论文标题
软机器人通过减少订单有限元模型的最佳控制
Soft Robot Optimal Control Via Reduced Order Finite Element Models
论文作者
论文摘要
有限元方法已成功地用于开发基于物理的软机器人模型，这些模型捕获了由连续变形引起的非线性动态行为。因此，这些高保真模型是为复杂动态任务（例如轨迹优化和轨迹跟踪）设计控制器的理想选择。但是，有限元模型通常也非常高维，这使实时控制具有挑战性。 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.特别是，基于非线性降低的有限元模型制定了约束最佳控制问题，并通过顺序凸编程解决。通过模拟电缆驱动的软机器人来证明这种方法，以进行约束轨迹跟踪任务，其中9768维有限元模型用于控制器设计。
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.
