A Hybrid Control Approach Integrating Model-Predictive Control and Fractional-Order Admittance Control for Automatic Internal Limiting Membrane Peeling Surgery

Abstract

As the prevalence of related diseases continues to rise, a corresponding increase in the demand for internal limiting membrane (ILM) peeling surgery has been observed. However, significant challenges are encountered in ILM peeling surgery, including limited force feedback, inadequate depth perception, and surgeon hand tremors. Research on fully autonomous ILM peeling surgical robots has been conducted to address the imbalance between medical resource availability and patient demand while enhancing surgical safety. An automatic control framework for break initiation in ILM peeling is proposed in this study, which integrates model-predictive control with fractional-order admittance control. Additionally, a multi-vision task surgical scene perception method is introduced based on target detection, key point recognition, and sparse binocular matching. A surgical trajectory planning strategy for break initiation in ILM peeling aligned with operative specifications is proposed. Finally, validation experiments for automatic break initiation in ILM peeling were performed using eye phantoms. The results indicated that the positional error of the micro-forceps tip remained within 40 μm. At the same time, the contact force overshoot was limited to under 6%, thereby ensuring both the effectiveness and safety of break initiation during ILM peeling.