关键词: 模型预测控制, 神经网络, 垂直起降无人机, 轨迹跟踪, 非线性优化

Abstract:

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.

Key words: MPC, Neural Network, VTOL, trajectory tracking, nonlinear optimization