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RISS 인기검색어
- 검색키워드
- 저자 : Yikang Zhang (검색결과 5 건)
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Zhang, Xiaoguang,He, Yikang,Hou, Benshuai The Korean Institute of Power Electronics 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.5
In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.
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Zhang, Xiaoguang,Hou, Benshuai,He, Yikang,Gao, Dawei The Korean Institute of Power Electronics 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.5
In this paper, a model predictive torque control (MPTC) without the use of a weighting factor for surface mounted permanent-magnet synchronous machine (SPMSM) drive systems is presented. Firstly, the desired voltage vector is predicted in real time according to the principles of deadbeat torque and flux control. Then the sector of this desired voltage vector is determined. The complete enumeration for testing all of the feasible voltage vectors is avoided by testing only the candidate vectors contained in the sector. This means that only two voltage vectors in the sector need to be tested for selecting the optimal voltage vector in each control period. Thus, the calculation time can be reduced when compared with the conventional enumeration method. On the other hand, a novel cost function that only includes the dq-axis voltage errors between the desired voltage and candidate voltage is designed to eliminate the weighting factor used in the conventional MPTC. Thus, the control complexity caused by the tuning of the weighting factor is effectively decreased when compared with the conventional MPTC. Simulation and experimental investigation have been carried out to verify the proposed method.
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Xiaoguang Zhang,Benshuai Hou,Yikang He,Dawei Gao 전력전자학회 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.5
In this paper, a model predictive torque control (MPTC) without the use of a weighting factor for surface mounted permanentmagnet synchronous machine (SPMSM) drive systems is presented. Firstly, the desired voltage vector is predicted in real time according to the principles of deadbeat torque and flux control. Then the sector of this desired voltage vector is determined. The complete enumeration for testing all of the feasible voltage vectors is avoided by testing only the candidate vectors contained in the sector. This means that only two voltage vectors in the sector need to be tested for selecting the optimal voltage vector in each control period. Thus, the calculation time can be reduced when compared with the conventional enumeration method. On the other hand, a novel cost function that only includes the dq-axis voltage errors between the desired voltage and candidate voltage is designed to eliminate the weighting factor used in the conventional MPTC. Thus, the control complexity caused by the tuning of the weighting factor is effectively decreased when compared with the conventional MPTC. Simulation and experimental investigation have been carried out to verify the proposed method.
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Xiaoguang Zhang,Yikang He,Benshuai Hou 전력전자학회 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.5
In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vectorbased model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.
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A Hybrid Public Opinion Analysis Method Based on Improved Clustering and Mutual Information
Zhiqiang Geng,Xia Tang,Yikang Zhang,Yongming Han 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.3
The Internet is frequently used as a medium for exchange of information and opinions, and it is imperative to conduct public opinion analysis to get people’s opinions well understood and guided. In this paper a hybrid public opinion analysis method based on improved clustering and mutual information is proposed. During feature extraction, the weights of words are modified based on Part-of-Speech Tagging to reduce the dimensions of original texts. As for clustering, a novel density peak algorithm is improved and combined with binary search algorithm to determine the cluster number K and initial centers for KMeans. Then hot words extraction, sentiment analysis and trend analysis for each cluster are processed with mutual information to mine useful knowledge to help decision-making. Extensive experiments are conducted on Hadoop, and the results show that our hybrid Public Opinion Analysis method is quite effective and has certain significance.
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