基于位移信号的磁悬浮飞轮转速估计

Estimation of rotor speed using displacement signals in magnetic suspended flywheel

针对测速传感器故障情况下磁悬浮飞轮不平衡振动抑制所需的高精度的转速信息的提取,提出了一种通过预先提取转子位移信号和转速信号构建BP神经网络模型,从而通过位移信号实时估计转速的方法;通过MATLAB/Simulink构建了磁悬浮飞轮系统模型,以仿真得到的位移和转速数据训练出一个神经网络模块,以此实时估计转速,得到恒速和变速两种情形下的转速估计结果,并与测速传感器获得的转速进行比较。仿真和实验结果证明,该转速估计方法在恒速和变速时均估计效果良好,实验估计误差不超过20 r/min。

To acquire the high-precision rotational speed information necessary to suppress unbalanced vibration ina magnetic suspension flywheel under the circumstance of speed sensors failure,a BP neural network model was constructed by pre-extracting the rotor displacement signal and the rotational speed signal to estimate the rotational speed in real time using the displacement signal.The magnetic suspension flywheel system model was constructed using MATLAB/Simulink.A neural network module was trained using simulated displacement and rotational speed data to estimate the rotational speed in real time,and the estimation results under constant speed and variable speed were obtained and compared with the system speed acquired from the speed sensor.The simulation and experimental results show that the speed estimation method demonstrates good estimation accuracy at both constant speed and variable speed,with an estimation error of less than 20 r/min throughout the experiment.