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，无人机在广泛的实际任务中得到了越来越多的应用。模型预测控制因其对建模误差/不确定性和外部干扰具有鲁棒性，已成为无人机飞行控制的一种实用方法。然而，MPC对人工调参的敏感性可能导致在面临未知环境动态时性能迅速退化。本文解决了无人机穿越具有未知动态的摆动门时的控制挑战。本文提出了一种名为hyMPC的参数化MPC方法，该方法利用高层决策变量适应不确定的环境条件。为推导这些决策变量，提出了一种旨在训练高层高斯策略的新型策略搜索框架。随后，我们利用通过重复执行高斯策略收集的数据训练的神经网络策略，提供实时决策变量。通过数值模拟验证了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，证明了其在环境动态先验知识有限的情况下实现安全精确飞行的能力。