Pub Date : 2018-05-01DOI: 10.1109/DDCLS.2018.8515986
Lijuan Wan, Chunping Chen, Yan Ji
Mathematical models are the base for the system analysis and the controller design. This paper focuses on the identification problems of controlled autoregressive models with autoregressive noise (CARAR system for short). By applying the iterative method and the hierarchical principle, a least squares identification algorithm is investigated. The key of this algorithm is replacing the unknown noise terms in the information vector with their estimated residuals. The effectiveness of this approach is demonstrated by the simulation experiment.
{"title":"A New Least Squares Iterative Estimation Algorithm for CARAR Systems","authors":"Lijuan Wan, Chunping Chen, Yan Ji","doi":"10.1109/DDCLS.2018.8515986","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515986","url":null,"abstract":"Mathematical models are the base for the system analysis and the controller design. This paper focuses on the identification problems of controlled autoregressive models with autoregressive noise (CARAR system for short). By applying the iterative method and the hierarchical principle, a least squares identification algorithm is investigated. The key of this algorithm is replacing the unknown noise terms in the information vector with their estimated residuals. The effectiveness of this approach is demonstrated by the simulation experiment.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"11 1","pages":"1170-1173"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84890156","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.2018.8516070
Chiang-Ju Chien, Ying-Chung Wang, Feng‐Li Lian
Design of iterative learning controller for continuous-time nonlinear systems with iteration-varying uncertainties is studied in this paper. The iteration-varying uncertainties include initial resetting tracking error, iteration-varying external disturbance, iteration-varying desired trajectory and iteration-varying system parameters. The iteration-varying uncertainties are not required to take any special structure and the uncertain bounds are not necessarily small. All the iteration-varying uncertainties are compensated by an adaptive iterative learning controller with a projection-type adaptive law. We show that the system output can converge to the desired one as close as possible after suitable numbers of learning trials. Compared with the existing papers studying the similar problems, this approach can be used to solve the iterative learning control issue with more general class of nonlinear uncertain systems and achieve better learning performance.
{"title":"Design and Analysis of Adaptive Iterative Learning Control for Iteration-varying Nonlinear Systems","authors":"Chiang-Ju Chien, Ying-Chung Wang, Feng‐Li Lian","doi":"10.1109/DDCLS.2018.8516070","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516070","url":null,"abstract":"Design of iterative learning controller for continuous-time nonlinear systems with iteration-varying uncertainties is studied in this paper. The iteration-varying uncertainties include initial resetting tracking error, iteration-varying external disturbance, iteration-varying desired trajectory and iteration-varying system parameters. The iteration-varying uncertainties are not required to take any special structure and the uncertain bounds are not necessarily small. All the iteration-varying uncertainties are compensated by an adaptive iterative learning controller with a projection-type adaptive law. We show that the system output can converge to the desired one as close as possible after suitable numbers of learning trials. Compared with the existing papers studying the similar problems, this approach can be used to solve the iterative learning control issue with more general class of nonlinear uncertain systems and achieve better learning performance.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"7 1","pages":"469-474"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86185410","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}
Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. The more comprehensive metrics accuracy and F1 Score are introduced for evaluation. The results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. The proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.
{"title":"Anode Effect prediction based on Expectation Maximization and XGBoost model","authors":"Zhixin Zhang, Gaofeng Xu, Hongting Wang, Kaibo Zhou","doi":"10.1109/DDCLS.2018.8516046","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516046","url":null,"abstract":"Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. The more comprehensive metrics accuracy and F1 Score are introduced for evaluation. The results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. The proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"9 1","pages":"560-564"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84115297","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.2018.8516012
Chunliang Zhao, Yuanyuan Hao, Shulin Sui, Shujiao Sui
License plate detection includes license plate positioning, segmentation characters, character recognition. The recognition rate of license plates under dynamic scenes is affected by many factors. Each process deviation may affect the overall system recognition rate, and the accuracy of each part is affected by many factors, in order to reduce this error, we combine the advantages of a variety of algorithms to propose a comprehensive detection model. In the license plate positioning phase, we propose HSV space and morphological methods; in the segmentation character phase, we propose the maximum adjacent character horizontal center distance segmentation method; in the character recognition stage, we choose to use the CNN algorithm. In the final simulation test, there are a set of 1 errors in the 30 groups of license plate recognition, the accuracy is higher.
{"title":"A New Method to Detect the License Plate in Dynamic Scene","authors":"Chunliang Zhao, Yuanyuan Hao, Shulin Sui, Shujiao Sui","doi":"10.1109/DDCLS.2018.8516012","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516012","url":null,"abstract":"License plate detection includes license plate positioning, segmentation characters, character recognition. The recognition rate of license plates under dynamic scenes is affected by many factors. Each process deviation may affect the overall system recognition rate, and the accuracy of each part is affected by many factors, in order to reduce this error, we combine the advantages of a variety of algorithms to propose a comprehensive detection model. In the license plate positioning phase, we propose HSV space and morphological methods; in the segmentation character phase, we propose the maximum adjacent character horizontal center distance segmentation method; in the character recognition stage, we choose to use the CNN algorithm. In the final simulation test, there are a set of 1 errors in the 30 groups of license plate recognition, the accuracy is higher.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"125 1","pages":"414-419"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91051053","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.2018.8516082
Yong-long Peng, Jianghao Huang, Yabin Li, Peizhe Liu, Jiuhui Cao
With the rapid development of electric vehicle, the problems of power quality on charging station have attracted much attention. Due to some traits of the charging station, the harmonic current changes gradually with time. What’s more, the traditional harmonic detection method based on ip−iq algorithm is influenced by the low-pass filter, resulting in the detecting and starting speed are relatively slow, which cannot satisfy the requests of charging station harmonic suppression. On the basis of analyzing the charging generator model based on the six-pulse rectifier, the charging station model of the charging generator based on the six-pulse rectification is established. A novel harmonic current detection algorithm based on adaptive filter of variable step size LMS / LMF algorithm is proposed and its theory is analyzed in detail. Simulation and experiment results show that the improved harmonic detection algorithm has variously improved in terms of the tracking speed and starting speed, which achieves desired effects.
{"title":"A Novel Harmonic Detection Algorithm for Electric Vehicle with Charging Piles","authors":"Yong-long Peng, Jianghao Huang, Yabin Li, Peizhe Liu, Jiuhui Cao","doi":"10.1109/DDCLS.2018.8516082","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516082","url":null,"abstract":"With the rapid development of electric vehicle, the problems of power quality on charging station have attracted much attention. Due to some traits of the charging station, the harmonic current changes gradually with time. What’s more, the traditional harmonic detection method based on ip−iq algorithm is influenced by the low-pass filter, resulting in the detecting and starting speed are relatively slow, which cannot satisfy the requests of charging station harmonic suppression. On the basis of analyzing the charging generator model based on the six-pulse rectifier, the charging station model of the charging generator based on the six-pulse rectification is established. A novel harmonic current detection algorithm based on adaptive filter of variable step size LMS / LMF algorithm is proposed and its theory is analyzed in detail. Simulation and experiment results show that the improved harmonic detection algorithm has variously improved in terms of the tracking speed and starting speed, which achieves desired effects.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"19 1","pages":"836-840"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82014856","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.2018.8515947
Benkai Li, Qinglai Wei, Derong Liu
This paper investigates the consensus problem for leader-following multi-agent systems with general linear dynamics in directed communication topology. The fixed directed communication topology is considered. To adjust the coupling weights of neighboring agents, an adjacent state feedback protocol with an adaptive law is developed. LaSalle’s invariance principle is used to analyze the stability. The consensus for multi-agent systems under directed communication topology containing a directed spanning tree with the leader as the root can be realized. The design method is based on Riccati inequality as well as algebraic graph theory. Finally, two examples are shown to illustrate the performance of the present controller.
{"title":"Cooperative Adaptive Control for Consensus of Leader-Following General Linear Multi-Agent Systems in Directed Communication Topology","authors":"Benkai Li, Qinglai Wei, Derong Liu","doi":"10.1109/DDCLS.2018.8515947","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515947","url":null,"abstract":"This paper investigates the consensus problem for leader-following multi-agent systems with general linear dynamics in directed communication topology. The fixed directed communication topology is considered. To adjust the coupling weights of neighboring agents, an adjacent state feedback protocol with an adaptive law is developed. LaSalle’s invariance principle is used to analyze the stability. The consensus for multi-agent systems under directed communication topology containing a directed spanning tree with the leader as the root can be realized. The design method is based on Riccati inequality as well as algebraic graph theory. Finally, two examples are shown to illustrate the performance of the present controller.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 3 1","pages":"27-32"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83411309","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.2018.8516113
Chenlong Li, Yong Fang, Jialu Zhang
This paper investigates the formation control problem for discrete-time multi-agent systems with switching network topologies and data quantization. It is assumed that the tracking error signals of individual agent are quantized before they are transmitted into the iterative learning controller. However, quantification of data can lead to quantization error, which seriously impacts the performance of multi-agent systems. Based on the nearest neighbor interaction rule, a quantized iterative learning approach is given to overcome the quantization error in the occasion of switching network topologies and guarantee the accurate formation of multi-agent systems simultaneously. Simulation results are provided to verify the effectiveness of the proposed method.
{"title":"Quantized Iterative Learning Control for Formation of Multi-agent System","authors":"Chenlong Li, Yong Fang, Jialu Zhang","doi":"10.1109/DDCLS.2018.8516113","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516113","url":null,"abstract":"This paper investigates the formation control problem for discrete-time multi-agent systems with switching network topologies and data quantization. It is assumed that the tracking error signals of individual agent are quantized before they are transmitted into the iterative learning controller. However, quantification of data can lead to quantization error, which seriously impacts the performance of multi-agent systems. Based on the nearest neighbor interaction rule, a quantized iterative learning approach is given to overcome the quantization error in the occasion of switching network topologies and guarantee the accurate formation of multi-agent systems simultaneously. Simulation results are provided to verify the effectiveness of the proposed method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"79 1","pages":"112-116"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83795733","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.2018.8516095
Chenxu Ke, Jinrui Ren, Q. Quan
In this paper, a saturated D-type iterative learning control (ILC) method is proposed for multicopter trajectory tracking based on the additive state decomposition (ASD) method. By using the ASD method, the multicopter nonlinear horizontal channel with input saturation is divided into a linear primary system and a nonlinear secondary system. The ILC method for linear systems can be used directly in the linear primary system to track desired trajectories. A state feedback is applied to stabilize the nonlinear secondary system. Then, the above two controllers are combined to achieve the control goal. Simulation results demonstrate the feasibility of the proposed method for the multicopter trajectory tracking problem with input saturation and other nonlinearities.
{"title":"Saturated D-type ILC for Multicopter Trajectory Tracking Based on Additive State Decomposition","authors":"Chenxu Ke, Jinrui Ren, Q. Quan","doi":"10.1109/DDCLS.2018.8516095","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516095","url":null,"abstract":"In this paper, a saturated D-type iterative learning control (ILC) method is proposed for multicopter trajectory tracking based on the additive state decomposition (ASD) method. By using the ASD method, the multicopter nonlinear horizontal channel with input saturation is divided into a linear primary system and a nonlinear secondary system. The ILC method for linear systems can be used directly in the linear primary system to track desired trajectories. A state feedback is applied to stabilize the nonlinear secondary system. Then, the above two controllers are combined to achieve the control goal. Simulation results demonstrate the feasibility of the proposed method for the multicopter trajectory tracking problem with input saturation and other nonlinearities.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"120 1","pages":"1146-1151"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87893495","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.2018.8516024
Yongshuai Wang, Zengqiang Chen, Mingwei Sun, Qinglin Sun
In the process of modern industrial control, systems with large time-delay are typical problems. Thus in order to get better control effect, it is productive by combining advanced control methods with traditional Smith predictor. The aim of this paper is to present the LADRC(linear active disturbance rejection control)-Smith controller design and parameters analysis for first-order inertial systems with large time-delay, along with the discussion of frequency response and parameters perturbation for systems. To be specific, it is proved that the system is stable when parameters of plant are exactly known. Moreover, a sufficient stable condition is obtained when parameters of plant change. Besides, the step response, stability margin and capability of disturbance rejection are compared when the plant has a different degree of perturbation, and these results make great sense to design the LADRC-Smith controller and regulate parameters for time-delay systems.
{"title":"LADRC-Smith Controller Design and Parameters Analysis for First-Order Inertial Systems with Large Time-Delay","authors":"Yongshuai Wang, Zengqiang Chen, Mingwei Sun, Qinglin Sun","doi":"10.1109/DDCLS.2018.8516024","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516024","url":null,"abstract":"In the process of modern industrial control, systems with large time-delay are typical problems. Thus in order to get better control effect, it is productive by combining advanced control methods with traditional Smith predictor. The aim of this paper is to present the LADRC(linear active disturbance rejection control)-Smith controller design and parameters analysis for first-order inertial systems with large time-delay, along with the discussion of frequency response and parameters perturbation for systems. To be specific, it is proved that the system is stable when parameters of plant are exactly known. Moreover, a sufficient stable condition is obtained when parameters of plant change. Besides, the step response, stability margin and capability of disturbance rejection are compared when the plant has a different degree of perturbation, and these results make great sense to design the LADRC-Smith controller and regulate parameters for time-delay systems.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"93 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89736773","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}
The calculation of canopy aerodynamic parameters plays an important part in the airdrop system. Based on the finite volume method, this paper calculates the aerodynamic parameters of the parafoil systems, and then the deflection and incision factors are estimated by the CFD output data. The obtained lift and drag coefficients instead of the traditional parameters based on lifting-line theory are incorporated into the aerodynamic equation of a parafoil system. The active disturbance rejection control strategy is applied to control the systems. The effectiveness of the proposed method can be demonstrated by the simulation results. The work in this paper may be a reference for the parafoil system design.
{"title":"Modeling and Control of Parafoil Systems Based on CFD","authors":"Wannan Wu, Qinglin Sun, Mingwei Sun, Zengqiang Chen","doi":"10.1109/DDCLS.2018.8516092","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516092","url":null,"abstract":"The calculation of canopy aerodynamic parameters plays an important part in the airdrop system. Based on the finite volume method, this paper calculates the aerodynamic parameters of the parafoil systems, and then the deflection and incision factors are estimated by the CFD output data. The obtained lift and drag coefficients instead of the traditional parameters based on lifting-line theory are incorporated into the aerodynamic equation of a parafoil system. The active disturbance rejection control strategy is applied to control the systems. The effectiveness of the proposed method can be demonstrated by the simulation results. The work in this paper may be a reference for the parafoil system design.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"295-301"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89799166","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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