Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.
翻译：微量元素方法已被成功地用于开发基于物理的软机器人模型,以捕捉由连续变形引起的非线性动态行为。因此,这些高纤维模型是设计控制器以完成诸如轨迹优化和轨迹跟踪等复杂动态任务的理想模式。然而,有限元素模型通常也是非常高的维度模型,这使得实时控制具有挑战性。在这项工作中,我们提出了一个基于软机器人的有限元素模型控制方法,该方法利用模型减少命令技术大大提高计算效率。特别是,一个有限的最佳控制问题是根据非线性减序限定元素模型拟订的,并通过连续的盘旋编程解决。这个方法通过模拟由电缆驱动的软机器人来模拟一个受限制的轨迹跟踪任务,在控制器设计中使用了9768维的有限元素模型。
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