Neural networks based electric motor drive for transportation systems

被引：0

作者：

Chen, ZP ^{[1
]}

Liu, L ^{[1
]}

机构：

[1] Tianjin Univ Technol, Dept Automat, Tianjin, Peoples R China

来源：

2003 IEEE INTELLIGENT TRANSPORTATION SYSTEMS PROCEEDINGS, VOLS. 1 & 2 | 2003年

关键词：

convergent speed; electric machine drive; neural networks; uncertainty parameter;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ac electric motor drives are widely used in applications of electric vehicle and subway transportation. The dynamic performance of ac motor control strongly depends on model parameter accuracy. As a result traditional control scheme can't achieve good performance under uncertainty parameters. In this paper an improved compound gradient vector (ICGV) is investigated and applied in induction motor drive control. The convergent analysis of the algorithm indicates that because the improved compound gradient vector is employed, the convergent speed of the algorithm can outperform that of the BP algorithm. Some simulations have been carried out and the results verify that satisfactory convergent performance and strong robustness are obtained in ac motor drive control involving uncertainty parameters with ICGV algorithm.

引用

页码：1378 / 1383

页数：6

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