The Application of Permanent Magnet Synchronous Motor with Small Electrical Time Constant in Fiber Positioner

With the development of cutting-edge multi-object spectrographs,fiber positioners located in the focal plane are being scaled down in size,and miniature hollow-cup Permanent Magnet motors are now being considered as a suitable replacement for Faulhaber Precistep stepper motors.However,the small electrical time constant of such coreless motors poses a challenge,as the problem of severe commutation torque ripple in a fiber positioner running a position loop has been tricky.To overcome this challenge,it is advised to increase the Pulse Width Modulation(PWM)frequency as much as possible to mitigate the effects of the current fluctuation.This must be done while ensuring adequate resolution of the PWM generator.By employing a voltage open-loop field-oriented control based on a modulation frequency of 1 MHz,the drive current only costs 25 m A under a 3.3 V power supply.The sine degree of phase current is immaculate,and the repeat positioning accuracy can reach 2μm.Moreover,it is possible to further shrink the bill of devices and the layout area of the Printed Circuit Board,especially in sizesensitive applications.This device has been developed under the new generation of The Large Sky Area MultiObject Fiber Spectroscopic Telescope.

Fault Diagnosis of the LAMOST Fiber Positioner Based on a Long Short-term Memory(LSTM)Deep Neural Network

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)has been in normal operation for more than 10 yr,and routine maintenance is performed on the fiber positioner every summer.The positioning accuracy of the fiber positioner directly affects the observation performance of LAMOST,and incorrect fiber positioner positioning accuracy will not only increase the interference probability of adjacent fiber positioners but also reduces the observation efficiency of LAMOST.At present,during the manual maintenance process of the positioner,the fault cause of the positioner is determined and analyzed when the positioning accuracy does not meet the preset requirements.This causes maintenance to take a long time,and the efficiency is low.To quickly locate the fault cause of the positioner,the repeated positioning accuracy and open-loop calibration curve data of each positioner are obtained in this paper through the photographic measurement method.Based on a systematic analysis of the operational characteristics of the faulty positioner,the fault causes are classified.After training a deep learning model based on long short-term memory,the positioner fault causes can be quickly located to effectively improve the efficiency of positioner fault cause analysis.The relevant data can also provide valuable information for annual routine maintenance methods and positioner designs in the future.The method of using a deep learning model to analyze positioner operation failures introduced in this paper is also of general significance for the maintenance and design optimization of fiber positioners using a similar double-turn gear transmission system.