TR2020-123

Improve Speed Estimation for Speed-Sensorless Induction Machines: A Variable Adaptation Gain and Feedforward Approach


    •  Zhou, L., Wang, Y., "Improve Speed Estimation for Speed-Sensorless Induction Machines: A Variable Adaptation Gain and Feedforward Approach", International Conference on Electrical Machines (ICEM), August 2020.
      BibTeX TR2020-123 PDF
      • @inproceedings{Zhou2020aug,
      • author = {Zhou, Lei and Wang, Yebin},
      • title = {Improve Speed Estimation for Speed-Sensorless Induction Machines: A Variable Adaptation Gain and Feedforward Approach},
      • booktitle = {International Conference on Electrical Machines (ICEM)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-123}
      • }
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  • Research Area:

    Control

This paper investigates speed-sensorless estimation problem for induction machines, aiming to offer a better balance between speed estimation bandwidth and robustness than a classic adaptive full-order observer (AFO). AFO suffers from a trade-off in selecting its speed adaptation gains: large gains for high bandwidth versus low gains for suppression of ripples induced by model mismatches and noises. We propose two revisions on the AFO to relax the trade-off. First is to adopt a variable speed adaptation gain which is large during transient and is small in steady-state. Second is to include a feedforward term in the speed adaptation law to accommodate the rotor’s mechanical dynamics. An iterative tuning method is presented to adjust feedforward gains, addressing the uncertainties in rotor’s inertia and load torque. Experiments show that the proposed method can significantly improve the speed estimation bandwidth while effectively suppressing the fluctuation of the speed estimate during steady state, compared with AFO.