This paper presents a practical model predictive control (MPC) framework for gust load alleviation of a flexible flying wing. Both the controller solving and state estimation are based on a reduced-order model, which features a linear parameter-varying (LPV) form, avoiding online linearization and reducing the scale of the corresponding quadratic programming problem. An improved modeling and model reduction process is used to enhance modeling efficiency and ensure that the reduced-order model can accurately capture the rigid-flexible coupled characteristics of the flexible flying wing under arbitrary gusts. By reconstructing the output of the control-oriented model to include both rigid-body motion and flexible vibrations, the rigid-flexible coupled multi-objective control is established as an MPC problem for reference tracking. The online optimization is formulated in a sparse fashion and combined with an iterative algorithm based on predicted trajectories, describing the variation of model dynamics within the prediction horizon more accurately. With a time-varying Kalman estimator for state updating, the closed-loop simulations are performed for gust alleviation performance validation. Additionally, the real-time potential of the proposed MPC framework is demonstrated through Monte Carlo simulations.

中文翻译：

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基于线性参数变化建模和模型预测控制的柔性飞翼的阵风载荷减轻


该文提出了一种用于减轻柔性飞翼阵风载荷的实用模型预测控制 （MPC） 框架。控制器求解和状态估计都基于降阶模型，该模型具有线性参数变化 （LPV） 形式，避免了在线线性化并减小了相应二次规划问题的规模。采用改进的建模和模型约简过程来提高建模效率，确保降阶模型能够准确捕捉任意阵风下柔性飞翼的刚柔耦合特性。通过重构面向控制模型的输出，使其同时包括刚体运动和柔性振动，将刚柔耦合多目标控制建立为参考跟踪的 MPC 问题。在线优化以稀疏方式制定，并与基于预测轨迹的迭代算法相结合，更准确地描述预测范围内模型动力学的变化。使用用于状态更新的时变卡尔曼估计器，执行闭环仿真以验证阵风缓解性能。此外，通过蒙特卡洛仿真证明了所提出的 MPC 框架的实时潜力。