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首页> 外文期刊>Ocean Engineering >Design and experimental investigation of a GA-based control strategy for a low-speed fin stabilizer
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Design and experimental investigation of a GA-based control strategy for a low-speed fin stabilizer

机译：低速翅片稳定器GA基控制策略的设计与实验研究

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y Fin stabilizers are widely used for to reduce ship rolling. However, reducing rolling motion at low ship speeds remains a challenge because of uncertainties in ship dynamics, transient and nonlinear hydrodynamic forces of fin. Also the phase difference between the force and fin angle is highly sensitive to ship speed. Herein, an improved control strategy is proposed to avoid these problems and to enhance the anti-roll effect of fin stabilizers. A prediction algorithm based on the radial base function artificial neural network (RBF-ANN) is first used to forecast ship rolling motion, and then the disturbing moment and roll time series are estimated. Uncertainty of disturbances in roll dynamics are encapsulated in the predictive algorithm. Moreover, an inversion method based on the genetic algorithm can be applied to minimize differences between the disturbing moment and stabilizing moment. A ship model is introduced to verify the proposed control strategy. Forced roll tests were carried out using the ship model to determine the optimal fin profile and maximum control moment. Anti-roll experiments and simulations were performed to verify the improved control strategy.
机译：Y Fin稳定器广泛用于减少船舶轧制。然而，由于船舶动态，瞬态和非线性流体动力学的不确定性，降低低船速下的轧制运动仍然是一个挑战。此外，力和翅片角之间的相位差对船舶速度非常敏感。在此，提出了一种改进的控制策略以避免这些问题并增强鳍稳定剂的抗腹菌作用。首先使用基于径向基函数人工神经网络（RBF-ANN）的预测算法来预测船舶轧制运动，然后估计扰动力矩和卷筒时间序列。在预测算法中封装了滚动动力学中干扰的不确定性。此外，可以应用基于遗传算法的反演方法来最小化干扰力矩与稳定时刻之间的差异。介绍船舶模型以验证所提出的控制策略。使用船舶模型进行强制滚动测试，以确定最佳的翅片轮廓和最大控制力矩。进行抗辊实验和模拟以验证改进的控制策略。

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