Pub Date : 2017-05-01DOI: 10.1109/DDCLS.2017.8068094
Wenlong Yao, R. Chi, Boyang Li, Ai-ling Chen
Speed sensorless vector control based on ILC of dynamic positioning system propulsion motor for semi-submersible ship is proposed for the problem of speed fluctuation of the semi-submersible ship propulsion motor which is caused by the external sea conditions and the unknown load disturbances. The speed error compensation is introduced in the algorithm, and the periodic torque ripple of the propulsion motor is reduced by utilizing the error trend and the previous error information. The results show that the speed sensorless vector control based on ILC can effectively suppress the torque ripple of the semi-submersible ship propulsion motor and improve the state observation accuracy of the system. It satisfies the steady-state error requirement of the semi-submersible ship propulsion system and the reliability of the system was improved through comparing with the vector control algorithm based on the classical PI control.
{"title":"Propulsion motor vector control based on ILC for dynamic positioning system","authors":"Wenlong Yao, R. Chi, Boyang Li, Ai-ling Chen","doi":"10.1109/DDCLS.2017.8068094","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068094","url":null,"abstract":"Speed sensorless vector control based on ILC of dynamic positioning system propulsion motor for semi-submersible ship is proposed for the problem of speed fluctuation of the semi-submersible ship propulsion motor which is caused by the external sea conditions and the unknown load disturbances. The speed error compensation is introduced in the algorithm, and the periodic torque ripple of the propulsion motor is reduced by utilizing the error trend and the previous error information. The results show that the speed sensorless vector control based on ILC can effectively suppress the torque ripple of the semi-submersible ship propulsion motor and improve the state observation accuracy of the system. It satisfies the steady-state error requirement of the semi-submersible ship propulsion system and the reliability of the system was improved through comparing with the vector control algorithm based on the classical PI control.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278008","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/DDCLS.2017.8068066
Zhiqiang Geng, Huachao Gao, Qunxiong Zhu, Yongming Han
Energy and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency, we put forward an energy analysis and management method based on index decomposition analysis (IDA). The proposed method can reflect the impact of energy usage by integrating the level of energy activity, energy hierarchy and energy intensity effectively. Meanwhile, energy efficiency improvement, energy consumption reduction and energy-savings can be visually disCovered by the proposed method. Finally, the proposed method is applied for energy management and conservation practices of the ethylene production process. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can propose corresponding improvement for the ethylene production.
{"title":"Energy analysis and management method of complex chemical processes based on index decomposition analysis","authors":"Zhiqiang Geng, Huachao Gao, Qunxiong Zhu, Yongming Han","doi":"10.1109/DDCLS.2017.8068066","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068066","url":null,"abstract":"Energy and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency, we put forward an energy analysis and management method based on index decomposition analysis (IDA). The proposed method can reflect the impact of energy usage by integrating the level of energy activity, energy hierarchy and energy intensity effectively. Meanwhile, energy efficiency improvement, energy consumption reduction and energy-savings can be visually disCovered by the proposed method. Finally, the proposed method is applied for energy management and conservation practices of the ethylene production process. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can propose corresponding improvement for the ethylene production.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131867874","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/DDCLS.2017.8068101
Yu Hui, R. Chi
This paper explores the question about iterative learning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterative learning state observer is conducted and updated along the iteration direction. Furthermore, the proposed method has data-driven nature and derives from nonlinear systems directly, where no any model information is required except for the input and output measurements. A simulation case was employed to prove the performance of the given observer.
{"title":"Iterative learning state estimation for nonlinear repetitive process","authors":"Yu Hui, R. Chi","doi":"10.1109/DDCLS.2017.8068101","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068101","url":null,"abstract":"This paper explores the question about iterative learning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterative learning state observer is conducted and updated along the iteration direction. Furthermore, the proposed method has data-driven nature and derives from nonlinear systems directly, where no any model information is required except for the input and output measurements. A simulation case was employed to prove the performance of the given observer.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"566 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761121","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/DDCLS.2017.8068060
Jin Xie, Weisheng Chen, Hao Dai
This paper investigates the problem of the distributed cooperative learning over networks via the wavelet approximation. On the basis of the wavelet approximation (WA) theory, the novel distributed cooperative learning (DCL) method, called DCL-WA, is proposed in this paper. The wavelet series is used to approximate the function of network nodes. For the networked systems, DCL method is used to train the optimal weight coefficient matrices of wavelet series, so as to get the best approximation function of network nodes. An illustrative example is presented to show the efficiency of the proposed strategy.
{"title":"Distributed cooperative learning over networks via wavelet approximation","authors":"Jin Xie, Weisheng Chen, Hao Dai","doi":"10.1109/DDCLS.2017.8068060","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068060","url":null,"abstract":"This paper investigates the problem of the distributed cooperative learning over networks via the wavelet approximation. On the basis of the wavelet approximation (WA) theory, the novel distributed cooperative learning (DCL) method, called DCL-WA, is proposed in this paper. The wavelet series is used to approximate the function of network nodes. For the networked systems, DCL method is used to train the optimal weight coefficient matrices of wavelet series, so as to get the best approximation function of network nodes. An illustrative example is presented to show the efficiency of the proposed strategy.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115330044","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/DDCLS.2017.8068173
Yifei Pan, Zehui Mao, Quan Xiao, Xiao He, Y. Zhang
In this paper, a multi-model data trend prediction method is proposed for marine diesel engine to the prognosis of faults. According to the data characteristics, the discrete wavelet transform is used to process the data, which can eliminate the noise of the high-frequency and retain the low-frequency signal. The auto-regression, the gray model, the BP neural network and the radial-based neural network methods are employed to trend prediction and the results are compared. In terms of convergence speed, the autoregressive model has the best performance of the fault prognosis. In terms of fitting error, the neural network model has the best accuracy.
{"title":"Discrete wavelet transform based data trend prediction for marine diesel engine","authors":"Yifei Pan, Zehui Mao, Quan Xiao, Xiao He, Y. Zhang","doi":"10.1109/DDCLS.2017.8068173","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068173","url":null,"abstract":"In this paper, a multi-model data trend prediction method is proposed for marine diesel engine to the prognosis of faults. According to the data characteristics, the discrete wavelet transform is used to process the data, which can eliminate the noise of the high-frequency and retain the low-frequency signal. The auto-regression, the gray model, the BP neural network and the radial-based neural network methods are employed to trend prediction and the results are compared. In terms of convergence speed, the autoregressive model has the best performance of the fault prognosis. In terms of fitting error, the neural network model has the best accuracy.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040825","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/DDCLS.2017.8068054
Tingting Meng, Wei He, Deqing Huang, Lung-Jieh Yang, Changyin Sun
In this paper, vibration control is addressed for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control into adaptive control, two dual-loop adaptive iterative learning control schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis, (I) the input backlash is tackled; (II) the transverse displacements and the angle displacements are suppressed to zero; and (III) the spatiotemporally varying disturbance and the time-varying disturbance are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.
{"title":"Iterative learning control for a timoshenko beam with input backlash","authors":"Tingting Meng, Wei He, Deqing Huang, Lung-Jieh Yang, Changyin Sun","doi":"10.1109/DDCLS.2017.8068054","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068054","url":null,"abstract":"In this paper, vibration control is addressed for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control into adaptive control, two dual-loop adaptive iterative learning control schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis, (I) the input backlash is tackled; (II) the transverse displacements and the angle displacements are suppressed to zero; and (III) the spatiotemporally varying disturbance and the time-varying disturbance are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369003","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/DDCLS.2017.8068090
Xuhui Bu, Jian Liu, Z. Hou
This paper develops a novel iterative learning parameter identification algorithm for a class of single parameter systems with multi-threshold quantized observations. The identification algorithm is constructed along the iteration axis and it can incorporate the parameter identification ability and the learning ability to deal with unknown time-varying parameters. Based on the recursive form of the estimation error along the iteration axis, it is proved that the convergence of parameter estimation can be guaranteed over the whole finite time interval. A numerical example is given to demonstrate the effectiveness of the algorithms.
{"title":"Iterative learning identification using quantized observations","authors":"Xuhui Bu, Jian Liu, Z. Hou","doi":"10.1109/DDCLS.2017.8068090","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068090","url":null,"abstract":"This paper develops a novel iterative learning parameter identification algorithm for a class of single parameter systems with multi-threshold quantized observations. The identification algorithm is constructed along the iteration axis and it can incorporate the parameter identification ability and the learning ability to deal with unknown time-varying parameters. Based on the recursive form of the estimation error along the iteration axis, it is proved that the convergence of parameter estimation can be guaranteed over the whole finite time interval. A numerical example is given to demonstrate the effectiveness of the algorithms.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526790","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/DDCLS.2017.8068136
Lei Li
This paper investigates a high-order PDα — type iterative learning control strategy for a class of fractional-order linear time-invariant systems with Caputo derivative 0<α<1. On the basis of fractional integration by parts and generalized Young inequality, sufficient convergence condition of the learning control law is established in the sense of Lebesgue-p norm. It is shown that the convergence condition is not only dependent on the fractional-order derivative learning gains, along with the system order, but also dependent on the proportional learning gains and all the matrices associated with the system. Finally, a mumerical example is given to demonstrate the validity of the proposed control law.
{"title":"High-order PDα-type iterative learning control and its Lebesgue-p norm convergence","authors":"Lei Li","doi":"10.1109/DDCLS.2017.8068136","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068136","url":null,"abstract":"This paper investigates a high-order PDα — type iterative learning control strategy for a class of fractional-order linear time-invariant systems with Caputo derivative 0<α<1. On the basis of fractional integration by parts and generalized Young inequality, sufficient convergence condition of the learning control law is established in the sense of Lebesgue-p norm. It is shown that the convergence condition is not only dependent on the fractional-order derivative learning gains, along with the system order, but also dependent on the proportional learning gains and all the matrices associated with the system. Finally, a mumerical example is given to demonstrate the validity of the proposed control law.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121912882","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/DDCLS.2017.8068051
Qi Wu, Haoping Wang, Yang Tian
In this paper, a Model Free Control based Nonlinear Integral Backstepping Control (MFC-NIB) strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller (iP), which is based on model-free theory and whose algebraic estimation technique is replaced by a Time-Delay Estimation(TDE) method is developed. Secondly, to improve the control convergence, the MFC-NIB is studied based on the proposed iP. Finally, to demonstrate the performance and effectiveness of the proposed method MFC-NIB, the simulations with comparisons with iP have been implemented on the referred glycemia regulation systems.
{"title":"Model free control based nonlinear integral-backstepping control for blood glucose regulation","authors":"Qi Wu, Haoping Wang, Yang Tian","doi":"10.1109/DDCLS.2017.8068051","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068051","url":null,"abstract":"In this paper, a Model Free Control based Nonlinear Integral Backstepping Control (MFC-NIB) strategy is developed and applied to blood glucose regulation systems, which is a typical biological system with parameter variations, uncertainties and external disturbances. Firstly, an Intelligent Proportional controller (iP), which is based on model-free theory and whose algebraic estimation technique is replaced by a Time-Delay Estimation(TDE) method is developed. Secondly, to improve the control convergence, the MFC-NIB is studied based on the proposed iP. Finally, to demonstrate the performance and effectiveness of the proposed method MFC-NIB, the simulations with comparisons with iP have been implemented on the referred glycemia regulation systems.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129582706","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/DDCLS.2017.8068053
Xiaochun Lu, J. Fei, Jiao Huang
The velocity tracking problem of wheeled mobile robots (WMRs) which work with repeatable trajectories and different initial errors is discussed in the paper. Three mathematical models of WMR, namely, kinematic model, dynamic model and DC motor driven model, are deduced and the stratagem of fuzzy neural network based adaptive iterative learning control (FNN-AILC), which includes the components of fuzzy neural network, approximation errors and feedback, is presented. The proposed scheme can deal with MIMO system, which is distinguished from previous research work. The simulation is presented and the result verifies the effectiveness of the controller.
{"title":"Fuzzy neural network based adaptive iterative learning control scheme for velocity tracking of wheeled mobile robots","authors":"Xiaochun Lu, J. Fei, Jiao Huang","doi":"10.1109/DDCLS.2017.8068053","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068053","url":null,"abstract":"The velocity tracking problem of wheeled mobile robots (WMRs) which work with repeatable trajectories and different initial errors is discussed in the paper. Three mathematical models of WMR, namely, kinematic model, dynamic model and DC motor driven model, are deduced and the stratagem of fuzzy neural network based adaptive iterative learning control (FNN-AILC), which includes the components of fuzzy neural network, approximation errors and feedback, is presented. The proposed scheme can deal with MIMO system, which is distinguished from previous research work. The simulation is presented and the result verifies the effectiveness of the controller.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126921754","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：