发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文扩展绝热谱子流形数据驱动降阶方法，用于软体机器人模型预测控制，在压力驱动软臂仿真中将位置与姿态跟踪误差降低逾60%。
AI 中文摘要
软体机器人通常被用于精细环境中的安全关键交互，其中精确的位置和姿态控制至关重要。模型预测控制（MPC）提供了一种解决方案，但它需要机器人的无限维非线性动力学模型，该模型既要准确又要计算成本低廉。最近关于绝热谱子流形（aSSMs）及其在软体机器人中应用的理论，提供了构建此类模型的数据驱动模型降阶方法。在此，我们扩展了这些方法，从扩大的可观测数据集中识别aSSMs，并升级了当前可用的aSSM-MPC方案。在压力驱动软臂的高保真有限元模拟上评估，我们的控制器相比现有数据驱动基线，将位置和姿态跟踪误差降低了超过60%。
英文摘要
Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative. Model predictive control (MPC) offers a solution, but it requires a model of the robot's infinite-dimensional nonlinear dynamics that is at once accurate and computationally cheap. Recent theory on adiabatic spectral submanifolds (aSSMs) and their applications to soft robots provide data-driven model-reduction methods to construct such models. Here, we extend these methods to identify aSSMs from enlarged observable datasets and upgrade the currently available aSSM-MPC schemes. Evaluated on a high-fidelity finite-element simulation of a pressure-actuated soft arm, our controller reduces position and orientation tracking error by more than 60% compared to existing data-driven baselines.
CommentsThis paper has been accepted for presentation at the 2026 IEEE Conference on Decision and Control (CDC)
