High-Precision Tracking of Time-Varying Trajectories for Microsurgical Robots in Constrained Environments

Abstract

This research addresses the challenge of achieving high-precision tracking of time-varying trajectories under nonlinear disturbances and motion constraints in microsurgical robots. A hybrid control framework integrating fuzzy adaptive sliding mode control with radial basis function neural networks is proposed. This framework dynamically adjusts the sliding mode gain to suppress high-frequency jitter and compensate for unmodeled disturbances such as joint friction and tissue contact forces. Experiments conducted on a self-developed microscopic ophthalmic robot platform demonstrated that the trajectory tracking error was reduced to 1.1 μm, representing improvements of 85.9%, 76.1%, and 66.7% compared to PID control, sliding mode control and non-singular fast terminal sliding mode control respectively. The tracking delay was 19 milliseconds. In experiments on living pigs with central retinal artery occlusion, the system successfully performed intravascular injection, with a maximum error of 3.97 μm. This solution, through optimization via fuzzy logic and neural networks, achieves micron-level precision and robustness, effectively solving high-frequency control noise and low-frequency environmental disturbances, ensuring both the accuracy and safety of the microsurgical robot.