输入约束下船舶横摇混沌运动模糊自适应反步控制

Fuzzy adaptive backstepping control of chaotic roll motion of ships under input constraints

  • 摘要:
    目的 为解决复杂海况中参数激励引发船舶横摇运动混沌现象导致的渐进性倾覆风险,提升航海安全保障能力,提出一种输入约束下船舶横摇混沌运动模糊自适应反步控制方法。
    方法 该方法结合反步法与模糊自适应控制,针对横摇角速度/加速度设计模糊状态观测器进行实时估计,针对未知非线性函数引入模糊逻辑系统进行逼近,针对减摇鳍控制输入问题构建辅助控制系统进行约束。
    结果 仿真实验表明,相较于已有文献的控制方法,本文所提方法平均绝对值误差(MAE)降低47.28%,平均积分绝对值(MIA)降低17.74%,平均全变差(MTV)降低57.20%,收敛时间降低44.62%。
    结论 因此,提方法可有效抑制参数激励型横摇混沌运动,显著增强控制系统鲁棒性、降低倾覆风险,为船舶航行安全提供技术保障。

     

    Abstract:
    Objective To address the risk of progressive capsizing caused by the chaotic roll motion of ships induced by parametric excitation in complex sea conditions, and to enhance the navigational safety, this paper proposes a fuzzy adaptive backstepping control method for regulating chaotic roll motion of ships under input constraints.
    Method Based on the backstepping control framework combined with fuzzy adaptive control technology, this method addresses the strong nonlinearity inherent in the chaotic roll motion of ships. Recognizing that critical state variables, such as roll angular velocity and roll angular acceleration, are difficult to measure directly in real-world applications, a fuzzy state observer is designed to estimate these unmeasurable states in real time, thereby enhancing system observability and reliability. To handle the unknown complex nonlinear functions present in the model, a fuzzy logic system is introduced to provide effective approximation, mitigating the impact of model uncertainty. Additionally, considering the mechanical amplitude limitations of the fin stabilizer in practical engineering, an auxiliary control system is constructed to constrain the control input, ensuring that the control commands remain within the physical execution capabilities of the fin stabilizer and preventing system instability due to input saturation. Finally, Lyapunov-based stability analysis is conducted, and it is rigorously proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, guaranteeing that the tracking error converges to a neighborhood of the origin.
    Results The simulation experiments show that, compared with existing control methods in the literature, the controller proposed in this paper achieves notable improvements in suppressing chaotic oscillations, reducing energy consumption, and accelerating response speed. The mean absolute error (MAE) is reduced by 47.28%, the mean integrated absolute (MIA) by 17.74%, the mean total variation (MTV) by 57.20%, and the convergence time by 44.62%.
    Conclusion The proposed method effectively suppresses the chaotic roll motion induced by parametric excitation, significantly enhances the robustness of the control system under model uncertainties and input constraints, and effectively reduces the risk of progressive capsizing. This provides reliable technical support for ensuring the safe navigation of ships.

     

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