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用于被动上肢康复的定制轨迹优化与柔顺跟踪控制

Customized Trajectory Optimization and Compliant Tracking Control for Passive Upper Limb Rehabilitation.

作者信息

Li Liaoyuan, Han Jianhai, Li Xiangpan, Guo Bingjing, Wang Xinjie

机构信息

School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Henan Provincial Key Laboratory of Robotics and Intelligent Systems, Luoyang 471000, China.

出版信息

Sensors (Basel). 2023 Aug 4;23(15):6953. doi: 10.3390/s23156953.

DOI:10.3390/s23156953
PMID:37571735
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422464/
Abstract

Passive rehabilitation training in the early poststroke period can promote the reshaping of the nervous system. The trajectory should integrate the physicians' experience and the patient's characteristics. And the training should have high accuracy on the premise of safety. Therefore, trajectory customization, optimization, and tracking control algorithms are conducted based on a new upper limb rehabilitation robot. First, joint friction and initial load were identified and compensated. The admittance algorithm was used to realize the trajectory customization. Second, the improved butterfly optimization algorithm (BOA) was used to optimize the nonuniform rational B-spline fitting curve (NURBS). Then, a variable gain control strategy is designed, which enables the robot to track the trajectory well with small human-robot interaction (HRI) forces and to comply with a large HRI force to ensure safety. Regarding the return motion, an error subdivision method is designed to slow the return movement. The results showed that the customization force is less than 6 N. The trajectory tracking error is within 12 mm without a large HRI force. The control gain starts to decrease in 0.5 s periods while there is a large HRI force, thereby improving safety. With the decrease in HRI force, the real position can return to the desired trajectory slowly, which makes the patient feel comfortable.

摘要

脑卒中后早期的被动康复训练可促进神经系统重塑。训练轨迹应融合医生经验和患者特点。并且训练应在安全前提下具备高准确性。因此，基于新型上肢康复机器人开展轨迹定制、优化及跟踪控制算法。首先，识别并补偿关节摩擦力和初始负载。采用导纳算法实现轨迹定制。其次，运用改进的蝴蝶优化算法（BOA）优化非均匀有理B样条拟合曲线（NURBS）。然后，设计可变增益控制策略，使机器人能以较小的人机交互（HRI）力良好跟踪轨迹，并能顺应较大的HRI力以确保安全。对于返回运动，设计了误差细分方法以减缓返回运动。结果表明，定制力小于6N。在无较大HRI力的情况下，轨迹跟踪误差在12mm以内。当存在较大HRI力时，控制增益在0.5s时间段内开始下降，从而提高安全性。随着HRI力减小，实际位置可缓慢回到期望轨迹，使患者感觉舒适。

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Trajectory optimization of the 6-degrees-of-freedom high-speed parallel robot based on B-spline curve.基于B样条曲线的六自由度高速并联机器人轨迹优化
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New Directions in Treatments Targeting Stroke Recovery.针对中风恢复的治疗新方向。
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