An Adaptive Luenberger Observer for Speed-Sensorless Estimation of Induction Machines

This work investigates the problem of speed sensorless state estimation for induction motors. We first exploit a state transformation for the induction motor model. Based on the new state coordinates, we design a new Luenberger observer, which can provide better dynamic performance compared to baseline algorithm. To address the parameter variation problem, the Lyapunov redesign method is used to achieve an adaptation with respect to the parameter $\alpha$. It is shown that the proposed observer can achieve guaranteed asymptotic stability and readily extend to the time-varying speed case. Advantages of the proposed observer include guaranteed asymptotic stability of estimation errors, parameter a adaptation, and better dynamic performance. Simulation results are presented to validate the proposed method.

这项工作研究了感应电机无速度传感器状态估计的问题。我们首先对感应电机模型进行一种状态变换。基于新的状态坐标，我们设计了一种新的龙贝格观测器，与基准算法相比，它能提供更好的动态性能。为了解决参数变化问题，利用李雅普诺夫重新设计方法实现了对参数\(\alpha\)的自适应。结果表明，所提出的观测器能够实现有保证的渐近稳定性，并且很容易扩展到时变转速的情况。所提出观测器的优点包括估计误差有保证的渐近稳定性、参数\(\alpha\)自适应以及更好的动态性能。给出了仿真结果以验证所提出的方法。