Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

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.