Neural network approach to gain scheduling for traction control of electrical vehicles

Jieh Shian Young, Yi Pin Lin, Po Wen Shih

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

This paper proposes a gain scheduling approach by neural network to force control of the electric vehicle wheels. To approximate to the reality in simulation, we utilize the traction force database of the motor, called the current-RPM-torque database, instead of the slip ratio measurements. The system is nonlinear and a constant gain cannot overcome all road conditions of the traction force control for the electric vehicles. The appropriate gains for different road conditions can be the training data of the neural network. In this paper, the proper parameters for the RBF neural network are obtained. The appropriate gains which have to fit the assigned specifications in time domain seem to be inverse proportion to the slip ratio slope.

原文English
主出版物標題Mechanical and Electrical Technology V
頁面272-276
頁數5
DOIs
出版狀態Published - 2013 十月 29
事件5th International Conference on Mechanical and Electrical Technology, ICMET 2013 - Chengdu, China
持續時間: 2013 七月 202013 七月 21

出版系列

名字Applied Mechanics and Materials
392
ISSN(列印)1660-9336
ISSN(電子)1662-7482

Other

Other5th International Conference on Mechanical and Electrical Technology, ICMET 2013
國家China
城市Chengdu
期間13-07-2013-07-21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • 引用此

    Young, J. S., Lin, Y. P., & Shih, P. W. (2013). Neural network approach to gain scheduling for traction control of electrical vehicles. 於 Mechanical and Electrical Technology V (頁 272-276). (Applied Mechanics and Materials; 卷 392). https://doi.org/10.4028/www.scientific.net/AMM.392.272