Elokda Ezzat, Coulson Jeremy, Beuchat Paul N, Lygeros John, Dörfler Florian
Automatic Control Lab (IfA) ETH Zürich Zürich Switzerland.
Int J Robust Nonlinear Control. 2021 Dec;31(18):8916-8936. doi: 10.1002/rnc.5686. Epub 2021 Jul 13.
We study the application of a data-enabled predictive control (DeePC) algorithm for position control of real-world nano-quadcopters. The DeePC algorithm is a finite-horizon, optimal control method that uses input/output measurements from the system to predict future trajectories without the need for system identification or state estimation. The algorithm predicts future trajectories of the quadcopter by linearly combining previously measured trajectories (motion primitives). We illustrate the necessity of a regularized variant of the DeePC algorithm to handle the nonlinear nature of the real-world quadcopter dynamics with noisy measurements. Simulation-based analysis is used to gain insights into the effects of regularization, and experimental results validate that these insights carry over to the real-world quadcopter. Moreover, we demonstrate the reliability of the DeePC algorithm by collecting a new set of input/output measurements for every real-world experiment performed. The performance of the DeePC algorithm is compared to Model Predictive Control based on a first-principles model of the quadcopter. The results are demonstrated with a video of successful trajectory tracking of the real-world quadcopter.
我们研究了一种基于数据的预测控制（DeePC）算法在实际纳米四旋翼飞行器位置控制中的应用。DeePC算法是一种有限时域的最优控制方法，它利用系统的输入/输出测量值来预测未来轨迹，而无需进行系统辨识或状态估计。该算法通过线性组合先前测量的轨迹（运动基元）来预测四旋翼飞行器的未来轨迹。我们说明了DeePC算法的正则化变体对于处理实际四旋翼飞行器动力学的非线性以及噪声测量的必要性。基于仿真的分析用于深入了解正则化的效果，实验结果验证了这些见解适用于实际四旋翼飞行器。此外，我们通过为每个实际实验收集一组新的输入/输出测量值来证明DeePC算法的可靠性。将DeePC算法的性能与基于四旋翼飞行器第一原理模型的模型预测控制进行了比较。通过实际四旋翼飞行器成功轨迹跟踪的视频展示了结果。
