A New Adaptive Neural Integrator for Improving Open-Loop Speed Estimators in Induction Machine Drives

This paper presents a new adaptive integrator to avoid the DC drift phenomena and the initial condition problem typical of open-loop integration methods. This adaptive neural integrator has been applied to an open-loop speed estimator used in a rotor-flux-oriented vector control of an induction machine drive. Simulation and experimental results show the improvement of this new integrator as for the dynamical performance of the drive and the speed accuracy estimation in the low speed region (around 70 rpm). A comparison is then made experimentally with a classical speed estimation using a LP (Low-Pass) filter for integrating.