Optimization of Delayed-State Kalman-Filter-Based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a classical local search and three popular metaheuristics in terms of quality of the final solution for the problem considered in this paper. A novel simple stator-flux-oriented sliding mode (SFO-SM) control scheme is online used in conjunction with the optimized DSKF-based algorithm to improve the robustness of the sensorless IM drive at low speed. The SFO-SM control scheme has closed loops of torque and stator-flux linkage without proportional-plus-integral controllers so that a minimum number of gains has to be tuned.