State Estimation of Two-Phase Permanent Magnet Synchronous Motors Using Kalman and Extended Kalman Filters

This article explores the application of Kalman and Extended Kalman Filters (EKF) for state estimation in two-phase permanent magnet synchronous motors. While motor winding currents are directly measurable, additional information is required to determine rotor position and rotational speed. The EKF algorithm addresses this need by providing optimal state estimation through sensor fusion techniques.

The Extended Kalman Filter operates as a recursive filtering algorithm that combines measurement data with system dynamics to estimate hidden states. Implementation typically involves two main stages: prediction (using system model) and update (incorporating measurements). For PMSM applications, the state vector commonly includes position and speed states, while the measurement vector utilizes current readings. The nonlinear system dynamics require Jacobian matrix calculations for linearization at each estimation step.

Through proper implementation of Kalman and EKF algorithms, engineers can achieve accurate state estimation for two-phase PMSMs, enabling improved motor performance monitoring and control system design. The algorithm's recursive nature allows real-time implementation with efficient computational requirements.