Abstract
The high-power multi-stage coil launcher generates electromagnetic force on armature projectile by capacitor energy storage discharge, which drives it to accelerate, the trigger control of multi-stage coil launcher usually makes the external structure of the launcher complex and unstable by adding position sensors and combining the position signals of emitters. Permanent magnet synchronous motor (PMSM)’s no-position detector control mode, which by establishing an estimation model get speed and position according to voltage and current. There are thyristors and diodes in the driving circuit of the multi-stage coil launcher, which makes the dynamic process nonlinear, and multiple circuits are coupled with the launcher armature. Compared with the permanent magnet synchronous motor, the mathematical model is complex and the order is higher. The method of mathematical model estimation is not suitable for coil launcher. In this paper, a method of using deep learning neural network to predict the speed of transmitting device and realize control is proposed. Through sample learning, the neural network for predicting the speed through the voltage and current signals of discharge is obtained, and the predicted speed signal is compared with the actual speed signal, which achieves a good fit.
Published Version
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