Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential in efficient control of soft robots without explicit parametric models. However, in object manipulation tasks, unknown external payloads and disturbances can significantly alter the system dynamics and behavior, leading to offset error and degraded control performance. In this paper, we present a novel velocity-form DeePC framework that achieves robust and optimal control of soft robots under unknown payloads. The proposed framework leverages input-output data in an incremental representation to mitigate performance degradation induced by unknown payloads, eliminating the need for weighted datasets or disturbance estimators. We validate the method experimentally on a planar soft robot and demonstrate its superior performance compared to standard DeePC in scenarios involving unknown payloads.

翻译：数据驱动控制方法，如数据驱动预测控制（DeePC），在无需显式参数模型的情况下高效控制软体机器人方面展现出巨大潜力。然而，在物体操控任务中，未知的外部载荷和干扰会显著改变系统动力学和行为，导致稳态误差和控制性能下降。本文提出了一种新颖的速度形式DeePC框架，实现了未知载荷下软体机器人的鲁棒最优控制。该框架利用增量形式的输入-输出数据来减轻未知载荷引起的性能下降，无需加权数据集或干扰估计器。我们在一个平面软体机器人上对该方法进行了实验验证，并证明了其在涉及未知载荷的场景中相比标准DeePC的优越性能。