Adaptive Event-Triggered Fuzzy Impedance Control for Robotic Systems

This paper presents an adaptive event-triggered fuzzy impedance control strategy to address the critical node omission and external disturbances in event-triggered sampling. The impedance control reshapes the tracking trajectory of the robot, while the adaptive event-triggered mechanism optimizes the sampling points. A fuzzy logic control system is employed to approximate unknown nonlinear functions, and adaptive triggering condition is constructed based on the Lyapunov stability theorem. Experimental results demonstrate that the presented strategy significantly enhances system performance.