复学习控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8516005

Yong Yang, Deqing Huang, Xiucheng Dong

引用次数: 6

A Unified Iterative Learning Fault Detection and Fault-Tolerant Control 统一迭代学习故障检测与容错控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-01 DOI: 10.1109/DDCLS.2018.8515972

Q. Yan, Youfang Yu, Jianping Cai, Qingping Zhou

引用次数: 0