基于神经网络的速度估计方法

Speed Estimation Methods Based on Neural Network

为解决无速度传感器感应电动机矢量控制系统的速度估计问题,以神经网络模式识别的理论为基础,结合神经网络在自动控制领域中的典型应用经验,提出了两种基于神经网络的速度估计方案,分析比较了各自的优点,并通过Matlab仿真,证明了所提方案的估计精度高,估计转速能很好地跟踪实际转速(即使负载发生变化或转速发生阶跃变化),而且对电机参数变化具有很强的鲁棒性,对考虑铁耗后所产生的影响也不太敏感,使相应的无速度传感器矢量控制系统具有良好的静、动态性能。

In order to solve the speed estimation problems of speed sensorless vector-controlled induction motor drives, the paper presents two speed estimation schemes based on neural network mode identification theory. The advantages of each scheme are discussed and the simulation results show that the estimated speed can trace the actual speed better (even under the circumstances of load variation or speed step variation). Also, these schemes are not sensitive to the variations of motor parameters and the effect of iron loss. Therefore, the proposed neural network based on speed sensorless vector-controlled induction motor drives have good performance in stady-state and transient-state operation.