Optimization and control of robotic vertebral plate grinding: Predictive modeling, parameter optimization, and fuzzy control strategies for minimizing bone damage in laminectomy procedures.

During the robotic grinding of vertebral plates in high-risk laminectomy procedures, programmed operations may inadvertently induce force or temperature-related damage to the bone tissue. Therefore, it is imperative to explore a control methodology aimed at minimizing such damage during the robotic grinding of vertebral plate cortical bone, contingent upon optimal grinding parameters. Initially, predictive models for both the grinding force and temperature of vertebral plate cortical bone were developed using the response surface design (RSD) methodology. Subsequently, employing the satisfaction function approach, multi-objective parameter optimization of these predictive models was conducted to ascertain the optimal combination of parameters conducive to low-damage grinding. The optimum grinding parameters identified were a speed of 6000 r/min, a depth of grind of 0.4 mm, and a feed rate of 3.8 mm/s. Moreover, a multi-layer adaptive fuzzy control strategy was devised, and a corresponding multi-layer adaptive fuzzy controller (MFLC) was then implemented to dynamically adjust the grinding feed speed. The efficacy of this control module was corroborated through Simulink simulations. Simulation results demonstrated that the magnitude of the grinding force fluctuated within the range of 2.2-2.6 N after FLC control, while the fluctuation range of the grinding force was limited to 2.2-2.48 N after MFLC control. This indicates that MFLC control brings the force closer to the target expectation value of 2.39 N compared with FLC control. Finally, the dynamic fuzzy control method predicated on optimal grinding parameters was validated through experimental porcine spine grinding conducted on a robotic vertebral plate grinding platform.