A novel 6/13-pole hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits strong fault tolerance capability, high efficiency, and large torque density. However, merely few research on speed sensorless control in HEAFFSPMM exists. The speed sensorless control methods based on model reference adaptive system (MRAS) are studied and compared for the machine to improve the stability and reliability of the system and consequently improve the application of machine in control system. Based on the field-oriented control strategy, the MRAS observer of speed is designed and built by applying stator currents, stator flux linkages, and simplified stator currents. The three speed sensorless control algorithms of MRAS are compared and analyzed by using MATLAB/Simulink simulation and dSPACE1104 experimental platform. Results show that the speed sensorless control algorithm based on simplified stator currents has good control performance and high control accuracy. INDEX TERMS Hybrid excitation, axial field flux-switching permanent magnet machine, simplified stator current, model reference adaptive system, and speed sensorless control. NOMENCLATURE u d , u q Stator voltage in d-q axis. u f Excitation voltage. i d , i q Stator current in d-q axis. i f Excitation current. L d , L q Stator inductance in d-q axis. L f Excitation inductance. R s Stator resistance. R f Excitation resistance. T e Electromagnetic torque. T eMAX Maximum value of torque. T eMIN Minimum value of torque. δ Torque ripple. ψ m Flux linkage produced by permanent magnets. ψ d , ψ q d, q-axis component of the stator flux. ω e Electric angular velocity. The associate editor coordinating the review of this manuscript and approving it for publication was Xiaodong Sun .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.