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.