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 paper, we propose L1-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.
翻译:非线性模型预测控制(NMPC)最近展示了敏捷的孔径控制(NMPC)的可喜结果,但以高度精确的性能模型为依据。因此,以未经模型的复杂空气动力效应、不同有效载荷和参数不匹配等形式出现的模型不确定性将降低整个系统性能。在本文中,我们提议采用L1-NMPC,一个新型的混合适应性适应性NMPC,在网上学习模型不确定性,并立即补偿这些不确定性,大大改进非适应基线的性能,并尽量减少计算间接费用。我们拟议的结构将许多不同的环境概括为我们用来评估风力、未知有效载荷和高度灵活的飞行条件。拟议方法显示了巨大的灵活性和稳健性,90%以上跟踪非适应性NMPC的误差减少,处于大为未知的扰动状态,且没有任何改进。此外,同样获得相同收益的控制器可以精确地飞行高度敏捷的直径直径达70公里/小时的顶级直径直径的直径直径,可以跟踪与非适应性NaptionC基线的50%左右的性能改进性能。