Study on a new floatation support algorithm based on a 12 m thin primary mirror

Abstract

Floatation support is often used to eliminate the extra interaction forces of a large primary mirror’s fixed points during the mirror’s active correction in order to maintain the mirror’s position in the cell. This paper introduces the principle of floatation support as well as its three force distribution algorithms. A new floatation support algorithm is proposed that directly utilizes the image of the mirror surface instead of the fixed-point feedback interaction forces to calculate the adjusting forces of the actuators. Simulations are conducted on a 1.2 m thin primary mirror to verify and compare the performances of all the algorithms. The results show that the new algorithm is as effective as the best traditional algorithm—it reduces the residual root mean square of the mirror surface to less than 3.5 nm. A performance study of this new algorithm shows that it is more sensitive to the fixed point’s position deviation and the Shack-Hartmann detection error than the force-based algorithm, but, because it does not need force sensors to feed back the interaction forces, it is more helpful in simplifying the hardware requirements of floatation support.