用数据驱动降维方法实现软体机器人的动态形状精准控制
Dynamic Shape Control of Soft Robots Enabled by Data-Driven Model Reduction
- 采用LOpInf等三种数据驱动降维法构建线性模型
- 基于LOpInf的控制策略误差最低，优于其他模型
- 适合对软体机器人动态控制感兴趣的工程师和研究者
软体机器人在需要全身动态控制的场景中展现出巨大潜力。然而，高效动态形状控制需依赖能处理高维动力学的控制器，而当前缺乏通用的软体机器人建模工具以适配控制需求。本文对比了三种数据驱动模型降维技术：特征系统实现算法、带控制的动态模态分解及拉格朗日算子推断（LOpInf）。利用这些模型，我们在三个实验中评估其在模拟鳗鱼仿生软体机器人上的模型预测控制性能：1）跟踪保证可行性的参考轨迹；2）跟踪基于生物鳗鱼运动模型生成的轨迹；3）跟踪缩小比例物理样机生成的轨迹。所有实验中，基于LOpInf的控制策略均取得更低的跟踪误差。
原文摘要 · Abstract (English)
Soft robots have shown immense promise in settings where they can leverage dynamic control of their entire bodies. However, effective dynamic shape control requires a controller that accounts for the robot's high-dimensional dynamics--a challenge exacerbated by a lack of general-purpose tools for modeling soft robots amenably for control. In this work, we conduct a comparative study of data-driven model reduction techniques for generating linear models amendable to dynamic shape control. We focus on three methods--the eigensystem realization algorithm, dynamic mode decomposition with control, and the Lagrangian operator inference (LOpInf) method. Using each class of model, we explored their efficacy in model predictive control policies for the dynamic shape control of a simulated eel-inspired soft robot in three experiments: 1) tracking simulated reference trajectories guaranteed to be feasible, 2) tracking reference trajectories generated from a biological model of eel kinematics, and 3) tracking reference trajectories generated by a reduced-scale physical analog. In all experiments, the LOpInf-based policies generated lower tracking errors than policies based on other models.
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