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 .

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With the continuous development of computer technology, many institutions in society have higher requirements for the efficiency and reliability of identification systems. In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology. The use of fingerprint and face recognition in biometric technology is a biometric technology that does not constitute an infringement on the human body and is convenient and reliable. The biometric technology has been continuously improved, and the existing biometric technologies are based on unimodal biometric features. The unimodal biometric technology has its own limitations such as proposing single information and checking data affected by the environment, which makes it difficult for the technology to play its advantages in practical applications. In this paper, we use CNN-SRU deep learning to preprocess a large amount of complex data in the perceptual layer. The data collected in the perceptual layer are first transmitted to CNN convolutional neural network for simple classification and analysis and then arrives at the LSTM session to update again and optimize the screening to improve the biometric performance. The results show that the CNN-LSTM, CNN-GRU, and CNN algorithms show a decreasing trend in accuracy under the three error evaluation criteria of RMSE, MAE, and ME, from 0.35 to 0.07, 0.58 to 0.19, and 0.38 to 0.15, respectively. The recognition rate of multifeature fusion can reach 95.2%; the recognition efficiency of the multibiometric authentication system and accuracy rate has been significantly improved. It provides a strong guarantee for the regional standardization, high integration, generalization, and modularization of multibiometric identification system application products.