Applying dual extended Kalman filter (DEKF) theory to sensorless control of brushless DC motor (BLDCM)

To our knowledge, no paper in the open literature has applied the DEKF theory to the sensorless control of BLDCM. Subsections 2.1, 2.2 and 2.3 of the full paper explain our new method. Subsection 2.1 explains the principles of the DEKF with the help of Fig. 1. Subsection 2.2 does two things: (1) it designs the observer which contains two extended Kalman filters (EKFs); the two EKFs estimate simultaneously states (rotor speed and rotor position) of BLDCM and its parameters (stator resistance and stator inductance) respectively; (2) at every update of states, the estimated current parameters are used as given inputs to filter the states, and likewise, the estimated current states are used to filter the parameters. With the help of Fig. 2, subsection 2.3 discusses the sensorless control scheme of the BLDCM that contains the observer we designed. Subsection 2.4 presents the simulation results as given in Fig. 4. The results show preliminarily that: (1) the observer is robust to parameter variations, model inaccuracies, process noise and measurement noise; (2) it has high accuracy estimation performance for the states and parameters of BLDCM during both steady-state and dynamic modes of operation.