Active fin control for ship stabilization system using heuristic genetic optimization

Abstract

Active fin control is the most effective anti-rolling approach to ship stabilization. However, an accurate model of the whole nonlinear dynamic ship system under random wave or wind impact is difficult to obtain. This study develops an intelligent guarded heuristic genetic algorithm fin controller, including a heuristic genetic algorithm fin controller and a guarded fin controller, to identify the optimum solution for ship stabilization system. In the heuristic genetic algorithm fin controller design, the gradient-descent training is embedded into a traditional genetic algorithm to construct the main controller, which consequently determines the optimum fin control angle in response to uncertainties. To ensure that the system states are around a defined bound region, the proposed system uses a guarded fin controller to adjust the control angle. The stabilization system uses a gyroscope and accelerometer to detect rolling conditions, and the gathered data are fed to an embedded microcontroller to calculate output commands. To verify the performance of the proposed guarded heuristic genetic algorithm fin controller, an analogous two-wheeled robot balance model and experimental platform are adopted in the simulations and experiments as the preliminary tool. Simulations and experimental results confirm the effectiveness of the proposed system, and this study compares its performance with other fin control schemes.