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.

中文翻译：

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四轴飞行器的数据支持预测控制

我们研究了基于数据的预测控制 (DeePC) 算法在现实世界纳米四轴飞行器位置控制中的应用。DeePC 算法是一种有限范围的优化控制方法，它使用来自系统的输入/输出测量来预测未来的轨迹，而无需系统识别或状态估计。该算法通过线性组合先前测量的轨迹（运动图元）来预测四轴飞行器的未来轨迹。我们说明了 DeePC 算法的正则化变体的必要性，以处理具有噪声测量的真实四轴飞行器动力学的非线性特性。基于模拟的分析用于深入了解正则化的影响，实验结果验证这些见解可以延续到现实世界的四轴飞行器。而且，我们通过为每个实际执行的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轴飞行器第一性原理模型的模型预测控制进行比较。结果通过真实世界四轴飞行器的成功轨迹跟踪视频进行了演示。