Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
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DOI：
10.1109/lra.2021.3131690
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发表时间：
2022-04-01
影响因子：
5.2
通讯作者：
Scaramuzza, Davide
中科院分区：
文献类型：
--
作者：
Hanover, Drew;Foehn, Philipp;Scaramuzza, Davide
灵活的四旋翼飞行在具有挑战性的环境具有革命性的航运，运输和搜索和救援应用的潜力。非线性模型预测控制（NMPC）最近在敏捷四旋翼控制中显示出有希望的结果，但依赖于高度精确的模型来获得最大的性能。因此，未建模的复杂气动效应、变化的有效载荷和参数失配等形式的模型不确定性将降低系统的整体性能。在这封信中，我们提出了L-1-NMPC，一种新型混合自适应NMPC，用于在线学习模型不确定性并立即补偿它们，以最小的计算开销大大提高了非自适应基线的性能。我们提出的架构可以推广到许多不同的环境，从这些环境中我们可以评估风、未知的有效载荷和高度敏捷的飞行条件。所提出的方法具有极大的灵活性和鲁棒性，在大的未知干扰下，与非自适应NMPC相比，该方法的跟踪误差降低了90%以上，并且不需要任何增益调谐。此外，具有相同增益的相同控制器可以精确地飞行高度敏捷的赛车轨迹，最高速度为70公里/小时，相对于非自适应NMPC基线，跟踪性能提高了约50%。
Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this letter, we propose L-1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline.