用智能控制让软体机器人食道自主产生规律蠕动，提升假体测试精度。
Nonlinear Model Predictive Control of a Robotic Soft Esophagus
- 基于传感器数据构建非线性动态模型，实现闭环控制。
- 成功生成20毫米/秒、75毫米波长的蠕动波，振幅达10毫米。
- 适用于支架移位测试和食物黏稠度研究，适合医疗仿真场景。
食管癌引起的狭窄会缩小食管腔径，导致吞咽困难。为缓解吞咽困难，研究人员开发了软体机器人食道（RoSE），作为食管支架测试与食物黏稠度研究的新体外平台。早期版本采用开环方式控制蠕动波，因管道缺乏可视性与嵌入式传感能力。本文设计了改进版RoSEv2.0，集成飞行时间（TOF）与压力传感器，分别测量管道位移与气压，用于建模与控制。采用模型预测控制（MPC）实现蠕动与气压分布的自主调控。所用MPC基于稀疏识别非线性动力学与控制（SINDYC）模型，从传感器数据中学习系统动态。成功生成速度为20毫米/秒、波长75毫米、振幅分别为5、7.5和10毫米的蠕动波。此外，搭载该控制系统的RoSEv2.0还用于不同食物团块黏稠度下的支架移位测试。主要贡献在于首次将基于SINDYC的MPC应用于RoSE的闭环控制，实现预期蠕动波形的精确生成。
原文摘要 · Abstract (English)
Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE), which provides a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) is designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 is implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models are discovered from the TOF and pressure sensor data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food bolus consistencies. The major contribution claimed in this paper is the application of SINDYC-based MPC to solve the closed-loop control problem of RoSE for achieving desired peristaltic waves.
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