Pub Date : 2017-06-18DOI: 10.1109/ICIEA.2017.8283068
S. Nguyen, Phi-Hung Pham, T. V. Pham, Hoa X. Ha, C. Nguyen, P. Do
Sensorless induction drive systems are more popular due to their reliability and low cost. Therefore, it is very beneficial to use sensorless drive systems where the rotor speed can be estimated by means of an intelligent control algorithm instead of the use of directly measuring methods. This paper presents a method of the online speed estimation for a three-phase induction motor in Indirect Field Oriented Control (IFOC) scheme accompanying an Artificial Neural Network (ANN). The error-back propagation algorithm is used for training the neural network. The error between rotor flux linkages in the adaptive model and the reference model is back propagated to adjust weights of the neural network model to estimate the motor speed. The simulation results obtained using MATLAB/Simulink show that the estimated motor speed always tracks the actual motor speed with very small error as long as the sampling time is small enough and the learning rate can be chosen appropriately.
{"title":"A sensorless three-phase induction motor drive using indirect field oriented control and artificial neural network","authors":"S. Nguyen, Phi-Hung Pham, T. V. Pham, Hoa X. Ha, C. Nguyen, P. Do","doi":"10.1109/ICIEA.2017.8283068","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8283068","url":null,"abstract":"Sensorless induction drive systems are more popular due to their reliability and low cost. Therefore, it is very beneficial to use sensorless drive systems where the rotor speed can be estimated by means of an intelligent control algorithm instead of the use of directly measuring methods. This paper presents a method of the online speed estimation for a three-phase induction motor in Indirect Field Oriented Control (IFOC) scheme accompanying an Artificial Neural Network (ANN). The error-back propagation algorithm is used for training the neural network. The error between rotor flux linkages in the adaptive model and the reference model is back propagated to adjust weights of the neural network model to estimate the motor speed. The simulation results obtained using MATLAB/Simulink show that the estimated motor speed always tracks the actual motor speed with very small error as long as the sampling time is small enough and the learning rate can be chosen appropriately.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133871135","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8283167
Linh V. Nguyen, Nalika Ulapane, J. V. Miró, G. Dissanayake, F. Munoz
This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracies at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.
{"title":"Improved signal interpretation for cast iron thickness assessment based on pulsed eddy current sensing","authors":"Linh V. Nguyen, Nalika Ulapane, J. V. Miró, G. Dissanayake, F. Munoz","doi":"10.1109/ICIEA.2017.8283167","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8283167","url":null,"abstract":"This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracies at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582865","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8283030
Xinkai Chen, Shengjun Wen, Aihui Wang
The micro/nano positioning system discussed in this paper includes a piezo electric actuator (PEA) and flexure-hinge-based positioning mechanism. Due to the existence of the hysteretic nonlinearity in the PEA and the friction in the system, the accurate positioning of the piezo-actuated positioning system calls applicable control schemes for practical applications. To this end, an implementable adaptive controller is developed in the paper, where a parameterized hysteresis model is employed to reduce the computational load. The formulated adaptive control law guarantees the global stability of the controlled positioning system, and the position error can be driven to approach to zero asymptotically. The advantage is that the real values of the parameters of the positioning system neither need to be identified nor measured; only the parameters in the formulation of the controller are estimated online, making online implementation feasible. Experimental results show the effectiveness of the proposed method.
{"title":"Adaptive control for piezo-actuated micro/nano positioning system","authors":"Xinkai Chen, Shengjun Wen, Aihui Wang","doi":"10.1109/ICIEA.2017.8283030","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8283030","url":null,"abstract":"The micro/nano positioning system discussed in this paper includes a piezo electric actuator (PEA) and flexure-hinge-based positioning mechanism. Due to the existence of the hysteretic nonlinearity in the PEA and the friction in the system, the accurate positioning of the piezo-actuated positioning system calls applicable control schemes for practical applications. To this end, an implementable adaptive controller is developed in the paper, where a parameterized hysteresis model is employed to reduce the computational load. The formulated adaptive control law guarantees the global stability of the controlled positioning system, and the position error can be driven to approach to zero asymptotically. The advantage is that the real values of the parameters of the positioning system neither need to be identified nor measured; only the parameters in the formulation of the controller are estimated online, making online implementation feasible. Experimental results show the effectiveness of the proposed method.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194137","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8283168
Xu Luo, Jun Yang
Compared to the instantaneous mobile source tracking, the mobile diffusion source tracking is more difficult. In this paper, we give a study on the mobile diffusion source tracking in sensor networks. The CPA realtime localization method, the centroid realtime localization algorithm, the analytic realtime localization algorithm and the tracking method based on PF(Particle Filter) are presented to solve the mobile diffusion source tracking problem. The preconditions, advantages and deficiencies of the methods are given. The performances of different tracking methods are compared in simulations when node densities and sampling intervals are different. The results show that all the proposed methods are valid, while the tracking method based on PF is the most robust method compared to others.
{"title":"Mobile diffusion source tracking in sensor networks","authors":"Xu Luo, Jun Yang","doi":"10.1109/ICIEA.2017.8283168","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8283168","url":null,"abstract":"Compared to the instantaneous mobile source tracking, the mobile diffusion source tracking is more difficult. In this paper, we give a study on the mobile diffusion source tracking in sensor networks. The CPA realtime localization method, the centroid realtime localization algorithm, the analytic realtime localization algorithm and the tracking method based on PF(Particle Filter) are presented to solve the mobile diffusion source tracking problem. The preconditions, advantages and deficiencies of the methods are given. The performances of different tracking methods are compared in simulations when node densities and sampling intervals are different. The results show that all the proposed methods are valid, while the tracking method based on PF is the most robust method compared to others.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125443464","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8282827
Jiaqi Li, Jin Xiao, Zhijie Zhang, Dan Sun
In this paper, a new positioning method based on magnetic sensor array is proposed, which includes the linear regression algorithm in machine learning, to make the system predict the position of the object in the magnetic field according to the measured data more accurately without large fluctuation caused by noise and surrounding magnetic fields. The feasibility of this method is introduced in the paper and the experiment proves that it could reduce noise and improve positioning accuracy.
{"title":"Positioning method for magnetic sensor array based on linear regression","authors":"Jiaqi Li, Jin Xiao, Zhijie Zhang, Dan Sun","doi":"10.1109/ICIEA.2017.8282827","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282827","url":null,"abstract":"In this paper, a new positioning method based on magnetic sensor array is proposed, which includes the linear regression algorithm in machine learning, to make the system predict the position of the object in the magnetic field according to the measured data more accurately without large fluctuation caused by noise and surrounding magnetic fields. The feasibility of this method is introduced in the paper and the experiment proves that it could reduce noise and improve positioning accuracy.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125766227","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}
As the soaring development of electric vehicles and distributed generation systems, lithium-ion battery has been commonly used for energy storage. Accurate estimation of state of charge (SOC) is crucial for charging or discharging the batteries safely and reliably. However, the SOC is immeasurable and nonlinearly varies with factors (e.g., current rate, battery degeneration, ambient temperature and measurement noise), a reliable and robust algorithm for SOC estimation is accordingly expected. In this paper, an optimal nonlinear observer (ONLO) for SOC estimation is proposed. The particle swarm optimization algorithm is employed to optimize parameters of the nonlinear observer. The proposed approach is verified by experiments performed on INR18650-25R lithium-ion batteries produced by SAMSUMG SDI. Experimental results indicate that the proposed ONLO can accurately estimate the battery SOC with a mean absolute error of 1.8% and a maximum error of less than 6.5%, which are both lower than that of the unscented Kalman filter (UKF). Furthermore, the computation cost of the ONLO is reduced to 30% compared with the UKF.
{"title":"An optimal nonlinear observer for state-of-charge estimation of lithium-ion batteries","authors":"Yong-Liang Tian, Dong Li, Jindong Tian, Bizhong Xia","doi":"10.1109/ICIEA.2017.8282810","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282810","url":null,"abstract":"As the soaring development of electric vehicles and distributed generation systems, lithium-ion battery has been commonly used for energy storage. Accurate estimation of state of charge (SOC) is crucial for charging or discharging the batteries safely and reliably. However, the SOC is immeasurable and nonlinearly varies with factors (e.g., current rate, battery degeneration, ambient temperature and measurement noise), a reliable and robust algorithm for SOC estimation is accordingly expected. In this paper, an optimal nonlinear observer (ONLO) for SOC estimation is proposed. The particle swarm optimization algorithm is employed to optimize parameters of the nonlinear observer. The proposed approach is verified by experiments performed on INR18650-25R lithium-ion batteries produced by SAMSUMG SDI. Experimental results indicate that the proposed ONLO can accurately estimate the battery SOC with a mean absolute error of 1.8% and a maximum error of less than 6.5%, which are both lower than that of the unscented Kalman filter (UKF). Furthermore, the computation cost of the ONLO is reduced to 30% compared with the UKF.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131788812","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8282819
Dong Liu, Weihai Chen, Z. Pei, Jianhua Wang
Brain-computer interfaces (BCIs) have been investigated in recent years to transfer the brain activities to external devices as rehabilitation tools in clinical trials. Here we present a BCI to detect lower-limb movement intention from electroencephalography (EEG) signals, combining movement-related cortical potentials (MRCPs) and sensorymotor rhythms (SMRs) with support vector machine (SVM) classification model. We report analysis of the EEG correlates of five healthy subjects while they perform self-paced ankle dorsiflexion. The average detection accuracy was 0.89 ± 0.04, while the latency was − 0.325 ± 0.127 ms with respect to actual movement onset. The combination of these two features has shown significantly better performance (p < 0.01) than the models using either MRCP or SMR. It is also demonstrated that complementary information was employed to boost the detection performance. The proposed paradigm could be further implemented as a brain switch in neurorehabilitation scenarios.
{"title":"Detection of lower-limb movement intention from EEG signals","authors":"Dong Liu, Weihai Chen, Z. Pei, Jianhua Wang","doi":"10.1109/ICIEA.2017.8282819","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282819","url":null,"abstract":"Brain-computer interfaces (BCIs) have been investigated in recent years to transfer the brain activities to external devices as rehabilitation tools in clinical trials. Here we present a BCI to detect lower-limb movement intention from electroencephalography (EEG) signals, combining movement-related cortical potentials (MRCPs) and sensorymotor rhythms (SMRs) with support vector machine (SVM) classification model. We report analysis of the EEG correlates of five healthy subjects while they perform self-paced ankle dorsiflexion. The average detection accuracy was 0.89 ± 0.04, while the latency was − 0.325 ± 0.127 ms with respect to actual movement onset. The combination of these two features has shown significantly better performance (p < 0.01) than the models using either MRCP or SMR. It is also demonstrated that complementary information was employed to boost the detection performance. The proposed paradigm could be further implemented as a brain switch in neurorehabilitation scenarios.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130272577","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8282868
O. González, J. Rodas, R. Gregor, M. Ayala, M. Rivera
In power electronics, multiphase machines have been recently proposed, where most sensorless algorithms applied to electrical drives are represented through a mathematical representation of the physical system which includes the electrical and mechanical parameters of the motor. However, in electrical drive applications, the rotor current cannot be measured, so it must be estimated. This paper proposes speed sensorless control of a five-phase induction machines by using an inner loop of model-based predictive control and it is obtained from the mathematical model of the machine, using a state-space representation where the two state variables are the stator and rotor currents, respectively. The rotor current is estimated using an optimal reduced order estimator based on a Kalman filter. Simulation results are provided to show the performance of the proposed speed sensorless control algorithm.
{"title":"Speed sensorless predictive current control of a five-phase induction machine","authors":"O. González, J. Rodas, R. Gregor, M. Ayala, M. Rivera","doi":"10.1109/ICIEA.2017.8282868","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282868","url":null,"abstract":"In power electronics, multiphase machines have been recently proposed, where most sensorless algorithms applied to electrical drives are represented through a mathematical representation of the physical system which includes the electrical and mechanical parameters of the motor. However, in electrical drive applications, the rotor current cannot be measured, so it must be estimated. This paper proposes speed sensorless control of a five-phase induction machines by using an inner loop of model-based predictive control and it is obtained from the mathematical model of the machine, using a state-space representation where the two state variables are the stator and rotor currents, respectively. The rotor current is estimated using an optimal reduced order estimator based on a Kalman filter. Simulation results are provided to show the performance of the proposed speed sensorless control algorithm.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132667588","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8282851
Yu-liang Wu, Xiwen Guo, Guoli Li, Chao Lu
The shaft power of the centrifugal pump is one of the important parameters of the reasonable matching of a motor-pump system. Firstly, this paper elaborates the basic principle of the throttle control and the vector control strategy to adjust the speed for changing operation point of the centrifugal pump. Then a comparative analysis of the shaft power of the centrifugal pump is conducted by two regulation methods under the equal flow rate condition. Finally, simulation results show that the vector control speed regulation method requires 37% less shaft power than the throttle control method to drive the centrifugal pump. It can provide reference for the high efficient matching of motor-pump systems.
{"title":"Analysis of shaft power of centrifugal pump under variable speed condition","authors":"Yu-liang Wu, Xiwen Guo, Guoli Li, Chao Lu","doi":"10.1109/ICIEA.2017.8282851","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282851","url":null,"abstract":"The shaft power of the centrifugal pump is one of the important parameters of the reasonable matching of a motor-pump system. Firstly, this paper elaborates the basic principle of the throttle control and the vector control strategy to adjust the speed for changing operation point of the centrifugal pump. Then a comparative analysis of the shaft power of the centrifugal pump is conducted by two regulation methods under the equal flow rate condition. Finally, simulation results show that the vector control speed regulation method requires 37% less shaft power than the throttle control method to drive the centrifugal pump. It can provide reference for the high efficient matching of motor-pump systems.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155093","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 : 2017-06-18DOI: 10.1109/ICIEA.2017.8282804
Haihong Qin, Qing Liu, Ying Zhang, Junyue Yu, Dan Wang, Shishan Wang
As SiC BJT is a current-controlled device, it becomes a trivial issue to achieve both a low power consumption and competitive switching performance. The dual-source base driver is a perfect candidate to achieve these objectives. However, it often exits ringing phenomenon and reversing current phenomenon, which degrades performance. This paper investigates the reasons for above phenomenons. As for ringing, the switching process is established as LCR circuit and suggestions for dual-source driver design are provided. Moreover, this paper discovers the reversing current phenomenon and introduces the new control methods to avoid it. Both ringing and reversing current are validated through LTSPICE simulation and experiment.
{"title":"The optimization design of dual-source driver for SiC BJT","authors":"Haihong Qin, Qing Liu, Ying Zhang, Junyue Yu, Dan Wang, Shishan Wang","doi":"10.1109/ICIEA.2017.8282804","DOIUrl":"https://doi.org/10.1109/ICIEA.2017.8282804","url":null,"abstract":"As SiC BJT is a current-controlled device, it becomes a trivial issue to achieve both a low power consumption and competitive switching performance. The dual-source base driver is a perfect candidate to achieve these objectives. However, it often exits ringing phenomenon and reversing current phenomenon, which degrades performance. This paper investigates the reasons for above phenomenons. As for ringing, the switching process is established as LCR circuit and suggestions for dual-source driver design are provided. Moreover, this paper discovers the reversing current phenomenon and introduces the new control methods to avoid it. Both ringing and reversing current are validated through LTSPICE simulation and experiment.","PeriodicalId":443463,"journal":{"name":"2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128899938","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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