FPGA-based sensorless PMSM speed control using adaptive extended Kalman filter

This paper presents the design and implementation of an adaptive extended Kalman filter (EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the adaptive EKF and their estimates are then used in vector control of the PMSM. Most EKF techniques for state estimation rely on fixed values of the state and measurement noise covariance matrices. In many practical applications, an a priori assumption of these matrices is often inadequate and it is desirable to tune online the process noise covariance to improve the filtering performance. For this, improved EKF versions can be obtained by incorporating an adjustment mechanism of the noise covariances into the filter. The adaptive EKF is, therefore, a promising estimator for sensorless PMSM drives with more accurate estimation features, provided it is feasible in implementation. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine (FSM) method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows the effectiveness of the adaptive EKF-based PMSM speed estimation.