Pub Date : 2017-05-28DOI: 10.1109/CCDC.2017.7978700
Qiangqiang Cui, Zhiheng Li, Jun Yang, Bin Liang
Rolling bearing devices are widely used in almost all industries in the world, and play a very critical role. So that once this critical device fails, the whole system will have a very serious impact. It will not only affect the performance of the entire system, but also the system reliability, security, applicability and so on. Therefore, it is very important to predict the bearing failure. Because recurrent neural network is quite effective in dealing with sequence problems, it is often used to do prediction-related problems. And in recent years, recurrent neural network has been put into great attention, so here we choose to use RNN for rolling bearing fault prognosis. Afterwards, we use the actual rolling bearing fault data to verify the effectiveness of our method.
{"title":"Rolling bearing fault prognosis using recurrent neural network","authors":"Qiangqiang Cui, Zhiheng Li, Jun Yang, Bin Liang","doi":"10.1109/CCDC.2017.7978700","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978700","url":null,"abstract":"Rolling bearing devices are widely used in almost all industries in the world, and play a very critical role. So that once this critical device fails, the whole system will have a very serious impact. It will not only affect the performance of the entire system, but also the system reliability, security, applicability and so on. Therefore, it is very important to predict the bearing failure. Because recurrent neural network is quite effective in dealing with sequence problems, it is often used to do prediction-related problems. And in recent years, recurrent neural network has been put into great attention, so here we choose to use RNN for rolling bearing fault prognosis. Afterwards, we use the actual rolling bearing fault data to verify the effectiveness of our method.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"55 1","pages":"1196-1201"},"PeriodicalIF":0.0,"publicationDate":"2017-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83180437","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 : 2017-05-28DOI: 10.1109/CCDC.2017.7978059
Zhang Yang, Wuwenhai, Wang Jie, Yu Liang
For a class of nonlinear systems with constraints, a full-state constrained prescribed performance controller based on the command filtered is designed. By combining the adaptive backstepping technique, the constraint command filter is adopted to adapt the constraint and avoid the expansion of the calculation. Pseudo-control hedging techniques are combined with command filters to compensate for errors. And the full state of transient performance of the tracking error is analyzed. Based on the Lyapunov stability theory, the controller design is proposed. Simulation interpretation and verification methods.
{"title":"A full state constrained prescribed performance controller design with constraints","authors":"Zhang Yang, Wuwenhai, Wang Jie, Yu Liang","doi":"10.1109/CCDC.2017.7978059","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978059","url":null,"abstract":"For a class of nonlinear systems with constraints, a full-state constrained prescribed performance controller based on the command filtered is designed. By combining the adaptive backstepping technique, the constraint command filter is adopted to adapt the constraint and avoid the expansion of the calculation. Pseudo-control hedging techniques are combined with command filters to compensate for errors. And the full state of transient performance of the tracking error is analyzed. Based on the Lyapunov stability theory, the controller design is proposed. Simulation interpretation and verification methods.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"15 1","pages":"18-22"},"PeriodicalIF":0.0,"publicationDate":"2017-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88034290","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 : 2017-05-28DOI: 10.1109/CCDC.2017.7978534
W. Zhou, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi, Wenyou Du
Diabetic retinopathy (DR) is a serious diabetic complication, and Microaneurysm (MA) is the earliest lesion in diabetic retinopathy, so early MA detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose the Joint Dynamic Sparse Representation (JDSR) algorithm with multiple-channel multiple-feature dictionaries. Candidates for MA are first extracted as small image blocks; then we develop the multiple-channel multiple-feature dictionaries for candidate representation. Next, sparse coefficient can be obtained by the proposed JDSR algorithm which can be used for classification. Additionally, in order to form an optimal dictionary, the group sparsity dictionary selection method is also introduced. We evaluate our algorithm by comparing it with other state-of-the-art algorithms. Extensive experiment results on ROC database demonstrate the effectiveness of the proposed algorithm.
{"title":"Automatic microaneurysm detection of diabetic retinopathy in fundus images","authors":"W. Zhou, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi, Wenyou Du","doi":"10.1109/CCDC.2017.7978534","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978534","url":null,"abstract":"Diabetic retinopathy (DR) is a serious diabetic complication, and Microaneurysm (MA) is the earliest lesion in diabetic retinopathy, so early MA detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose the Joint Dynamic Sparse Representation (JDSR) algorithm with multiple-channel multiple-feature dictionaries. Candidates for MA are first extracted as small image blocks; then we develop the multiple-channel multiple-feature dictionaries for candidate representation. Next, sparse coefficient can be obtained by the proposed JDSR algorithm which can be used for classification. Additionally, in order to form an optimal dictionary, the group sparsity dictionary selection method is also introduced. We evaluate our algorithm by comparing it with other state-of-the-art algorithms. Extensive experiment results on ROC database demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"167 2 Suppl 1","pages":"7453-7458"},"PeriodicalIF":0.0,"publicationDate":"2017-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83356537","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 : 2017-05-28DOI: 10.1109/CCDC.2017.7978067
Liying Sun, Yan Zhao
For the Multi-Machine power system with STATCOM, it is equivalent to the two machine system. A nonlinear robust STATCOM controller is designed by using the improved backstepping method, immersion and invariant adaptive control and sliding mode control. Firstly, the parameter substitution law of damping coefficient is replaced by the immersion and invariant adaptive control. Then, based on the traditional backstepping method, the k-class function is added to improve the convergence rate of the system. And, in the last step of the backstepping design, the sliding mode control is added to enhance the robustness of the controller. The simulation results show that the STATCOM controller is more robust and adaptive ability, accelerates the convergence speed of the system, and obviously improves the stability of the multi-machine power system.
{"title":"A novel nonlinear adaptive robust control for multi-machine power system with STATCOM","authors":"Liying Sun, Yan Zhao","doi":"10.1109/CCDC.2017.7978067","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978067","url":null,"abstract":"For the Multi-Machine power system with STATCOM, it is equivalent to the two machine system. A nonlinear robust STATCOM controller is designed by using the improved backstepping method, immersion and invariant adaptive control and sliding mode control. Firstly, the parameter substitution law of damping coefficient is replaced by the immersion and invariant adaptive control. Then, based on the traditional backstepping method, the k-class function is added to improve the convergence rate of the system. And, in the last step of the backstepping design, the sliding mode control is added to enhance the robustness of the controller. The simulation results show that the STATCOM controller is more robust and adaptive ability, accelerates the convergence speed of the system, and obviously improves the stability of the multi-machine power system.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"60 1","pages":"63-67"},"PeriodicalIF":0.0,"publicationDate":"2017-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85871350","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7979229
Shuai Zhao, Bing Song, H. Shi
Quality-related fault detection has received extensive attention in recent years. It requires an appropriate supervisory relationship between process variables and quality variables. While the traditional principal component analysis (PCA) doesn't consider the relationships between them. Thus we proposed the mutual information principal component analysis (MIPCA) to detect the quality-related faults. MIPCA fully integrates the advantages of mutual information (MI) and PCA. With MIPCA, process variables can be utilized to monitor the process under the supervision of quality variables and judge a fault is whether related to the quality or not. Finally, the feasibility and effectiveness of the MIPCA are verified in Tennessee Eastman Process (TEP).
{"title":"Quality-related fault detection based on mutual information principal component analysis","authors":"Shuai Zhao, Bing Song, H. Shi","doi":"10.1109/CCDC.2017.7979229","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7979229","url":null,"abstract":"Quality-related fault detection has received extensive attention in recent years. It requires an appropriate supervisory relationship between process variables and quality variables. While the traditional principal component analysis (PCA) doesn't consider the relationships between them. Thus we proposed the mutual information principal component analysis (MIPCA) to detect the quality-related faults. MIPCA fully integrates the advantages of mutual information (MI) and PCA. With MIPCA, process variables can be utilized to monitor the process under the supervision of quality variables and judge a fault is whether related to the quality or not. Finally, the feasibility and effectiveness of the MIPCA are verified in Tennessee Eastman Process (TEP).","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"30 1","pages":"4163-4167"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73476053","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7978772
Ge Yang, Qin Ming Jie, Niu Tao
When the disturbance of ship is compensated, it can't get the specific motion parameters in time. In order to solve this problem, the motion attitude needs to be predicted in advance, and the reliable data also needs to be provided for the wave compensation system. This paper introduces a method of motion attitude prediction based on BP neural network. The method solves the learning problem of hidden layer by selecting the BP neural network model, and the results show that using such method can effectively improve the prediction speed and precision of motion attitude.
{"title":"Prediction of ship motion attitude based on BP network","authors":"Ge Yang, Qin Ming Jie, Niu Tao","doi":"10.1109/CCDC.2017.7978772","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978772","url":null,"abstract":"When the disturbance of ship is compensated, it can't get the specific motion parameters in time. In order to solve this problem, the motion attitude needs to be predicted in advance, and the reliable data also needs to be provided for the wave compensation system. This paper introduces a method of motion attitude prediction based on BP neural network. The method solves the learning problem of hidden layer by selecting the BP neural network model, and the results show that using such method can effectively improve the prediction speed and precision of motion attitude.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"21 1","pages":"1596-1600"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73508113","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7978157
Huabo Liu, Haisheng Yu
A decentralized state estimator based on a robust estimation framework is derived for a spatially interconnected system constituting by many arbitrarily interconnected subsystems. It combines the robust estimation framework in [21] and the decentralized estimation design in [22] whose computation efficiently utilizes the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Numerical simulations show that the derived decentralized state estimator has nice estimation performance.
{"title":"Decentralized state estimation for large-scale spatially interconnected systems using a robust estimation framework","authors":"Huabo Liu, Haisheng Yu","doi":"10.1109/CCDC.2017.7978157","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978157","url":null,"abstract":"A decentralized state estimator based on a robust estimation framework is derived for a spatially interconnected system constituting by many arbitrarily interconnected subsystems. It combines the robust estimation framework in [21] and the decentralized estimation design in [22] whose computation efficiently utilizes the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Numerical simulations show that the derived decentralized state estimator has nice estimation performance.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"58 1","pages":"5561-5566"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73833096","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7979394
Jiong Ma, Zhenxing Sun, Shihua Li
In this paper, the method of improving the performance of permanent magnet synchronous motor in the presence of disturbance and friction is studied. First, collected data are used to train BP neural network to get an accurate friction model. Friction model is used to compensate the friction. Considering the influence of friction over-compensation or less-compensation and external disturbance, the disturbance observer is used to compensate the disturbance. Finally, the simulation analysis of the proposed compensation method shows that the proposed method based on the neural network and the disturbance observer can improve the position and velocity tracking accuracy.
{"title":"Servo control method based on neural network and disturbance observation","authors":"Jiong Ma, Zhenxing Sun, Shihua Li","doi":"10.1109/CCDC.2017.7979394","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7979394","url":null,"abstract":"In this paper, the method of improving the performance of permanent magnet synchronous motor in the presence of disturbance and friction is studied. First, collected data are used to train BP neural network to get an accurate friction model. Friction model is used to compensate the friction. Considering the influence of friction over-compensation or less-compensation and external disturbance, the disturbance observer is used to compensate the disturbance. Finally, the simulation analysis of the proposed compensation method shows that the proposed method based on the neural network and the disturbance observer can improve the position and velocity tracking accuracy.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"7 1","pages":"5066-5071"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75161195","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7979270
Shaoping Chang, Wuxi Shi
This paper presents an adaptive fuzzy time-varying sliding mode control scheme for quadrotor unmanned aerial vehicle (UAV) attitude system with prescribed performance. A performance function is used and an error transformation is provided to transform the original constrained nonlinear system into an equivalent unconstrained one. Fuzzy systems are used to approximate unknown nonlinear functions of the attitude system. To eliminate the reaching phase which can make the control is robust with respect to external disturbances and parameter uncertainties from the very beginning, a time-varying fast terminal sliding mode surface is designed by the transformed error and a tracking differentiator. Then by using the sliding mode surface, the controller is developed. The proposed scheme guarantees that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds in finite time. Simulation results are used to demonstrate the effectiveness of the proposed scheme.
{"title":"Adaptive fuzzy time-varying sliding mode control for quadrotor UAV attitude system with prescribed performance","authors":"Shaoping Chang, Wuxi Shi","doi":"10.1109/CCDC.2017.7979270","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7979270","url":null,"abstract":"This paper presents an adaptive fuzzy time-varying sliding mode control scheme for quadrotor unmanned aerial vehicle (UAV) attitude system with prescribed performance. A performance function is used and an error transformation is provided to transform the original constrained nonlinear system into an equivalent unconstrained one. Fuzzy systems are used to approximate unknown nonlinear functions of the attitude system. To eliminate the reaching phase which can make the control is robust with respect to external disturbances and parameter uncertainties from the very beginning, a time-varying fast terminal sliding mode surface is designed by the transformed error and a tracking differentiator. Then by using the sliding mode surface, the controller is developed. The proposed scheme guarantees that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds in finite time. Simulation results are used to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"8 1","pages":"4389-4394"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75286389","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 : 2017-05-01DOI: 10.1109/CCDC.2017.7978272
Taomei Lv, Fannian Meng, Jiangping Tao
Thermal error is always the key factor which affects processing precision of high-speed machine center. How to predict the thermal error of the high-speed machine center, is the prerequisite and foundation of thermal error compensation. To solve this problem, a grey bootstrap model is proposed, which is first used thermal error prediction of high-speed machine center. Experimental study shows that the prediction accuracy is very high using grey bootstrap model, and the maximum, the minimum and the mean of the relative errors of the predicted results are respectively 7.72%, 1.19% and 4.48%, and the reliability of the predicted interval is proved to be 100%. The point prediction and interval prediction are actualized, which solve the problem of dynamic evaluation of thermal error of high-speed machine center.
{"title":"Dynamic prediction for thermal error of high-speed machine center using grey bootstrap","authors":"Taomei Lv, Fannian Meng, Jiangping Tao","doi":"10.1109/CCDC.2017.7978272","DOIUrl":"https://doi.org/10.1109/CCDC.2017.7978272","url":null,"abstract":"Thermal error is always the key factor which affects processing precision of high-speed machine center. How to predict the thermal error of the high-speed machine center, is the prerequisite and foundation of thermal error compensation. To solve this problem, a grey bootstrap model is proposed, which is first used thermal error prediction of high-speed machine center. Experimental study shows that the prediction accuracy is very high using grey bootstrap model, and the maximum, the minimum and the mean of the relative errors of the predicted results are respectively 7.72%, 1.19% and 4.48%, and the reliability of the predicted interval is proved to be 100%. The point prediction and interval prediction are actualized, which solve the problem of dynamic evaluation of thermal error of high-speed machine center.","PeriodicalId":6588,"journal":{"name":"2017 29th Chinese Control And Decision Conference (CCDC)","volume":"140 1","pages":"6130-6133"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75647559","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：