A Combined Approach to Identify Induction Machine Parameters and to Design an Extended Kalman Filter for Speed and Torque Estimation

Vector-controlled induction machines (IM) are sensitive to incorrect model parameters and measurement errors when utilized as open-loop torque or speed-sensorless drives. Hence, there are manifold publications available addressing the optimization of the 1M's parameter identification as well as the flux- and speed-observer design. Up to now this was handled as a sequential two-step process. However, measurement uncertainty will adversely affect the 1M identification result and, consequently, the resulting uncertainty regarding the 1M's parameter will then adversely affect the observer design. The innovation of this contribution is to combine the observer design and machine identification by one global optimization step. As machine identification and observer tuning have the same goal, i.e. being able to control a given drive with highest speed and torque accuracy, there is no need to separate them from each other. By doing so, more degrees of freedom are given to the identification process. Moreover, since an extended Kalman filter is applied as flux- and speed-observer, sensor deviations are inherently considered due its working principle. Experimental tests proof the benefits of the proposed approach compared to the state of the art.