Speed and Position Sensorless Control of Interior Permanent Magnet Synchronous Motor using Square-root Cubature Kalman filter with Joint Parameter Estimation

In this paper, a Square root Cubature Kalman filter (SCKF) is applied for speed and position estimation in the control of Interior Permanent Magnet Synchronous Motor (IPMSM). Cubature Kalman filter avoids the linearization errors prevailing in the Extended Kalman filter algorithm, and is much easier to tune than the Unscented Kalman filter. The square root version of the Cubature Kalman filter gives an increased numerical stability in the digital implementation of the observer. With an augmented state space model of the system, the proposed observer facilitates joint parameter estimation, wherein load torque and stator resistance are also estimated. Estimated load torque is used in the speed control loop and the estimated resistance is used in tuning the PI controllers for current adaptively. The proposed observer's robustness to parameter variations is demonstrated through the simulations.