论文标题
在可变加载条件下，基于库普曼的软连续操作器的控制
Koopman-based Control of a Soft Continuum Manipulator Under Variable Loading Conditions
论文作者
论文摘要
由于其无限的自由度，非线性材料特性以及载荷下的较大偏转，因此很难控制柔软的连续操纵臂。本文提出了一种数据驱动的方法，用于识别软件模型，该模型可以在可变加载条件下进行一致的控制。这是通过将负载纳入线性Koopman操作员模型并通过控制循环中的观察者在线估算其值来实现的。使用这种方法，可以实现对气动驱动的软连续操作器的实时自主控制。在实验之后的几个轨迹中，基于无法明确考虑加载的模型，该控制器比控制器更准确，更精确。操纵器还成功地执行了具有未知质量的对象的选择和地点，这证明了这种方法在执行现实世界操纵任务中的功效。
Controlling soft continuum manipulator arms is difficult due to their infinite degrees of freedom, nonlinear material properties, and large deflections under loading. This paper presents a data-driven approach to identifying soft manipulator models that enables consistent control under variable loading conditions. This is achieved by incorporating loads into a linear Koopman operator model as states and estimating their values online via an observer within the control loop. Using this approach, real-time, fully autonomous control of a pneumatically actuated soft continuum manipulator is achieved. In several trajectory following experiments, this controller is shown to be more accurate and precise than controllers based on models that are unable to explicitly account for loading. The manipulator also successfully performs pick and place of objects with unknown mass, demonstrating the efficacy of this approach in executing real-world manipulation tasks.
