We present model predictive selection (MPS), a new method for selecting the stable closed-loop (CL) equilibrium attitude-error quaternion (AEQ) of an uncrewed aerial vehicle (UAV) during the execution of high-speed yaw maneuvers. In this approach, we minimize the cost of yawing measured with a performance figure of merit (PFM) that takes into account both the aerodynamic-torque control input and attitude-error state of the UAV. Specifically, this method uses a control law with a term whose sign is dynamically switched in real time to select, between two options, the torque associated with the lesser cost of rotation as predicted by a dynamical model of the UAV derived from first principles. This problem is relevant because the selection of the stable CL equilibrium AEQ significantly impacts the performance of a UAV during high-speed rotational flight, from both the power and control-error perspectives. To test and demonstrate the functionality and performance of the proposed method, we present data collected during one hundred real-time high-speed yaw-tracking flight experiments. These results highlight the superior capabilities of the proposed MPS-based scheme when compared to a benchmark controller commonly used in aerial robotics, as the PFM used to quantify the cost of flight is reduced by 60.30 %, on average. To our best knowledge, these are the first flight-test results that thoroughly demonstrate, evaluate, and compare the performance of a real-time controller capable of selecting the stable CL equilibrium AEQ during operation.

翻译：我们提出了模型预测选择（MPS）方法，这是一种在无人机执行高速偏航机动时，用于选择稳定闭环平衡姿态误差四元数的新方法。在该方法中，我们最小化通过性能指标（PFM）测量的偏航代价，该指标同时考虑了无人机的气动力矩控制输入和姿态误差状态。具体而言，该方法采用了一种控制律，其项符号可实时动态切换，以在两种选项中选出由基于第一原理推导的无人机动力学模型预测的、与较小旋转代价相关的力矩。该问题具有重要相关性，因为从能耗和控制误差角度出发，稳定闭环平衡姿态误差四元数的选择对无人机高速旋转飞行性能具有显著影响。为测试并验证所提方法的功能与性能，我们展示了在100次实时高速偏航跟踪飞行实验中收集的数据。这些结果表明，与航空机器人领域常用的基准控制器相比，基于MPS的方案具有优越性能，用于量化飞行代价的PFM平均降低了60.30%。据我们所知，这是首次通过飞行试验结果全面展示、评估和比较能够在运行中选择稳定闭环平衡姿态误差四元数的实时控制器性能的研究。