Dynamic Modeling and Motion Control of a Soft Robotic Arm Segment
Dynamic Modeling and Motion Control of a Soft Robotic Arm Segment
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DOI：
10.23919/acc.2019.8815212
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Zhi Qiao;P. Nguyen;Panagiotis Polygerinos;Wenlong Zhang
中科院分区：
文献类型：
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作者：
Zhi Qiao;P. Nguyen;Panagiotis Polygerinos;Wenlong Zhang
软机器人技术由于其顺应性，在操纵和人机交互方面显示出了巨大的潜力。然而，软系统通常具有较大的自由度和较强的非线性，这给精确建模和控制带来了重大挑战。在本文中，开发了一种线性参数变化（LPV）模型来描述软机器人手臂部分的动力学。考虑到不同的驱动机制，通过实验数据确定了伸长和弯曲运动的 LPV 模型。状态反馈 $H_{\infty}$ 控制器是使用线性矩阵不等式 (LMI) 为 LPV 模型设计的。状态反馈控制器的仿真表明闭环系统是稳定的，但存在稳态误差。因此，采用具有P型学习功能的迭代学习控制（ILC）来提高跟踪性能。 ILC+状态反馈控制器的仿真结果表明，随着迭代，稳态误差显着减少。 ILC+状态反馈控制器在多次实验迭代中成功地将软机械臂部分移动到所需位置。
Soft robotics has shown great potential in manipulation and human-robot interaction due to its compliant nature. However, soft systems usually have a large degree of freedom and strong nonlinearities, which pose significant challenges for precise modeling and control. In this paper, a linear parameter-varying (LPV) model is developed to describe the dynamics of a soft robotic arm segment. Given the different actuation mechanisms, the LPV models for elongation and bending motions are identified through experimental data. A state-feedback $H_{\infty}$ controller is designed for the LPV model using a linear matrix inequality (LMI). Simulation of the state-feedback controller indicates that the closed-loop system is stable but with steady-state errors. As a result, an iterative learning control (ILC) with P-type learning function is implemented to improve the tracking performance. Simulation results of the ILC+state-feedback controller show steady-state errors are significantly reduced with iterations. The ILCs+state-feedback controller successfully moves the soft robotic arm segment to its desired position within several iterations in experiments.