Desensitized Ensemble Kalman Filtering for Induction Motor Estimation

Abstract

The ensemble Kalman filter (EnKF) is a random sampling method based on Monte Carlo and Kalman filter for the extremely high-dimensional nonlinear system. However, the uncertain parameters may be have unexpected effects on the state estimate problem. This paper introduces the desensitized optimal control methodology into the EnKF, and derives the algorithm formula of the proposed desensitized EnKF (DEnKF). A new desensitized cost function is designed by combing the mean square error cost function with the penalty cost function, and the optimal gain is obtained by minimizing the new cost function. The sensitivity of the state errors is propagated by the ensemble error matrix. To demonstrate the effectiveness of the proposed DEnKF, two numerical simulations for a falling body model and induction motor model are designed. The simulation results show that the proposed DEnKF could weaken the sensitivity of state estimate errors to the uncertain parameters and improves the state estimation accuracy, comparing with the standard EnKF.

Keywords

• Ensemble Kalman filter (EnKF)
• desensitized optimal control
• uncertain parameter
• desensitized Kalman filter
• random sample