Model and Data Based Approaches to the Control of Tensegrity Robots
Model and Data Based Approaches to the Control of Tensegrity Robots
复制标题
DOI：
10.1109/lra.2020.2979891
复制
发表时间：
2020-03
影响因子：
5.2
通讯作者：
Ran Wang;R. Goyal;S. Chakravorty;R. Skelton
中科院分区：
文献类型：
--
作者：
Ran Wang;R. Goyal;S. Chakravorty;R. Skelton
这封信提出了两种方法来控制软机器人应用的结构的形状或末端执行器的位置。第一种方法是基于模型的方法，其中张拉整体系统的非线性动力学用于将位置、速度和加速度调节到指定的参考轨迹。该制剂使用状态反馈来获得作为线性规划问题的控制（张力在字符串）的解决方案。另一种无模型方法是一种新的解耦基于数据的控制（D2C），它首先使用黑盒（没有实际模型）仿真模型优化确定性开环轨迹，然后围绕线性化开环轨迹开发线性二次调节器。一个二维张拉整体机器人reacher是用来比较的结果，为这两种方法为一个给定的成本函数。D2C方法也被用来研究两个更复杂的张拉整体的例子，其动力学是很难建模分析。
this letter proposes two approaches to control the shape of the structure or the position of the end effector for a soft-robotic application. The first approach is a model-based approach where the non-linear dynamics of the tensegrity system is used to regulate position, velocity and acceleration to the specified reference trajectory. The formulation uses state feedback to obtain the solution for the control (tension in the strings) as a linear programming problem. The other model-free approach is a novel decoupled data-based control (D2C) which first optimizes a deterministic open-loop trajectory using a black-box (no actual model) simulation model and then develops a linear quadratic regulator around the linearized open-loop trajectory. A two-dimensional tensegrity robotic reacher is used to compare the results for both the approaches for a given cost function. The D2C approach is also used to study two more complex tensegrity examples whose dynamics is difficult to model analytically.