Low-Speed Convergence Performance Improvement for Sensorless Control of Induction Motors Based on Adaptive Full-Order Observer

The performance of speed-sensorless control of induction motors based on adaptive full-order observer (SCIM_AFO) tends to deteriorate at low speeds. The two challenges leading to this phenomenon have not been well addressed so far. On the one hand, due to the weak back electromotive force, voltage errors (VEs) have a significant effect on speed observation at low speeds. On the other hand, the design of the feedback matrix is limited to ensure stability in low-speed regenerating region. To address the above issues, based on the theoretical analysis of the effect of VEs on current error, the current error used in the speed adaptive law is corrected in this article to reduce the effect of VEs. In addition, under the premise of ensuring stability in low-speed regenerating region, a variable feedback matrix is designed that can be adjusted in real time according to the operating state of the motor, thereby improving the convergence performance of speed identification. Through the above methods, either the steady-state accuracy or dynamic convergence performance of SCIM_AFO at low speeds can be significantly improved. The validity of the proposed method has been verified by experiments.