Dynamics and Control Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Soft Robot. 2023 Feb;10(1):129-148. doi: 10.1089/soro.2021.0035. Epub 2022 Jun 23.
The motion complexity and use of exotic materials in soft robotics call for accurate and computationally efficient models intended for control. To reduce the gap between material and control-oriented research, we build upon the existing piece-wise constant curvature framework by incorporating hyperelastic and viscoelastic material behavior. In this work, the continuum dynamics of the soft robot are derived through the differential geometry of spatial curves, which are then related to finite-element data to capture the intrinsic geometric and material nonlinearities. To enable fast simulations, a reduced-order integration scheme is introduced to compute the dynamic Lagrangian matrices efficiently, which in turn allows for real-time (multilink) models with sufficient numerical precision. By exploring the passivity and using the parameterization of the hyperelastic model, we propose a passivity-based adaptive controller that enhances robustness toward material uncertainty and unmodeled dynamics-slowly improving their estimates online. As a study-case, a soft robot manipulator is developed through additive manufacturing, which shows good correspondence with the dynamic model under various conditions, for example, natural oscillations, forced inputs, and under tip-loads. The solidity of the approach is demonstrated through extensive simulations, numerical benchmarks, and experimental validations.
在软机器人中，运动复杂性和奇异材料的使用需要用于控制的准确且计算高效的模型。为了缩小材料和面向控制的研究之间的差距，我们通过纳入超弹性和粘弹性材料行为，在现有的分段常数曲率框架的基础上进行构建。在这项工作中，软机器人的连续体动力学是通过空间曲线的微分几何推导出来的，然后将其与有限元数据相关联，以捕获内在的几何和材料非线性。为了实现快速仿真，引入了降阶积分方案，以有效地计算动态拉格朗日矩阵，这反过来又允许具有足够数值精度的实时（多连杆）模型。通过探索被动性并使用超弹性模型的参数化，我们提出了一种基于被动性的自适应控制器，该控制器增强了对材料不确定性和未建模动态的鲁棒性，可在线缓慢改进其估计。作为一个研究案例，通过增材制造开发了一种软机器人操纵器，该操纵器在各种条件下（例如自然振荡、强制输入和末端负载）与动力学模型具有很好的对应关系。该方法的可靠性通过广泛的仿真、数值基准和实验验证得到了证明。
