COOPERATIVE OBSTACLE AVOIDANCE CONTROL OF FIN STABILISER BASED ON ADAPTIVE RBF NEURAL NETWORK

Mingxiao Sun, Houxin Lian, Yongde Zhang, and Tiantian Luan

Keywords

Fin stabiliser, adaptive control, RBF neural network, obstacle avoidance, cooperative control

Abstract

The safety of the anti-roll fin during operation is the primary factor in ensuring the effectiveness of ship anti-roll, which involves planning the safe movement area of the fin body when the ship encounters marine obstacles. This paper proposes an adaptive RBF neural network-based collaborative obstacle avoidance control method to ensure the safe obstacle avoidance of anti-roll fins without affecting the anti-roll effect. Firstly, the RBF neural network is used to approximate the uncertainty of the roll motion model, design adaptive weights, handle nonlinear and uncertain links, and improve the anti-interference performance of the system. Subsequently, the maximum fin angle for obstacle avoidance is introduced to solve the safe motion area of the fin body. On this basis, considering the constraints of fin motion and the loss of control torque caused by obstacle avoidance, collaborative control of the front and rear fin motion is carried out to compensate for the loss of roll reduction efficiency.

Important Links:



Go Back