基于模糊控制的上肢康复机器人变导纳控制OACSTPCD

Traditional fixed-parameter admittance models could not adjust the compliance of upper limb rehabilita-tion robots in real-time,existing variable-parameter admittance models required improvement based on actual reha-bilitation needs.To address this issue,a novel variable-admittance control strategy was proposed for upper limb re-habilitation robots with a self-developed series-parallel hybrid end-effector traction structure,combining fuzzy and admittance control and tailored to actual rehabilitation needs.Four fuzzy rules that could benefit rehabilitation effi-ciency and safety were developed.This strategy proposed the use of interaction force error and its rate of change as inputs to fuzzy control,to adjust admittance model parameters and achieve autonomous compliance control in real time.Simulation and experimental results validated the feasibility of the proposed variable-admittance control strate-gy and the effectiveness of the developed four fuzzy rules.In scenarios where patients could adapt to training inten-sity,the variable-admittance model could reduce the redundant path generated during path tracking by up to 56.13％thus improving rehabilitation training efficiency.When rehabilitation movements exceeded the patients'tolerance limit,the variable-admittance model could change the tracking path half a second earlier,improving reha-bilitation safety.