Adaptive finite-time sliding mode control of coupled motion in ships with liquid tank dynamics using neural networks

This paper proposes an adaptive finite-time sliding-mode control approach based on neural networks to address the coupled motion of ships with liquid tank dynamics. Firstly, a brief analysis of ship sloshing dynamics is conducted to derive the impact of sloshing forces on the governing equations of ship dynamics. Secondly, to achieve effective tracking control, a dynamic positioning model of the ship is established using the Froude-Kryloff assumption, simplifying the handling of complex terms. Next, based on analysis of ship mechanics, the control equations for liquid-carrying ships are derived. Then, a finite-time hierarchical sliding-mode controller is designed to stabilize the system within a global finite time using nonlinear sliding surfaces. Subsequently, to address the unknown upper bounds of the nonlinear sloshing forces and second-order wave forces, an RBF neural network along with an adaptive control method is employed. In addition, the global finite-time stability of the system is proven using Lyapunov stability theory and homogeneity. The significance of these findings lies in their potential to enhance operational stability and safety in maritime applications, providing valuable insights for both research and practical implementation. Finally, numerical simulations are conducted to confirm the effectiveness of the proposed control approach, demonstrating its potential to significantly improve operational stability in various maritime operations.