Comparing model-based control methods for simultaneous stiffness and position control of inflatable soft robots
Comparing model-based control methods for simultaneous stiffness and position control of inflatable soft robots
复制标题
基于模型的充气式软机器人刚度和位置同时控制方法的比较
DOI：
10.1177/0278364920911960
复制
发表时间：
2020-05-08
影响因子：
9.2
通讯作者：
Killpack, Marc D.
中科院分区：
文献类型：
--
作者：
Best, Charles M.;Rupert, Levi;Killpack, Marc D.
充气式机器人天生重量轻，顺应性强，这可能使它们非常适合在非结构化环境中或靠近人类的地方工作。本文中使用的可充气接头由坚固的织物外部组成，该织物外部约束两个相对的顺应性气囊，该气囊产生扭矩（不像McKibben致动器，其中压力变化引起平移）。这种对抗结构允许同时控制位置和刚度。然而，软机器人的动态模型，允许变刚度控制还没有得到很好的发展。在这项工作中，包括刚度作为状态变量的模型的开发和验证。利用刚度模型，滑模控制器和模型预测控制器的开发，同时控制刚度和位置。对于滑模控制（SMC），关节刚度控制在0.07 Nm/rad的45 Nm/rad的命令。对于模型预测控制（MPC），关节刚度被控制在相同刚度命令的0.045Nm/rad内。SMC和MPC都能够在稳定状态下控制到期望位置的0.5度内。利用MPC将刚度控制扩展到多自由度柔性机器人。与低刚度（30 Nm/rad）相比，当使用更高的关节刚度（40 Nm/rad）时，在末端执行器处施加4 lb（1.8 kg）步进输入时，控制4-DOF臂的刚度将末端执行器偏转减少约50%（从17.9 cm到12.2 cm）。这项工作表明，推导出的刚度模型可以使有效的位置和刚度控制。
Inflatable robots are naturally lightweight and compliant, which may make them well suited for operating in unstructured environments or in close proximity to people. The inflatable joints used in this article consist of a strong fabric exterior that constrains two opposing compliant air bladders that generate torque (unlike McKibben actuators where pressure changes cause translation). This antagonistic structure allows the simultaneous control of position and stiffness. However, dynamic models of soft robots that allow variable stiffness control have not been well developed. In this work, a model that includes stiffness as a state variable is developed and validated. Using the stiffness model, a sliding mode controller and model predictive controller are developed to control stiffness and position simultaneously. For sliding mode control (SMC), the joint stiffness was controlled to within 0.07 Nm/rad of a 45 Nm/rad command. For model predictive control (MPC) the joint stiffness was controlled to within 0.045 Nm/rad of the same stiffness command. Both SMC and MPC were able to control to within 0.5 degrees of a desired position at steady state. Stiffness control was extended to a multiple-degree-of-freedom soft robot using MPC. Controlling stiffness of a 4-DOF arm reduced the end-effector deflection by approximately 50% (from 17.9 to 12.2cm) with a 4 lb (1.8 kg) step input applied at the end effector when higher joint stiffness (40 Nm/rad) was used compared with low stiffness (30 Nm/rad). This work shows that the derived stiffness model can enable effective position and stiffness control.