The use of finite-state methods is critical to the development of accurate and efficient inflow models used in rotorcraft flight dynamics simulation and control. Recent work in the finite-state field has allowed for the application of these models to multirotor systems using the adjoint theorem, which involves time delays and adjoint variables. However, the addition of time delays and adjoint variables drives the necessity for the addition of further inflow states to achieve model accuracy. Computation with a higher numbers of inflow states requires greater computing power and therefore limits the ability of real-time analysis. To help mitigate these issues, this paper explores the use of a gradient booted trees in XGBoost^TM, as well as the use of varied, lower state training data and limited higher state training data, to accurately predict the velocity on the lower rotor of a coaxial rotor helicopter. The investigation involves XGBoost hyperparameter searches to determine the best model, variation in training and testing subset splits, and use of validation subset comparisons for identifying the best performing model.

有限状态方法的使用对于开发用于旋翼飞行器飞行动力学仿真和控制的准确有效的流入模型至关重要。最近在有限状态领域的工作允许将这些模型应用于使用伴随定理的多旋翼系统，其中涉及时间延迟和伴随变量。然而，时间延迟和伴随变量的添加推动了添加更多流入状态以实现模型准确性的必要性。具有更多流入状态的计算需要更大的计算能力，因此限制了实时分析的能力。为了帮助缓解这些问题，本文探讨了在 XGBoost ^{TM 中}使用梯度引导树，以及使用各种低状态训练数据和有限的高状态训练数据，以准确预测同轴旋翼直升机下旋翼的速度。调查涉及 XGBoost 超参数搜索以确定最佳模型、训练和测试子集拆分的变化，以及使用验证子集比较来确定性能最佳的模型。