Least mean Kurtosis algorithm-based MRAS estimator for speed-sensorless model predictive control of induction motor

Yıldız R., Demir R., Zerdali E., Barut M.

V. International Turkic World Congress on Science and Engineering, Bishkek, Kyrgyzstan, 15 - 17 September 2023, vol.1, pp.80-92

  • Publication Type: Conference Paper / Full Text
  • Volume: 1
  • City: Bishkek
  • Country: Kyrgyzstan
  • Page Numbers: pp.80-92
  • Kayseri University Affiliated: Yes


In this paper, a speed-sensorless model predictive controlled induction motor (IM) drive system is proposed and tested through simulation studies. To perform high-performance IM control, predictive current control (PCC) strategy is chosen since it eliminates weighting factor adjustment. Also, a stator current-based model reference adaptive system (MRAS) is chosen to estimate the speed and flux information due to its simple structure, low computational complexity, and sufficient estimation performance. Different from the current literature, this MRAS estimator uses a least mean Kurtosis (LMK) algorithm optimized by a genetic algorithm (GA) to further improve the speed and flux estimations. The proposed IM drive is tested by the simulation studies under a challenging scenario that includes wide speed range operation of the IM with linear- and step-type load torque variations. The simulation results verify the robustness of the proposed speed-sensorless PCC-based IM drive. Moreover, from the simulation studies, it is clearly seen that the proposed IM drive is reliable for both industrial and electrical vehicle applications.