Full Study, Model Verification, and Control of a Five Degrees of Freedom Hybrid Robotic‐Assisted System for Neurosurgery

Abstract

BACKGROUND: Neurosurgery demands high precision, and robotic-assisted systems are increasingly employed to enhance surgical outcomes. This study focuses on a hybrid robotic-assisted system for neurosurgery, addressing forward and inverse kinematics, Jacobian matrices, and system singularities. METHODS: The system is simulated using MATLAB/Simscape Multibody to achieve accurate kinematic and dynamic representations. An inverse kinematics framework was developed for generating and validating a circular trajectory at the end-effector tip. Two control strategies are compared: traditional active joint PID control and combined trajectory feedback plus feedforward control. RESULTS: The combined control strategy significantly improves performance, reducing the maximum absolute error of each output by an average of 46.5% and the mean square error by 50.31% under optimal conditions. CONCLUSION: The findings highlight the potential of trajectory feedback and feedforward control to enhance the precision and reliability of robotic-assisted neurosurgical procedures.