An adaptive neural network approach for ship roll stabilization via fin control

Combining the adaptive backstepping technique with neural network, an adaptive neural-network-based fin control design method is proposed for the ship roll stabilization. A barrier Lyapunov function is employed to address the problem of the output constraints, and an adaption mechanism is to solve the problem of unknown parameters. The ship roll stabilization is achieved via a fin control system, where the disturbance is estimated and compensated to improve the robustness of the controller. The stability analysis shows that the proposed approach guarantees all signals in the closed-loop system to be semi-globally, uniformly and ultimately bounded (SGUUB). The simulation results verify the performance and effectiveness of the proposed approach.