Deep submergence rescue vehicle docking based on parameter adaptive control with acoustic and visual guidance

Abstract

In view of the difficulties in the attitude determination of wrecked submarine and the automatic attitude matching of deep submergence rescue vehicles during the docking and guidance of a submarine rescue vehicle, this study proposes a docking method based on parameter adaptive control with acoustic and visual guidance. This study omits the process of obtaining the information of the wrecked submarine in advance, thus saving considerable detection time and improving rescue efficiency. A parameter adaptive controller based on reinforcement learning is designed. The S-plane and proportional integral derivative controllers are trained through reinforcement learning to obtain the control parameters in the improvement of the environmental adaptability and anti-current ability of deep submarine rescue vehicles. The effectiveness of the proposed method is proved by simulation and pool tests. The comparison experiment shows that the parameter adaptive controller based on reinforcement learning has better control effect, accuracy, and stability than the untrained control method.