Research on Adaptive Variable Impedance Control Method Based on Adaptive Neuro-Fuzzy Inference System.

Precise force tracking and overshoot suppression are critical for manipulator dynamic contact tasks, especially in unstructured environments such as complex surface cleaning that rely on dynamic feedback from force sensors. Traditional impedance control methods exhibit limitations through excessive force overshoot and steady-state error, severely impacting cleaning performance. To address this problem, this paper introduces proportional-integral-derivative (PID) control based on the traditional impedance model and verifies the stability and convergence of the controller through theoretical analysis. Meanwhile, to improve the applicability of the controller and avoid using expert experience to formulate fuzzy rules, this paper designs an adaptive neuro-fuzzy inference system (ANFIS) to dynamically adjust the update rate. To validate the effectiveness of the proposed method, simulation experiments mirroring real-world scenarios of contact cleaning tasks are constructed in Simulink. The results demonstrate that, compared to adaptive impedance control (AIC) and adaptive variable impedance control (AVIC), the proposed controller achieves a faster steady-state response and exhibits negligible overshoot and minimal force steady-state error during both constant and sinusoidal force tracking. Furthermore, the controller demonstrates superior stability under abrupt changes in stiffness and desired force.