Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515976
Mengji Chen, Yinjun Zhang, Jianhuan Su
In this paper, a class of a class linear singular system with an arbitrary initial state was proposed based on singular value decomposition. A novel generalized theoretical result is presented by using the D-type learning law. We established the convergence conditions of algorithm. By the matrix theory, we give rigorous convergence proof. The effectiveness of the theoretical result is illustrated in two application examples.
{"title":"Iterative Learning Control for Singular System with An Arbitrary Initial State","authors":"Mengji Chen, Yinjun Zhang, Jianhuan Su","doi":"10.1109/DDCLS.2018.8515976","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515976","url":null,"abstract":"In this paper, a class of a class linear singular system with an arbitrary initial state was proposed based on singular value decomposition. A novel generalized theoretical result is presented by using the D-type learning law. We established the convergence conditions of algorithm. By the matrix theory, we give rigorous convergence proof. The effectiveness of the theoretical result is illustrated in two application examples.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"58 1","pages":"141-144"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81044761","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8516073
Jiahui Zhang, Zhuo Liu, Tianzhen Wang, M. Benbouzid, Yide Wang
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.
{"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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516009
Chen Liu, D. Shen
This paper investigates the consensus tracking problem for a class of multi-agent systems with measurement saturation and random noises. A distributed iterative learning control algorithm is proposed by utilizing the input signals and the measured output information from previous iterations. The considered multi-agent systems have a fixed topology of the communication graph and the desired trajectory is only accessible to a subset of agents. With the help of a decreasing gain sequence, it is proved that the input sequence will converge to the desired one in an almost sure sense as the iteration number goes to infinity. Simulation results are given to verify the effectiveness of the proposed algorithm.
{"title":"Iterative Learning Consensus for Discrete-time Multi-Agent Systems with Measurement Saturation and Random Noises","authors":"Chen Liu, D. Shen","doi":"10.1109/DDCLS.2018.8516009","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516009","url":null,"abstract":"This paper investigates the consensus tracking problem for a class of multi-agent systems with measurement saturation and random noises. A distributed iterative learning control algorithm is proposed by utilizing the input signals and the measured output information from previous iterations. The considered multi-agent systems have a fixed topology of the communication graph and the desired trajectory is only accessible to a subset of agents. With the help of a decreasing gain sequence, it is proved that the input sequence will converge to the desired one in an almost sure sense as the iteration number goes to infinity. Simulation results are given to verify the effectiveness of the proposed algorithm.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"50-55"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74756632","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516105
A. Zhang, Kailun Huang, Gang Luo, Zhiqiang Zhang
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.
{"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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516053
Xiangfeng Shen, Z. Xiong, Yingdong Hong
The point-to-point tracking control method under constrained input is proposed by using updating-reference and an integrated predictive iterative learning control strategy. A reference trajectory through the desired key points is adopted and updated batch-to-batch, and then the whole system is described as 2D model. By using the integrated predictive ILC, the control method can depress effectively disturbances. For the constrained input, its convex set is abstracted and the procedure of calculating the constrained input is presented in detail. Comparing with gradient based point-to-point control algorithms, updating- reference relaxes the output constraints and the proposed algorithm can lead to faster convergence. Simulation results of a numerical model have demonstrated the effectiveness of the proposed method.
{"title":"Point-to-Point Iterative Learning Control Based on Updating Reference Trajectory with Constrained Input","authors":"Xiangfeng Shen, Z. Xiong, Yingdong Hong","doi":"10.1109/DDCLS.2018.8516053","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516053","url":null,"abstract":"The point-to-point tracking control method under constrained input is proposed by using updating-reference and an integrated predictive iterative learning control strategy. A reference trajectory through the desired key points is adopted and updated batch-to-batch, and then the whole system is described as 2D model. By using the integrated predictive ILC, the control method can depress effectively disturbances. For the constrained input, its convex set is abstracted and the procedure of calculating the constrained input is presented in detail. Comparing with gradient based point-to-point control algorithms, updating- reference relaxes the output constraints and the proposed algorithm can lead to faster convergence. Simulation results of a numerical model have demonstrated the effectiveness of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"788-793"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78230448","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}
In this paper, there will be considered the robust stability problem in the nonlinear fuzzy network control system. In the nonlinear fuzzy network control system, the delay dependent condition is proposed by the linear matrix inequality(LMI) method. Based an applicable free weighting matrix (FWM) method, the delay upper bound of the fuzzy network control system is obtained. Finally, there will be given a numerical example to proof the proposed method.
{"title":"Robust Stability for Nonlinear Fuzzy Network Control Systems with Time Varying Delay","authors":"Yue Hu, H. Lu, Chaoqun Guo, Xingping Liu, Renren Wang, Hongwei Chen","doi":"10.1109/DDCLS.2018.8516077","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516077","url":null,"abstract":"In this paper, there will be considered the robust stability problem in the nonlinear fuzzy network control system. In the nonlinear fuzzy network control system, the delay dependent condition is proposed by the linear matrix inequality(LMI) method. Based an applicable free weighting matrix (FWM) method, the delay upper bound of the fuzzy network control system is obtained. Finally, there will be given a numerical example to proof the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"46 1","pages":"1095-1100"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75002068","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515977
Zhongtian Chen, Xianqing Wu, Xianhua Ou, Xiongxiong He
In this paper, a sliding mode control (SMC) scheme is presented for the rotational/translational actuator (RTAC) system, which is proposed without linearizing or approximating the dynamics. Different from the existing control methods, external disturbances are taken into consideration in this paper. In particular, after some model transformations, a novel dynamic equation with a cascade form of the underactuated RTAC system is obtained. Then, based on the backstepping technique, a desired control variable is proposed to control the first subsystem and a corresponding deviation-based subsystem is constructed. On the basis of the introduced deviation-based subsystem, a sliding mode controller is proposed straightforwardly. Simulation results including a comparative study are included to examine the control performance of the presented scheme.
{"title":"Sliding Mode Control of the RTAC System","authors":"Zhongtian Chen, Xianqing Wu, Xianhua Ou, Xiongxiong He","doi":"10.1109/DDCLS.2018.8515977","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515977","url":null,"abstract":"In this paper, a sliding mode control (SMC) scheme is presented for the rotational/translational actuator (RTAC) system, which is proposed without linearizing or approximating the dynamics. Different from the existing control methods, external disturbances are taken into consideration in this paper. In particular, after some model transformations, a novel dynamic equation with a cascade form of the underactuated RTAC system is obtained. Then, based on the backstepping technique, a desired control variable is proposed to control the first subsystem and a corresponding deviation-based subsystem is constructed. On the basis of the introduced deviation-based subsystem, a sliding mode controller is proposed straightforwardly. Simulation results including a comparative study are included to examine the control performance of the presented scheme.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"87 1","pages":"682-687"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77399919","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516098
Mingcheng Song, L. Jia
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.
{"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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8516005
Yong Yang, Deqing Huang, Xiucheng Dong
In this paper, robust repetitive learning control for lower limb exoskeleton, CASWELL-II, is addressed. A hybrid electro-hydraulic system which consists of unidirectional servo valve and magnetic valve is presented to driven the exoskeleton leg. First, a full state space model of CASWELL-II is worked out by combining both the rigid body and hybrid electro-hydraulic actuators dynamics. Secondly, a robust repetitive learning controller is presented to perform the periodic tracking task of the hybrid electro-hydraulic actuators via backstepping design, and the stability of the closed-loop system is proved by Lyapunov method. Finally, the controller is realized and tested on CASWELL-II by experiment.
{"title":"Robust Repetitive Learning Control of Lower Limb Exoskeleton with Hybrid Electro-hydraulic System","authors":"Yong Yang, Deqing Huang, Xiucheng Dong","doi":"10.1109/DDCLS.2018.8516005","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516005","url":null,"abstract":"In this paper, robust repetitive learning control for lower limb exoskeleton, CASWELL-II, is addressed. A hybrid electro-hydraulic system which consists of unidirectional servo valve and magnetic valve is presented to driven the exoskeleton leg. First, a full state space model of CASWELL-II is worked out by combining both the rigid body and hybrid electro-hydraulic actuators dynamics. Secondly, a robust repetitive learning controller is presented to perform the periodic tracking task of the hybrid electro-hydraulic actuators via backstepping design, and the stability of the closed-loop system is proved by Lyapunov method. Finally, the controller is realized and tested on CASWELL-II by experiment.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"26 1","pages":"718-723"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81909375","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}
Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515972
Q. Yan, Youfang Yu, Jianping Cai, Qingping Zhou
In this paper, a unified iterative learning based fault detection and fault-tolerant control scheme is proposed. A system fault detector is constructed by using contraction mapping technique, and LMI technique is applied in the design of Lyapnov-based iterative controller, responsible for solving the state tracking problem no matter whether faults occur or not. Numerical results demonstrate the effectiveness of the proposed unified fault detection and control scheme.
{"title":"A Unified Iterative Learning Fault Detection and Fault-Tolerant Control","authors":"Q. Yan, Youfang Yu, Jianping Cai, Qingping Zhou","doi":"10.1109/DDCLS.2018.8515972","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515972","url":null,"abstract":"In this paper, a unified iterative learning based fault detection and fault-tolerant control scheme is proposed. A system fault detector is constructed by using contraction mapping technique, and LMI technique is applied in the design of Lyapnov-based iterative controller, responsible for solving the state tracking problem no matter whether faults occur or not. Numerical results demonstrate the effectiveness of the proposed unified fault detection and control scheme.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"119 1","pages":"984-989"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84897291","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}
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