针对尾座式垂直起降（VTOL）无人机（UAV）在全飞行包线下的轨迹跟踪问题，提出了一种基于神经网络气动预测模型的非线性模型预测控制（NMPC）的轨迹跟踪控制器。所设计的NMPC的气动模型只保留了一个非线性气动系数，以降低优化问题复杂度。神经网络气动模型的辨识过程如下：首先，使用无气动预测模型的NMPC跟踪预设参考圆形轨迹并保持协调飞行，收集在这个过程中的飞行数据并训练神经网络气动模型。随后，依据模型准确度与复杂度2个关键指标，从众多训练出的模型中筛选出帕累托前沿模型组进行轨迹跟踪并评估。仿真实验结果表明，相比其他参数结构模型，提出的神经网络气动NMPC能够有效跟踪预设参考圆形轨迹，大幅增加了轨迹跟踪的准确度并具备实时性，有望进一步部署在实机上。

This paper addresses the trajectory tracking problem for tail-sitter Vertical Takeoff and Landing （VTOL） Unmanned Aerial Vehicles （UAVs） across their entire flight envelope. We propose a trajectory tracking controller based on Nonlinear Model Predictive Control （NMPC） integrated with a neural network aerodynamic predictive model. The aerodynamic model of our designed NMPC reserves only a single nonlinear aerodynamic coefficient， reducing optimization complexity. The identification process for the neural network aerodynamic model involves two key steps： first， an aerodynamic model-free NMPC is employed to track a predefined circular reference trajectory while maintaining coordinated flight. Flight data collected during this process are then used to train the neural network aerodynamic model. Subsequently， a Pareto-optimal set of models is selected based on two critical metrics-prediction accuracy and computational complexity-for trajectory tracking and evaluation. Simulation results demonstrate that compared with other parametric models， the proposed neural network aerodynamic NMPC effectively tracks the reference trajectory， significantly improving tracking accuracy while maintaining real-time computational performance， showing this approach a promising candidate for real-world deployment.