Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-06-07 DOI:10.1002/rcs.2639

Jintao Lei, Zhuangzhuang Wang

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引用次数: 0

Abstract

Background

For the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances.

Methods

A control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety.

Results

Experiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements.

Conclusions

The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.

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基于非线性干扰观测器和神经网络的断裂修复机器人力/位置跟踪控制。

背景：对于骨折复位机器人来说，肌肉拉伸产生的动态载荷、机器人动力学模型的不确定性以及各种内部和外部干扰都会影响其位置跟踪精度和顺应性：对于骨折复位机器人来说，位置跟踪精度和顺应性受到肌肉拉伸产生的动态负载、机器人动力学模型的不确定性以及各种内部和外部干扰的影响：方法：提出了一种将径向基函数神经网络（RBFNN）与非线性干扰观测器相结合的控制方法，以提高位置跟踪精度。此外，还在力跟踪中采用了导纳控制，以增强机器人的顺应性，从而提高安全性：在模拟肌肉力量的长骨骨折模型上进行了实验，结果表明位置跟踪误差小于±0.2 mm，角位移误差小于±0.3°，最大力跟踪误差为 26.28 N，这一结果可以满足手术要求：该控制方法在提高机器人辅助长骨骨折复位的安全性和准确性方面显示出良好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.