基于灵敏度的稳健非线性模型预测控制用于近距海上风力涡轮机检查
Sensitivity-Based Robust NMPC for Close-Proximity Offshore Wind Turbine Inspection with a Tilted Multirotor
AI总结：
本文提出一种基于灵敏度的稳健NMPC方法，用于近距海上风力涡轮机检查，通过在线约束紧缩提升塔筒 clearance 约束的鲁棒性，减少因风况和模型不匹配导致的约束违反问题。
摘要：
近距海上风力涡轮机检查需要在风力和模型不匹配情况下严格控制与大型圆柱结构的间距。名义非线性模型预测控制（NMPC）在质量、惯性、推力效率、阻力或风况与名义假设不一致时可能违反安全约束。本文提出了一种针对倾斜多旋翼的基于灵敏度的稳健NMPC，通过在线约束紧缩来增强塔筒间距约束。一阶参数状态灵敏度提供结构不确定性边际，而有界阵风通过阶段依赖的附加边际处理。该方法仅在名义NMPC中增加灵敏度传播和边际评估，保持递推时间窗优化结构不变。蒙特卡洛评估在500次不确定性实现上对边界关键的螺旋检查轨迹显示，所提控制器在名义NMPC下观察到的间距违反问题得到消除，以适度增加求解时间的代价。
英文摘要：
Close-proximity offshore wind turbine inspection requires strict clearance control around large cylindrical structures under wind and model mismatch. Nominal Nonlinear Model Predictive Control (NMPC) may violate safety constraints when mass, inertia, thrust effectiveness, drag, or wind conditions differ from nominal assumptions. We propose a sensitivity-based robust NMPC for a tilted multirotor that robustifies the tower-clearance constraint via online constraint tightening. First-order parametric state sensitivities provide a structured-uncertainty margin, while bounded gusts are handled by a stage-dependent additive margin. The formulation augments the nominal NMPC with sensitivity propagation and margin evaluation only, leaving the receding-horizon optimization structure unchanged. Monte-Carlo evaluation over 500 uncertainty realizations on a boundary-critical helical inspection trajectory shows that the proposed controller eliminates the clearance violations observed under nominal NMPC at the cost of a moderate increase in solve time.
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