Adaptive Neural Network-Based Tracking Control of Underactuated Offshore Ship-to-Ship Crane Systems Subject to Unknown Wave Motions Disturbances

As a typical underactuated mechanical system, offshore ship-mounted cranes are widely used to carry out the tasks of transferring cargos from one ship to another in the marine environment. Different from land-fixed cranes as well as traditional harbor cranes, offshore ship-to-ship crane systems work in two noninertial (ship) frames, while the target locations of the cargos are also inevitably influenced by the movements of the target ship. Besides, various external disturbances, which are caused by persistent sea waves, sea winds, or currents, etc., bring much more challenges to the control task of offshore ship-to-ship crane systems. To properly address these practical problems, in this article, we propose an increased adaptive neural network (NN)-based anti-swing tracking control strategy for such coordinated offshore crane systems, with a ship-motion prediction algorithm suggested to generate the target trajectories for cargos, and an adaptive NN proposed to deal with complicated unknown wave-induced disturbances. Bounded tracking performance is also guaranteed through a complete Lyapunov-based stability analysis. To the best of our knowledge, without any simplification of the original nonlinear dynamics, this article provides a high-performance adaptive control approach to deal with the anti-interference tracking control problem for offshore ship-to-ship crane systems, which are subjected to trajectories uncertainties as well as unknown wave motions disturbances. Furthermore, comparative hardware experimental results are presented to demonstrate the efficiency of the proposed control method.

Automated summary

The article proposes an enhanced adaptive neural network-based anti-swing tracking control strategy for coordinated offshore crane systems, addressing the control challenges caused by sea wave disturbances. The effectiveness of the method is demonstrated through hardware experimental results.