| With the development of new power electronic component and computertechnology, induction motor field-oriented control (FOC) system is an importantresearch direction as a modern AC drive control and gradually become a researchhotspot. According to induction motor FOC system in Nuclear power station, theresearch on speed controller and speed identification is popular now. In this paper, thestructure and the speed control and speed identification methods of speed sensor-lessFOC system are discussed. The math model of the induction motor is nonlinear, timevarying and coupled. We can find the performance of the system using PID controlleris not ideal because it is influenced by external disturbance or uncertainty. Based onthe neural network with the capability of self-study and self-adaptive we present acombination of neural network with PID controller and speed controller design. Thenew system has a better performance than the PID controller system. It has manyperformances such as faster dynamic response,. Then, intelligent neural network PIDcontroller combined with the based on MRAS-ANN speed estimator is applied in thecontrol system, the neural network approach as a model reference adaptive adjustmentmechanism. Finally, the RBFNN hybrid speed identification method and the abovemethods are compared, the instability Based on the RBFNN vector control system hashigh estimating precision, strong in robustness, strong anti-interference and so on.The design method of the induction motor full digital AC control system is alsointroduced. The software and hardware implementation of the system is discussed indetail. |
