摘要
针对在传统的直接转矩控制系统中,定子磁链受定子电阻及积分计算的影响使参数辨识不准确这一点,将一种新型磁链观测器应用于直接转矩控制系统中,取代传统的纯积分器,并进行对比仿真.得知该磁链观测器对定子磁链的观测精度较高,对于电机参数的鲁棒性较好,在电机低速运行时仍能实现对定子磁链准确的观测.在此基础上,提出了一种神经网络辨识电机转速的新方案.该神经网络结构简单,不受电动机负载、参数等影响,通过简单快速的运算,便可得到正确的辨识结果.
In order to improve the inaccurate stator flux identification influenced by stator resistance and integral calculation used in traditional direct torque control （DTC） systems, a kind of new flux observer is applied to DTC system, which replaces the classical pure integrator. Simulation experiment proves that this flux observer holds higher stator flux observation precision, better robustness to motor parameters, and at low speeds, this observer still realizes exact stator flux observation. Furthermore, a new scheme for motor speed identification is proposed based on neural network, which has the characteristics of simple network structure, no influences of motor load and parameters. The correct identification results can be obtained by a simple and fast operation.
出处
《沈阳工业大学学报》
EI
CAS
2006年第5期522-525,共4页
Journal of Shenyang University of Technology
基金
辽宁省自然科学基金资助项目(20032032)
教育部"春晖计划"合作科研项目(Z2005-2-11008)
辽宁省教育厅科研计划项目(20206331)
