Sensorless control of fault-tolerant permanent magnet vernier rim-driven motor based on improved model reference adaptive system

To enhance the accuracy of rotor position and speed estimation in the sensorless vector control system of a fault-tolerant permanent magnet vernier rim-driven motor (FTPMV-RDM), a fuzzy fast super-twisting algorithm based model reference adaptive system (FFSTA-MRAS) method is proposed. A full-order current observer is designed to improve the estimation accuracy in an adjustable model in a FFSTA-MRAS. It incorporates the adjustable model and a calibration link to form a closed-loop estimation by introducing a correction term. The proportional integral (PI) adaptive law in a conventional MRAS is substituted with a fast super-twisting algorithm. To overcome the difficulty of selecting the sliding mode gain parameters in the fast super-twisting algorithm, a fuzzy control algorithm is introduced to obtain a reasonable sliding mode gain in real time, which can solve the contradictory problem of accuracy and chattering. Finally, a hardware experimental platform utilizing a StarSim controller is developed and experimental results demonstrate that the proposed method has superiority in terms of accurate estimation and minimum chattering under both healthy and one phase open-circuit fault conditions.