Despun control system for electro-optical tracking imaging based on adaptive neural network

In order to solve the problem of the rotation for imaging picture caused by the azimuth direction change of optical system and imaging device, a despun control system based on adaptive neural network is presented. The despun instruction angle is used as the given position in the system. The difference between two practical angle values obtained by photoelectric encoder is used as the measured velocity value, which is the velocity feedback inner loop. The angle value measured by gyroscope is used as the position feedback value, which is the position outer loop. A second order lead-lag correction method is used in this system, and an adaptive neural network algorithm, which adjusts the control parameters, is also used. The experimental results show that the despun velocity meets the design requirement in shooting. The picture shot is clear. And the despun accuracy (mean square) is 1.4' which decreases by 46% than the traditional error correction method.