Submarine Low-Noise Optimum Depth Controller Design Based on LQR

Abstract

An optimum manipulating scheme for depth control is key to low noise administration during submarine submerged status. The Linear Quadratic Regulator (LQR) controller for depth change was developed, where the bow plane, stern plane deflection and attack angle absolute value integration during depth change period were selected as low-noise index. The controller performance evaluation function was established by synthetically considering the maneuvering and low-noise indices, and the weighting matrices were optimized by Genetic Algorithm (GA). The results show that after controller optimization, the low-noise index increases by 46.5% and the depth maneuvering index increases by 16.4%. Low-noise optimum manipulation was realized while fully utilizing the maneuvering performance of the submarine. The controller performance can be adjusted between low noise management and depth maneuvering requirement by changing low-noise index proportion in the performance evaluation function.