EKF and UKF-based estimation of a sensor-less axial flux PM machine under an internal-model control scheme using a SVPWM inverter

This paper presents a comparative estimation study of rotor speed and position of a sensor-less axial flux permanent magnet synchronous motor (AFPMSM) drive system using extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMC-based control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of the two estimation algorithms. The UKF seems to be more promising under noisy conditions. Nevertheless, there is no clear preference for either where steady-state performance is more critical.