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In traditional maximum torque per ampere (MTPA) method based on signal injection, fixed wave amplitude and constant integral coefficient are employed, which actually degrade the optimal current angle tracking performance in variable conditions, especially under heavy loads. In order to solve this problem, an improved MTPA method based on amplitude-adjustable square wave injection is proposed. First, the relationships among optimal current angle, square wave amplitude and electromagnetic torque are analyzed. The analysis results indicate that if fixed wave amplitude is adopted, the fluctuations in the obtained current angle increases when load torque rises. Based on this relationship, an amplitude-adjustable square wave virtual signal injection MTPA method is proposed. By adjusting square wave amplitude according to the load torque, steady-state fluctuations of the obtained current angle can be reduced compared to conventional method under heavy loads. Meanwhile, to improve the MTPA dynamic response under load change conditions, an adaptive fuzzy control method is proposed for adjusting integral coefficient. Finally, the validity of the improved MTPA method is verified through experimental results.

Cylindrical shell vibration gyroscope is a kind of solid wave gyroscope which uses the inertia effect of elastic wave to detect the rotation angular velocity. Precision dynamic balance is the key to improve the stability of gyro standing wave vibration. In this paper, precision dynamic mass-stiffness balancing along the working mode axis at the top of the resonator cup-wall is studied. The radial region of different trimming types and the variation of resonator natural frequency and frequency split with trimming hole depth are determined by the simulation. The femtosecond laser precision trimming-ablation experiment verifies the simulation results. The experiment and simulation have good consistency. This paper clearly clarifies the precision dynamic mass-stiffness balancing law of trimming along the working mode axis, which provides a guarantee for high efficiency and high precision trimming.

Phase-to-neutral voltage or neutral-to-virtual neutral voltage zero-crossing points (ZCPs) detection method is usually used for sensorless brushless dc motor commutation control. Unfortunately, neither of them can be realized in lower speed range. In this paper, a simple commutation point detection method is proposed based on detecting inactive phase terminal to dc-link midpoint voltage. It eliminates the requirement of neutral wire or virtual neutral voltage and provides an amplified version of back electromotive force at the ZCPs which makes the lower speed range detection possible. As the speed increases, commutation point error is enlarged due to the low-pass filter. Utilizing the symmetry of the terminal to midpoint voltage, the phase error can be corrected. However, due to the nonlinear relationship between the detected voltage difference and phase error, it is difficult to regulate the error fast and robustly. Therefore, a novel phase regulator based on fuzzy neural network is proposed in this paper with simple structure and learning ability. The validity of the proposed ZCPs detection method and commutation instant shift correction method are verified through experimental results.

Back-EMF zero crossing-based sensorless motor driving method suffers from a starting problem since there is no back-EMF at standstill, while the ${\rm{V}/ }f$ or ${\rm{I}/ }f$ based method is parameter sensitive. Without position sensors, smoothly driving a nonsalient BLDC motor from standstill is difficult. To solve this problem, the relationship between preposition current, energizing time, and transient speed in startup process is analyzed. Based on this relationship, starting from standstill and directly switching to self-synchronization stage can be realized without external-synchronization process. In addition, to lower the back-EMF zero-crossing points (ZCPs) detection speed, the line voltages difference ZCPs detection circuit is designed, and a novel phase error correction method based on a neuro-fuzzy controller is presented to provide accurate commutation point in the high-speed range. The experimental results show that reliable startup and accurate commutation can be guaranteed by the proposed strategy.