Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

The state estimation accuracy of the nonlinear induction motor is restricted by parameter uncertainties. To reduce state estimation error sensitivities to parameter uncertainties, we proposed a novel desensitized rank Kalman filter (DRKF) based on a new sensitivity definition. 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. The induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance on mitigating the negative effects of parameter uncertainties.