Pub Date : 2019-05-01DOI: 10.1109/DDCLS.2019.8908864
Juntao Li, Mingming Chang, Qinghui Gao, Xuekun Song
Small cell lung cancer (SCLC) accounts for a small proportion of lung cancer types, but its mortality rate is the highest owing to the rapidly early development. Identifying the key genes of SCLC will be of great significance for targeted therapy. In this paper, a new gene identification method is proposed by combining affinity propagation (AP) clustering and conditional mutual information (CMI). AP clustering is firstly presented to divide genes of SCLC into 49 groups. Then gene significance in each group is evaluated by CMI. Eight genes with highest significance in corresponding groups and four exemplars whose significance is larger than two-thirds of the maximum significance index are identified as key genes of SCLC after literature search in NCBI database.
{"title":"Gene Identification for Small Cell Lung Cancer via Combining Affinity Propagation Clustering and Conditional Mutual Information","authors":"Juntao Li, Mingming Chang, Qinghui Gao, Xuekun Song","doi":"10.1109/DDCLS.2019.8908864","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908864","url":null,"abstract":"Small cell lung cancer (SCLC) accounts for a small proportion of lung cancer types, but its mortality rate is the highest owing to the rapidly early development. Identifying the key genes of SCLC will be of great significance for targeted therapy. In this paper, a new gene identification method is proposed by combining affinity propagation (AP) clustering and conditional mutual information (CMI). AP clustering is firstly presented to divide genes of SCLC into 49 groups. Then gene significance in each group is evaluated by CMI. Eight genes with highest significance in corresponding groups and four exemplars whose significance is larger than two-thirds of the maximum significance index are identified as key genes of SCLC after literature search in NCBI database.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"41 1","pages":"287-291"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84062688","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908921
Huihui Shi, Qiang Chen, Kaijie Chen, Mingxuan Sun
This paper presents an adaptive iterative learning control for a class of nonlinear systems with input saturation. The input saturation is approximated by a smooth hyperbolic tangent function based on the mean-value theorem. Then, an integral Lyapunov function is constructed to avoid the potential singularity problem caused by the differential of unknown gain functions. A radial basis function neural network (RBFNN) is employed to approximate the unknown system nonlinearity, and the combined adaptive laws are designed to estimate NN weight and the bound of the approximation error, respectively. With the proposed scheme, the tracking error is guaranteed to converge into a neighborhood of zero in the sense of $L^{2}$-norm within the finite iterations, and numerical simulations show the effectiveness of the proposed scheme.
{"title":"Adaptive Iterative Learning Control of Nonlinear Systems with Input Saturation","authors":"Huihui Shi, Qiang Chen, Kaijie Chen, Mingxuan Sun","doi":"10.1109/DDCLS.2019.8908921","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908921","url":null,"abstract":"This paper presents an adaptive iterative learning control for a class of nonlinear systems with input saturation. The input saturation is approximated by a smooth hyperbolic tangent function based on the mean-value theorem. Then, an integral Lyapunov function is constructed to avoid the potential singularity problem caused by the differential of unknown gain functions. A radial basis function neural network (RBFNN) is employed to approximate the unknown system nonlinearity, and the combined adaptive laws are designed to estimate NN weight and the bound of the approximation error, respectively. With the proposed scheme, the tracking error is guaranteed to converge into a neighborhood of zero in the sense of $L^{2}$-norm within the finite iterations, and numerical simulations show the effectiveness of the proposed scheme.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 1","pages":"1208-1212"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90678421","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909009
S. Tian, Hong-guang Li, Yongjian Wang
Emotion plays a vital role in human learning, memory and decision-making, which have attracted wide attention in various research fields. To improve the learning speed and performance, a novel Brain Emotional Learning Network (BELN) is developed in this paper, which adds the affective neurons into the model of brain emotional learning and ameliorates the structure of the model. The newly proposed BELN enjoys lower computational complexity, in the sense that two affective coefficients named “anxiety” and “confidence” are added to simulate the changes of emotion in human learning process, which improves the online learning speed of the network. In order to verify the effectiveness of the network, a numerical example and the predictive control of petroleum heating process in a co-current tubular heat exchanger have been studied. The results show that compared with the BP neural network, BELN has better learning speed and recognition ability.
{"title":"Brain Emotional Learning Networks with Applications","authors":"S. Tian, Hong-guang Li, Yongjian Wang","doi":"10.1109/DDCLS.2019.8909009","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909009","url":null,"abstract":"Emotion plays a vital role in human learning, memory and decision-making, which have attracted wide attention in various research fields. To improve the learning speed and performance, a novel Brain Emotional Learning Network (BELN) is developed in this paper, which adds the affective neurons into the model of brain emotional learning and ameliorates the structure of the model. The newly proposed BELN enjoys lower computational complexity, in the sense that two affective coefficients named “anxiety” and “confidence” are added to simulate the changes of emotion in human learning process, which improves the online learning speed of the network. In order to verify the effectiveness of the network, a numerical example and the predictive control of petroleum heating process in a co-current tubular heat exchanger have been studied. The results show that compared with the BP neural network, BELN has better learning speed and recognition ability.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"103 1","pages":"37-41"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80349309","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909007
Jihan Li, Xiaoli Li, Linkun Wang, Yang Li, Kang Wang
To accurately predict the concentration of PM2.5 in the atmosphere, this paper establishes LSSVR prediction model based on historical data of atmospheric PM2.5 concentration. The parameters of LSSVR model are optimized by particle swarm optimization algorithm (PSO). According to PM2.5 concentration data per hour and meteorological conditions from June to August 2017 in Beijing, other PM2.5 concentration prediction models are established, which include ANN prediction model and $varepsilon$-SVR prediction model. By comparing the prediction errors of these three prediction models, the calculated mean absolute error of the ANN prediction model was 25.24%, the mean absolute percent error of $varepsilon$-SVR is 10.39%, and the mean absolute percent error of PSO-LSSVR model is 4.95%. The simulation results show that the PSO-LSSVR model is better than ANN model and $varepsilon$-SVR model, and the PSO-LSSVR model has less computational time and reduces the complexity of the algorithm. Therefore, the proposed PSO-LSSVR algorithm is effective and reliable by predicting PM2.5 concentration.
{"title":"Prediction of PM2.5 Concentration Based on PSO-LSSVR","authors":"Jihan Li, Xiaoli Li, Linkun Wang, Yang Li, Kang Wang","doi":"10.1109/DDCLS.2019.8909007","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909007","url":null,"abstract":"To accurately predict the concentration of PM2.5 in the atmosphere, this paper establishes LSSVR prediction model based on historical data of atmospheric PM2.5 concentration. The parameters of LSSVR model are optimized by particle swarm optimization algorithm (PSO). According to PM2.5 concentration data per hour and meteorological conditions from June to August 2017 in Beijing, other PM2.5 concentration prediction models are established, which include ANN prediction model and $varepsilon$-SVR prediction model. By comparing the prediction errors of these three prediction models, the calculated mean absolute error of the ANN prediction model was 25.24%, the mean absolute percent error of $varepsilon$-SVR is 10.39%, and the mean absolute percent error of PSO-LSSVR model is 4.95%. The simulation results show that the PSO-LSSVR model is better than ANN model and $varepsilon$-SVR model, and the PSO-LSSVR model has less computational time and reduces the complexity of the algorithm. Therefore, the proposed PSO-LSSVR algorithm is effective and reliable by predicting PM2.5 concentration.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"51 1","pages":"723-727"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85163373","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909013
Jing-Ru Su, Jianguo Wang, Zhong-Tao Xie, Yuan Yao, Junjiang Liu
In order to obtain better performance in BCI systems, multi-channel electrodes are often used to collect EEG signals. However, using multi-channel electrodes may cause inconvenience to the EEG signal acquisition work, and may cause problems such as slow system operation and poor performance. This paper proposes a new contributory channel selection method based on data driven method, which realizes the optimal selection of channels by means of the Deep Belief Network with strong learning ability for high-dimensional vectors. First, the DBN model is trained through the continuous adjustment of the parameters, which result in an optimal DBN model. Then, the distribution of the weights in the first layer of the obtained optimal DBN model are analyzed and the channels with larger weights are selected as the optimal channel combination to achieve the purpose of channel selection. The experimental results show that there are different channel selection results among individuals, and the EEG classification accuracy similar to or higher than that of using high-density channels can be obtained by using selected fewer channels, which enhances the practicability of the BCI system.
{"title":"A Method for EEG Contributory Channel Selection Based on Deep Belief Network","authors":"Jing-Ru Su, Jianguo Wang, Zhong-Tao Xie, Yuan Yao, Junjiang Liu","doi":"10.1109/DDCLS.2019.8909013","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909013","url":null,"abstract":"In order to obtain better performance in BCI systems, multi-channel electrodes are often used to collect EEG signals. However, using multi-channel electrodes may cause inconvenience to the EEG signal acquisition work, and may cause problems such as slow system operation and poor performance. This paper proposes a new contributory channel selection method based on data driven method, which realizes the optimal selection of channels by means of the Deep Belief Network with strong learning ability for high-dimensional vectors. First, the DBN model is trained through the continuous adjustment of the parameters, which result in an optimal DBN model. Then, the distribution of the weights in the first layer of the obtained optimal DBN model are analyzed and the channels with larger weights are selected as the optimal channel combination to achieve the purpose of channel selection. The experimental results show that there are different channel selection results among individuals, and the EEG classification accuracy similar to or higher than that of using high-density channels can be obtained by using selected fewer channels, which enhances the practicability of the BCI system.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"1247-1252"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79813988","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909022
Guofa Sun, Hanbo Yu, Wei Wei
This paper proposes an adaptive fuzzy output feedback control approach based on nonlinear tracking differentiator for a class of strict feedback systems with input saturation, unknown nonlinear functions and unmeasurable states. Fuzzy logic systems(FLSs) are used to approximate the unknown nonlinear function and a fuzzy state observer is designed to estimate the unmeasured state of the system. The nonlinear tracking differentiator (TD)is used to estimate the differential of command signal which avoids the problem of “explosion of complexity” in traditional backstepping control. The compensation signal is introduced to eliminate the filtering error caused by the nonlinear tracking differentiator. The proposed approach guarantees stability of the closed-loop system and all signals are bounded. Finally, simulation examples are provided to check the effectiveness of the proposed approach.
{"title":"Adaptive Fuzzy Output Feedback Control for Input-saturated System Based on Nonlinear Tracking Differentiator","authors":"Guofa Sun, Hanbo Yu, Wei Wei","doi":"10.1109/DDCLS.2019.8909022","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909022","url":null,"abstract":"This paper proposes an adaptive fuzzy output feedback control approach based on nonlinear tracking differentiator for a class of strict feedback systems with input saturation, unknown nonlinear functions and unmeasurable states. Fuzzy logic systems(FLSs) are used to approximate the unknown nonlinear function and a fuzzy state observer is designed to estimate the unmeasured state of the system. The nonlinear tracking differentiator (TD)is used to estimate the differential of command signal which avoids the problem of “explosion of complexity” in traditional backstepping control. The compensation signal is introduced to eliminate the filtering error caused by the nonlinear tracking differentiator. The proposed approach guarantees stability of the closed-loop system and all signals are bounded. Finally, simulation examples are provided to check the effectiveness of the proposed approach.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"28 2 1","pages":"1306-1311"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83580366","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908929
Zhonghua Pang, Wentai Song, Wencheng Luo, Cunwu Han, Dehui Sun
With the development of modern industry and information science and technology, modern industrial processes become more and more complex, which brings many challenges for model-based controller design. In this case, data-driven control is a complementary approach to model-based control. This paper proposes an improved model free adaptive control method based on compact format dynamic linearization technique for a class of nonlinear systems. Its control law consists of a time-varying proportional term, a time-varying integral term, and a time-varying derivative term. As a result, compared with the original method where there is only a time-varying integral term, it can strongly improve the dynamical performance of control systems. The effectiveness of the proposed method is demonstrated through simulation results.
{"title":"Improved Model Free Adaptive Control Based on Compact Form Dynamic Linearization","authors":"Zhonghua Pang, Wentai Song, Wencheng Luo, Cunwu Han, Dehui Sun","doi":"10.1109/DDCLS.2019.8908929","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908929","url":null,"abstract":"With the development of modern industry and information science and technology, modern industrial processes become more and more complex, which brings many challenges for model-based controller design. In this case, data-driven control is a complementary approach to model-based control. This paper proposes an improved model free adaptive control method based on compact format dynamic linearization technique for a class of nonlinear systems. Its control law consists of a time-varying proportional term, a time-varying integral term, and a time-varying derivative term. As a result, compared with the original method where there is only a time-varying integral term, it can strongly improve the dynamical performance of control systems. The effectiveness of the proposed method is demonstrated through simulation results.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"54 1","pages":"1301-1305"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88931433","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908932
Hanyu Wang, L. Jia
With the development of electric-power industry, a large amount of historical data of thermal power units are accumulated, conventional optimization methods of operation parameters have the limitations in storage and computation for massive data. To solve the problem, this paper proposes a big data analysis architecture for thermal power based on data processing flow. According to this architecture, a big data mining method for operation parameters optimization based on parallel association rules is presented. Firstly, a new distributed adaptive K-means algorithm is proposed to realize the classification of working conditions based on external constraints, which can improve the computing efficiency and avoid the defect of determining the division number artificially. Then, Spark-based FP-growth algorithm is applied to mine the strong association rules under various working conditions, thus the optimization target values of operation parameters can be obtained by the best strong association rules. Lastly, the excavated optimization target values constitute the historical knowledge database to optimize the real-time operating parameters. The experiment results show that the proposed method in this paper is effective, and can improve the accuracy of operation parameters optimization.
{"title":"Big Data Knowledge Mining Based Operation Parameters Optimization of Thermal Power","authors":"Hanyu Wang, L. Jia","doi":"10.1109/DDCLS.2019.8908932","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908932","url":null,"abstract":"With the development of electric-power industry, a large amount of historical data of thermal power units are accumulated, conventional optimization methods of operation parameters have the limitations in storage and computation for massive data. To solve the problem, this paper proposes a big data analysis architecture for thermal power based on data processing flow. According to this architecture, a big data mining method for operation parameters optimization based on parallel association rules is presented. Firstly, a new distributed adaptive K-means algorithm is proposed to realize the classification of working conditions based on external constraints, which can improve the computing efficiency and avoid the defect of determining the division number artificially. Then, Spark-based FP-growth algorithm is applied to mine the strong association rules under various working conditions, thus the optimization target values of operation parameters can be obtained by the best strong association rules. Lastly, the excavated optimization target values constitute the historical knowledge database to optimize the real-time operating parameters. The experiment results show that the proposed method in this paper is effective, and can improve the accuracy of operation parameters optimization.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"338-343"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89165640","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908869
Qi Bian, Guoshan Zhang
Dynamic 3D reconstruction of human body is a key issue in the field of computer vision, especially in the case that human body is undergoing large deformation. A novel human body 3D reconstruction method using Laplacian deformation is proposed for fine reconstruction in the region of large deformation. The preliminary reconstructed model is firstly achieved through warp field. Then large deformed region is detected and fine reconstruction model is finally obtained by use Laplacian deformation in the detected large deformed region. The proposed method is verified in different datasets for reconstructing the whole human body and the human body parts. Laplacian deformation improves the reconstruction accuracy in the detected large deformed region.
{"title":"Dynamic Human Body 3D Reconstruction using Laplacian Deformation","authors":"Qi Bian, Guoshan Zhang","doi":"10.1109/DDCLS.2019.8908869","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908869","url":null,"abstract":"Dynamic 3D reconstruction of human body is a key issue in the field of computer vision, especially in the case that human body is undergoing large deformation. A novel human body 3D reconstruction method using Laplacian deformation is proposed for fine reconstruction in the region of large deformation. The preliminary reconstructed model is firstly achieved through warp field. Then large deformed region is detected and fine reconstruction model is finally obtained by use Laplacian deformation in the detected large deformed region. The proposed method is verified in different datasets for reconstructing the whole human body and the human body parts. Laplacian deformation improves the reconstruction accuracy in the detected large deformed region.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"64 1","pages":"1091-1096"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86088443","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908981
Huiyu Jin, Yang Chen, Weiyao Lan
The problem of how to replace an existing continuous-time PI controller with a first-order linear active disturbance rejection controller is investigated. A parametric tuning approach, which is based on the parameters of the PI controller, is proposed. With the first-order linear active disturbance rejection controller generated by the approach, the control system can have almost same gain crossover frequency and phase margin to with the PI controller, while have better performance on rejecting measurement noise and attenuating overshoot when phase margin is not enough.
{"title":"Replacing PI Control With First-Order Linear ADRC","authors":"Huiyu Jin, Yang Chen, Weiyao Lan","doi":"10.1109/DDCLS.2019.8908981","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908981","url":null,"abstract":"The problem of how to replace an existing continuous-time PI controller with a first-order linear active disturbance rejection controller is investigated. A parametric tuning approach, which is based on the parameters of the PI controller, is proposed. With the first-order linear active disturbance rejection controller generated by the approach, the control system can have almost same gain crossover frequency and phase margin to with the PI controller, while have better performance on rejecting measurement noise and attenuating overshoot when phase margin is not enough.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"1097-1101"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89461604","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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