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针对矿用蓄电池机车开关磁阻电机(SRM)位置传感器的使用导致控制系统的可靠性降低的问题,提出基于小脑模型神经网络(CMAC)的无位置传感器转子位置检测技术,建立了SRM转子位置检测的CMAC模型,并给出了具体算法和训练步骤。通过对三相6/4极SRM进行MATLAB仿真实验证明,系统具有较好的动态跟随性和较高的精确度,最大检测误差<2°,减少了系统的硬件提高了控制的可靠性。
Aiming at the problem that the reliability of the control system is reduced due to the use of locomotive switched reluctance motor (SRM) position sensor for locomotive battery, the sensorless rotor position detection technology based on the cerebellar model neural network (CMAC) is proposed and the SRM rotor position detection CMAC model, and gives the specific algorithm and training steps. Through MATLAB simulation of three-phase 6/4 pole SRM, the system has good dynamic following and high accuracy, the maximum detection error is less than 2 °, which reduces the hardware of the system and improves the control reliability.