loading page

Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter
  • +1
  • Tai-shan Lou ,
  • Dong-xuan Han ,
  • Xiao-liang Yang ,
  • Su-xia Jiang
Tai-shan Lou
Zhengzhou University of Light Industry

Corresponding Author:[email protected]

Author Profile
Dong-xuan Han
Author Profile
Xiao-liang Yang
Author Profile
Su-xia Jiang
Author Profile


To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.