ScholarWorks@성균관대학교

초록

This paper presents a fuzzy-based adaptive admittance control (FAAC) framework for position-controlled robots in uncertain contact environments. The proposed FAAC regulates admittance parameters using three fuzzy adaptation maps rather than directly generating robot control inputs. The Mass-Adaptation Fuzzy Map (MAFM) adjusts the dominant virtual mass eigenvalue, the Damper-Mass Ratio Fuzzy Map (DMRFM) adapts the damping-related ratio, and the Rendering-Quality Supervisory Fuzzy Map (RQ-SFM) restricts unsafe low-mass adaptation based on rendering quality and vibration metrics. An energy-tank-based admissibility filter is integrated to preserve passivity during online parameter adaptation and contact transitions. Comparative simulations against a stiffness-adaptive baseline and an ablated mass-damping adaptive baseline under nominal, noisy, and filtered sensing conditions verify the robustness of the proposed architecture. Experiments on a UR10 polishing task further show that the proposed FAAC improves force-tracking consistency and contact-maintenance robustness compared with fixed-parameter AAC baselines and FAAC-M. In particular, the proposed FAAC achieved the lowest force standard deviation of 2.76 N and no contact-loss events, whereas the baseline AAC controllers exhibited force fluctuations associated with abrupt desired stiffness changes during contact. These results demonstrate the effectiveness of FAAC for robust robot-environment interaction under uncertain contact conditions.

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