Data-enabled Predictive Control (DeePC) is a powerful data-driven approach for predictive control without requiring an explicit system model. However, its high computational cost limits its applicability to real-time robotic systems. For robotic applications such as motion planning and trajectory tracking, real-time control is crucial. Nonlinear DeePC either relies on large datasets or learning the nonlinearities to ensure predictive accuracy, leading to high computational complexity. This work introduces contextual sampling, a novel data selection strategy to handle nonlinearities for DeePC by dynamically selecting the most relevant data at each time step. By reducing the dataset size while preserving prediction accuracy, our method improves computational efficiency, of DeePC for real-time robotic applications. We validate our approach for autonomous vehicle motion planning. For a dataset size of 100 sub-trajectories, Contextual sampling DeePC reduces tracking error by 53.2 % compared to Leverage Score sampling. Additionally, Contextual sampling reduces max computation time by 87.2 % compared to using the full dataset of 491 sub-trajectories while achieving comparable tracking performance. These results highlight the potential of Contextual sampling to enable real-time, data-driven control for robotic systems.

翻译：数据驱动预测控制（DeePC）是一种强大的数据驱动预测控制方法，无需显式的系统模型。然而，其高昂的计算成本限制了其在实时机器人系统中的应用。对于运动规划和轨迹跟踪等机器人应用，实时控制至关重要。非线性DeePC要么依赖大型数据集，要么需要学习非线性特性以确保预测精度，这导致了较高的计算复杂度。本文提出了一种新颖的数据选择策略——上下文采样，通过在每个时间步动态选择最相关的数据来处理DeePC中的非线性问题。该方法在保持预测精度的同时减小了数据集规模，从而提高了DeePC在实时机器人应用中的计算效率。我们在自动驾驶车辆运动规划任务中验证了所提方法的有效性。对于包含100条子轨迹的数据集，上下文采样DeePC相比杠杆得分采样将跟踪误差降低了53.2%。此外，与使用全部491条子轨迹的完整数据集相比，上下文采样在获得相当跟踪性能的同时，将最大计算时间减少了87.2%。这些结果凸显了上下文采样在实现机器人系统实时数据驱动控制方面的潜力。