Neural networks based electric motor drive for transportation systems

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 the model parameter accuracy. As a result traditional control scheme cannot 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.