Pub Date : 2017-05-01DOI: 10.1109/DDCLS.2017.8068108
Yao-bin Yue, Ruikun Zhang, Bing Wu, Wei Shao
Permanent magnet synchronous motor (PMSM) is a strongly coupled nonlinear system. In this paper, the speed control of PMSM with the direct torque control (DTC) scheme and SVPWM is studied, where the fractional order calculus theory is used to design the fractional order PIλDμ controller. Simulation results show that the proposed fractional order PID control system has better dynamic performance and capacity of resisting disturbance than the integer order PID controller. In addition, the results provide a theoretical basis and foundation for the development and application of fractional order PIλDμ controller in the PMSM speed control system.
{"title":"Direct torque control method of PMSM based on fractional order PID controller","authors":"Yao-bin Yue, Ruikun Zhang, Bing Wu, Wei Shao","doi":"10.1109/DDCLS.2017.8068108","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068108","url":null,"abstract":"Permanent magnet synchronous motor (PMSM) is a strongly coupled nonlinear system. In this paper, the speed control of PMSM with the direct torque control (DTC) scheme and SVPWM is studied, where the fractional order calculus theory is used to design the fractional order PIλDμ controller. Simulation results show that the proposed fractional order PID control system has better dynamic performance and capacity of resisting disturbance than the integer order PID controller. In addition, the results provide a theoretical basis and foundation for the development and application of fractional order PIλDμ controller in the PMSM speed control system.","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":"130988824","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.2017.8068148
Bo Zhang, Meng Zhou, Min Fan, Zhihong Liu, Qi Han
This paper proposes an overall design for a smart power utilization system, and presents a realizable method based on practices in pilot districts in Chongqing. This design can effectively achieve data transmission and communication among many subsystems, while information management, monitoring, and controlling of smart power utilization districts in the subsystems are divided into different security zones. This system has two outstanding characteristics. One is that monitoring and accurate fault location for user's meters and power distribution equipment are realized through regional power distribution automation. The other is that electric vehicle charge pile management can make full use of peak and valley load shifting and realize efficient coordinate regulation by distribution load. This smart power utilization system has been successfully put into use in Jiaxinqinyuan and Fubaoquan districts in Chongqing.
{"title":"Design and application of smart power utilization system in pilot districts of Chongqing","authors":"Bo Zhang, Meng Zhou, Min Fan, Zhihong Liu, Qi Han","doi":"10.1109/DDCLS.2017.8068148","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068148","url":null,"abstract":"This paper proposes an overall design for a smart power utilization system, and presents a realizable method based on practices in pilot districts in Chongqing. This design can effectively achieve data transmission and communication among many subsystems, while information management, monitoring, and controlling of smart power utilization districts in the subsystems are divided into different security zones. This system has two outstanding characteristics. One is that monitoring and accurate fault location for user's meters and power distribution equipment are realized through regional power distribution automation. The other is that electric vehicle charge pile management can make full use of peak and valley load shifting and realize efficient coordinate regulation by distribution load. This smart power utilization system has been successfully put into use in Jiaxinqinyuan and Fubaoquan districts in Chongqing.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"151 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114048650","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.2017.8068154
S. Bi, Qi Diao, Xiaofeng Chai, Cunwu Han
Habituation is non-associative learning mechanism of biological neurons. This paper studied the simplified description of associative learning mechanism, and based on the classical M-P (McCulloch — Pitts) neuron model, put forward study neurons model with the ability of habituation learning, including habituation neurons. At the same time, in this paper, based on the simplified description of Learning neurons, the mathematical model of habituation neurons is designed, and habituation neurons are applied to deep convolution neural networks. It has been verified by experiment that habituation neurons have typical habituation learning ability, and can optimize the performance of convolution networks.
{"title":"On a neural network model based on non-associative learning mechanism and its application","authors":"S. Bi, Qi Diao, Xiaofeng Chai, Cunwu Han","doi":"10.1109/DDCLS.2017.8068154","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068154","url":null,"abstract":"Habituation is non-associative learning mechanism of biological neurons. This paper studied the simplified description of associative learning mechanism, and based on the classical M-P (McCulloch — Pitts) neuron model, put forward study neurons model with the ability of habituation learning, including habituation neurons. At the same time, in this paper, based on the simplified description of Learning neurons, the mathematical model of habituation neurons is designed, and habituation neurons are applied to deep convolution neural networks. It has been verified by experiment that habituation neurons have typical habituation learning ability, and can optimize the performance of convolution networks.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"13 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":"116681616","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.2017.8068071
Tianrui Chen, Cong Wang
In this paper, an approach for rapid fault detection for a class of nonlinear sampled-data systems is proposed. Firstly, a learning estimator is constructed to capture the unknown system dynamics effects in sampled-data systems. The key issue in the learning process is that partial neural weights will converge into their optimal values based on the deterministic learning theory. Then a knowledge bank can be established, which stores the knowledge of various system dynamics effects, such as the Euler approximation modeling error, effect of the unstructured modeling uncertainty and different faults dynamics. Secondly, by utilizing knowledge bank, a set of estimators are constructed. The learned knowledge can quickly be recalled to compensate the unknown system dynamics effect. As a result, the occurrence of a fault can be rapidly detected. Finally, a rigorous analysis for characterizing the detection capability of the proposed scheme is given. Simulation study is included to demonstrate the effectiveness of the approach.
{"title":"Fault detection for uncertain sampled-data systems via deterministic learning","authors":"Tianrui Chen, Cong Wang","doi":"10.1109/DDCLS.2017.8068071","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068071","url":null,"abstract":"In this paper, an approach for rapid fault detection for a class of nonlinear sampled-data systems is proposed. Firstly, a learning estimator is constructed to capture the unknown system dynamics effects in sampled-data systems. The key issue in the learning process is that partial neural weights will converge into their optimal values based on the deterministic learning theory. Then a knowledge bank can be established, which stores the knowledge of various system dynamics effects, such as the Euler approximation modeling error, effect of the unstructured modeling uncertainty and different faults dynamics. Secondly, by utilizing knowledge bank, a set of estimators are constructed. The learned knowledge can quickly be recalled to compensate the unknown system dynamics effect. As a result, the occurrence of a fault can be rapidly detected. Finally, a rigorous analysis for characterizing the detection capability of the proposed scheme is given. Simulation study is included to demonstrate the effectiveness of the approach.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"24 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":"129595505","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.2017.8068087
M. Liao, Cui Lu, Hong Zhang, Sheng-Jie Wei, Ying Zheng
Automata model based method is widely applied for fault diagnosis of discrete event systems. In practical systems, the occurrences of system events often have fixed order, and the faults may be reCoverable. The traditional automata model cannot handle these problems. In this paper, an automata model containing the information of time sequence is built, which will help to describe the system accurately and simplify the structure of the model. Based on this model, a diagnosis method is proposed to diagnose the faults, which searches for the observable events sequence of the system to obtain diagnosis results. An example indicates that the proposed method can reduce the number of diagnose paths and save diagnosis time compared with the traditional method.
{"title":"Fault diagnosis of discrete event systems with time sequence constraint","authors":"M. Liao, Cui Lu, Hong Zhang, Sheng-Jie Wei, Ying Zheng","doi":"10.1109/DDCLS.2017.8068087","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068087","url":null,"abstract":"Automata model based method is widely applied for fault diagnosis of discrete event systems. In practical systems, the occurrences of system events often have fixed order, and the faults may be reCoverable. The traditional automata model cannot handle these problems. In this paper, an automata model containing the information of time sequence is built, which will help to describe the system accurately and simplify the structure of the model. Based on this model, a diagnosis method is proposed to diagnose the faults, which searches for the observable events sequence of the system to obtain diagnosis results. An example indicates that the proposed method can reduce the number of diagnose paths and save diagnosis time compared with the traditional method.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"38 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":"125862833","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.2017.8068088
Lei Liu, Zejin Feng, Cunwu Han
A class of linear singularly perturbed system and the optimal problem of the upper bound of the perturbed parameter based on the genetic algorithm are considered. Firstly, the problem of the asymptotically stability is studied in the term of Lyapunov stability theory based on the Linear Matrix Inequality (LMI). Then, the standard problem of the upper perturbed parameter to be optimized is presented. Thirdly, the optimization algorithm for the upper bound of the perturbed parameter in the linear singularly perturbed system is given based on the genetic algorithm. Lastly, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed methods.
{"title":"Optmization for the upper bound of the perturbed parameter in singularly perturbed system based on genetic algorithm","authors":"Lei Liu, Zejin Feng, Cunwu Han","doi":"10.1109/DDCLS.2017.8068088","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068088","url":null,"abstract":"A class of linear singularly perturbed system and the optimal problem of the upper bound of the perturbed parameter based on the genetic algorithm are considered. Firstly, the problem of the asymptotically stability is studied in the term of Lyapunov stability theory based on the Linear Matrix Inequality (LMI). Then, the standard problem of the upper perturbed parameter to be optimized is presented. Thirdly, the optimization algorithm for the upper bound of the perturbed parameter in the linear singularly perturbed system is given based on the genetic algorithm. Lastly, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed methods.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"220 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":"122643158","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.2017.8068059
Min Fan, Bo Zhang, Q. Yao, Jianliang Zhang, Darong Huang, Qi Han
In order to guarantee the power quality and the highly efficient operation of the power network, a reliable operating condition recognition system of distribution networks is necessary. To solve the problem of multi-condition recognition, an operating condition recognition system based on the workflow of decision-making tree is proposed. Big data of waveforms acquired by an online recording system is transformed into characteristics through time-domain, frequency-domain and wavelet transformation, and ANN(Artificial Neural Networks) models is automatically built with the training of those characteristics of waveform data. As shown by the experimental results, this recognition system can accurately recognize operating conditions and improve the automatic operating capacity of distribution networks.
{"title":"Smart distribution network operating condition recognition based on big data analysis","authors":"Min Fan, Bo Zhang, Q. Yao, Jianliang Zhang, Darong Huang, Qi Han","doi":"10.1109/DDCLS.2017.8068059","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068059","url":null,"abstract":"In order to guarantee the power quality and the highly efficient operation of the power network, a reliable operating condition recognition system of distribution networks is necessary. To solve the problem of multi-condition recognition, an operating condition recognition system based on the workflow of decision-making tree is proposed. Big data of waveforms acquired by an online recording system is transformed into characteristics through time-domain, frequency-domain and wavelet transformation, and ANN(Artificial Neural Networks) models is automatically built with the training of those characteristics of waveform data. As shown by the experimental results, this recognition system can accurately recognize operating conditions and improve the automatic operating capacity of distribution networks.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"20 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":"126332009","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.2017.8068162
Juan Chen, Dingtao Chao, Qing Guo
A sliding mode control (SMC) method based on loop transfer reCovery (LTR) observer is proposed for the equivalent first-order model of pH neutralization process in this paper. The non-singular linear transformations is also used to make delay-free transform for the time-delay process. At the same time, two observers are designed by using LTR method: one is used to observe the system states and the other is used to estimate the variable of the sliding mode surface which is difficult to obtain. And then, integrator is used to weaken the chattering. The pH process is controlled by sliding mode controller eventually. The simulation results show that the proposed method can solve the problems of nonlinear controlled object, time-delay, and parameter uncertainty existing in the pH process effectively and the system has a strong robustness.
{"title":"Sliding mode control based on LTR observer for PH neutralization process","authors":"Juan Chen, Dingtao Chao, Qing Guo","doi":"10.1109/DDCLS.2017.8068162","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068162","url":null,"abstract":"A sliding mode control (SMC) method based on loop transfer reCovery (LTR) observer is proposed for the equivalent first-order model of pH neutralization process in this paper. The non-singular linear transformations is also used to make delay-free transform for the time-delay process. At the same time, two observers are designed by using LTR method: one is used to observe the system states and the other is used to estimate the variable of the sliding mode surface which is difficult to obtain. And then, integrator is used to weaken the chattering. The pH process is controlled by sliding mode controller eventually. The simulation results show that the proposed method can solve the problems of nonlinear controlled object, time-delay, and parameter uncertainty existing in the pH process effectively and the system has a strong robustness.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"55 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":"133962048","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.2017.8068067
Guojun Li
Iterative learning control demands the same initial state in each iteration, which is equal to the desired state. But this condition is unattainable in practice. This paper addresses the problem of some fixed initial state in iterative learning control for high-order nonlinear system. It presents a new control algorithm. In the process of tracking, this algorithm can rectify the initial errors through a step-by-step rectifying controller. The controller rectifies the xn at first, then xn−1 after finishing the rectifying actions of xn, and so on. All of these rectifying actions are finished in a small interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.
{"title":"High-order iterative learning control for nonlinear systems","authors":"Guojun Li","doi":"10.1109/DDCLS.2017.8068067","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068067","url":null,"abstract":"Iterative learning control demands the same initial state in each iteration, which is equal to the desired state. But this condition is unattainable in practice. This paper addresses the problem of some fixed initial state in iterative learning control for high-order nonlinear system. It presents a new control algorithm. In the process of tracking, this algorithm can rectify the initial errors through a step-by-step rectifying controller. The controller rectifies the xn at first, then xn−1 after finishing the rectifying actions of xn, and so on. All of these rectifying actions are finished in a small interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"7 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":"126389321","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.2017.8068082
Yinlei Wen, Huaguang Zhang, Jinhai Liu, Fangming Li
In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the models and weights. Compared with traditional neural network method, this adaboost based method does not need to adjust the node numbers of neural networks. In addition, it remains accuracy and reduces complexity. Finally, an example is given to demonstrate the effectiveness and advantages of the methods.
{"title":"A novel adaboost based algorithm for processing defect big data","authors":"Yinlei Wen, Huaguang Zhang, Jinhai Liu, Fangming Li","doi":"10.1109/DDCLS.2017.8068082","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068082","url":null,"abstract":"In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the models and weights. Compared with traditional neural network method, this adaboost based method does not need to adjust the node numbers of neural networks. In addition, it remains accuracy and reduces complexity. Finally, an example is given to demonstrate the effectiveness and advantages of the methods.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"2006 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":"128200992","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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