FPGA implementation of extended Kalman filter for speed-sensorless control of induction motors

Abstract

This paper presents a hardware in the loop (HIL) system including the implementation of an Extended Kalman Filter (EKF) based estimator on the Field Programmable Gate Array (FPGA) for speed-sensorless control of IM. The implemented EKF algorithm simultaneously estimates stator currents (1sα and 1sβ), stator fluxes (Ψsα and Ψsβ), rotor angular velocity (wm), and load torque (tL) by assuming that stator voltages and currents are available. The HIL system also includes stator currents and fluxes based IM model which provides actual stator currents to the EKF algorithm and is also utilized to validate the flux, speed and load torque estimations of the implemented EKF algorithm. Virtex-5 VSX110T FPGA evolution board is used for this real-time application. The FPGA board is programmed via Very High Speed Integrated Circuit Hardware Description Language (VHDL) in order to develop both IM model and the EKF algorithm. ISE Design Suit Interface is used as debugger and compiler. The results obtained from the EKF and IM model developed on FPGA are graphically compared to verify the sufficiency of estimation performance of the EKF algorithm and demonstrate that EKF algorithm is implemented successfully with less computational time (less sampling time for each recursive operation) due to the inherent parallel signal processing ability of FPGA.