基于自适应迭代优化FSMO的PMSM无感控制

针对磁链滑模观测器(flux sliding mode observer,FSMO)中存在的抖振以及转子位置估计精度的问题,提出了一种自适应迭代磁链滑模观测器(adaptive iterative flux sliding mode observer,AIFSMO)。首先,构建了以电流和磁链为状态变量的FSMO模型;其次,为了减少计算FSMO最优增益的迭代次数,构建了带有速度和电流的误差准则函数对其进行自适应调整,减少了系统的抖振;最后,与转子速度自适应辨识相结合,设计了改进转子位置估计器,在稳态、转速突变和负载突变3种工况下进行仿真测试。测试结果表明,与基于PLL(phase locked loop,PLL)的FSMO相比,消除了系统的抖振,提高了转子速度和位置的估计精度,在转速和负载突变时使系统的动态性能得到加强,减小了相位延迟。

Improved estimation of rotor position for sensorless control of a PMSM based on a sliding mode observer

This work proposes a new strategy to improve the rotor position estimation of a permanent magnet synchronous motor(PMSM) over wide speed range. Rotor position estimation of a PMSM is performed by using sliding mode observer(SMO). An adaptive observer gain was designed based on Lyapunov function and applied to solve the chattering problem caused by the discontinuous function of the SMO in the wide speed range. The cascade low-pass filter(LPF) with variable cut-off frequency was proposed to reduce the chattering problem and to attenuate the filtering capability of the SMO. In addition, the phase shift caused by the filter was counterbalanced by applying the variable phase delay compensation for the whole speed area. High accuracy estimation result of the rotor position was obtained in the experiment by applying the proposed estimation strategy.