In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.

翻译：近年来，无人机在现实世界任务中的应用日益广泛。模型预测控制（MPC）因其对建模误差/不确定性和外部干扰的鲁棒性，已成为无人机飞行控制的实用方法。然而，MPC对人工调参参数的敏感性可能导致其在面对未知环境动力学时性能快速退化。本文针对无人机穿越具有未知动力学特性的摆动门这一控制挑战展开研究。我们提出一种名为hyMPC的参数化MPC方法，通过利用高层决策变量适应不确定的环境条件。为获取这些决策变量，本文提出一种新型策略搜索框架，旨在训练高层高斯策略。进而，我们利用在重复执行高斯策略过程中采集的数据训练神经网络策略，以提供实时决策变量。通过数值仿真验证了hyMPC的有效性：在20次穿越摆动门的无人机飞行测试中实现了100%的成功率，展示了其在仅有限了解环境动力学先验知识的情况下实现安全精确飞行的能力。