新方法让软体机器人在未知负载下仍能精准控制
Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads
- 用增量数据形式构建控制框架，避免参数模型依赖
- 实验显示对未知负载的控制误差显著低于传统方法
- 无需额外数据权重或扰动估计，适合实际操作场景
基于数据的控制方法（如数据启用预测控制，DeePC）在无需显式参数模型的情况下展现出对软体机器人高效控制的潜力。然而，在物体操作任务中，未知外部负载和扰动会显著改变系统动态，导致稳态误差并降低控制性能。本文提出一种新型速度形式的DeePC框架，可在未知负载条件下实现鲁棒且最优的软体机器人控制。该框架利用输入-输出数据的增量表示，有效缓解未知负载引起的性能退化，无需加权数据集或扰动估计器。我们在平面软体机器人上进行了实验验证，结果表明该方法在未知负载场景下相比标准DeePC具有更优的控制性能。
原文摘要 · Abstract (English)
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.
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