Control-oriented Modeling of Soft Robotic Swimmer with Koopman Operators
Control-oriented Modeling of Soft Robotic Swimmer with Koopman Operators
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DOI：
10.1109/aim43001.2020.9159033
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发表时间：
2020-07
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影响因子：
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通讯作者：
Maria L. Castaño;Andrew Hess;Giorgos Mamakoukas;Tong Gao;T. Murphey;Xiaobo Tan
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文献类型：
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作者：
Maria L. Castaño;Andrew Hess;Giorgos Mamakoukas;Tong Gao;T. Murphey;Xiaobo Tan
近年来，由于软机器人在众多应用中的潜力，人们对软机器人的兴趣有所增加。各种各样的软机器人已经出现，包括仿生机器人游泳者，如水母、鳐鱼和机器鱼。然而，高度非线性的流体-结构相互作用对这些软机器人游泳者的分析、建模和反馈控制提出了相当大的挑战。特别是，开发高保真度且易于控制此类机器人的模型仍然是一个悬而未决的问题。在这项工作中，我们提出了一种数据驱动的方法，利用库普曼算子来获得软游泳者动力学的线性表示。具体来说，探索了两种方法来获取算子的基函数，一种是基于使用高增益观测器估计的基于数据的导数，另一种是基于尾部驱动的刚体机器鱼的动力学结构。使用来自包含流体-结构相互作用的高保真 CFD 模拟的数据来训练和评估所得的近似有限维算子。验证结果表明，虽然这两种方法在生成面向控制的模型方面都有希望，但基于导数估计的方法在状态预测方面显示出更高的准确性。
Interest in soft robotics has increased in recent years due to their potential in a myriad of applications. A wide variety of soft robots has emerged, including bio-inspired robotic swimmers such as jellyfish, rays, and robotic fish. However, the highly nonlinear fluid-structure interactions pose considerable challenges in the analysis, modeling, and feedback control of these soft robotic swimmers. In particular, developing models that are of high fidelity but are also amenable to control for such robots remains an open problem. In this work, we propose a data-driven approach that exploits Koopman operators to obtain a linear representation of the soft swimmer dynamics. Specifically, two methodologies are explored for obtaining the basis functions of the the operator, one based on data-based derivatives estimated using high-gain observers, and the other based on the dynamics structure of a tail-actuated rigid-body robotic fish. The resulting approximate finite-dimensional operators are trained and evaluated using data from high-fidelity CFD simulations that incorporate fluid-structure interactions. Validation results demonstrate that, while both methods are promising in producing control-oriented models, the approach based on derivative estimates shows higher accuracy in state prediction.