School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Int J Med Robot. 2024 Jun;20(3):e2639. doi: 10.1002/rcs.2639.
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.
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.
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.
The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.
对于骨折复位机器人，位置跟踪精度和顺应性会受到肌肉拉伸产生的动态负载、机器人动力学模型中的不确定性以及各种内部和外部干扰的影响。
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，采用导纳控制进行力跟踪，以提高机器人的顺应性，从而提高安全性。
在模拟肌肉力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±0.2mm，角位移误差小于±0.3°，最大力跟踪误差为 26.28N。这个结果可以满足手术要求。
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了良好的效果。
