Direct Torque Control of the Genetic Neural Network under the Low-Speed

Lian Zhang; Wei Lai; Jie Li

doi:10.4028/www.scientific.net/AMR.466-467.694

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467Direct Torque Control of the Genetic Neural...

Direct Torque Control of the Genetic Neural Network under the Low-Speed

Abstract:

For the direct torque control of asynchronous motor rapid response and speed ripple, this article proposes the genetic neural network algorithm based on the model of stator flux, to achieve the selecting of switch state under the low-speed. Using the global optimization and search method of genetic algorithm obtains global optimal solution, while the connection weights and network structure learning improve the training effectiveness of neural network, so that the BP network has better adaptive characteristic. The effectiveness of the design is verified by the simulation, and it shows that the speed control system has good dynamic performance and steady state performance under the low-speed.

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Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

694-697

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.694

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Online since:

February 2012

Authors:

Lian Zhang, Wei Lai, Jie Li

Keywords:

Asynchronous Motor, Direct Torque Control (DTC), Genetic Neural Network Algorithm

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References

[1] Jianghua Zhang, Chen Gaohua, Huang Song tao. Direct torque control(DTC) of asynchronous motor[J]. Transactions of China Electrotechnical Scociety, 1999, 14(3): 29-33.

Google Scholar

[2] Deying Gu, Zhengdong Ji, Zhang Ping. Modeling and simulation of asynchronous motors based on simulink[J]. Proceedings of the EPSA, 2003, 15(2): 71-73.

Google Scholar

[3] Kan-jian Zhang, Shu-min Fei. Improved results on passivity analysis of discrete-time stochastic neural networks with time-varying delay. In: Journal of Central South University (Science and Technology), Vol. 40, Suppl. 1, Sep, 2009. pp.63-67.

Google Scholar

[4] Shen Hua-yu, Wang Zhao-xia. Determining the number of BP neural network hidden layer units[J]. Journal of Tian jian University of Technology. 2008, 24(3): 13-15.

Google Scholar

[5] Xiao-fan Li. Research on Speed Sensor-less DTC System of Genetic Neural Network[J]. Journal of Yancheng Institute of Technology(Natural Science Edition), 2009, 22(3): 47-50.

Google Scholar

[6] Yang Hua-fen, Yang You, Shang Jin. An Improved Evolution Optimiza tion of Neural Network[J]. Journal of Zhengzhou University (Engineer ing Sc ience). 2010, 31(5): 116-120.

Google Scholar

[7] Wei x, Chen DY, Zhao CY. Minimization of torque ripple of direct-torque-controlled induction machines by improved discrete space vector modulation[J]. Electric Power Systems Research (S0378-7796), 2004, 72(8): 103-112.

DOI: 10.1016/j.epsr.2004.03.004

Google Scholar