A Fuzzy Variable Admittance Control Strategy Based on Bilateral Motion Intention for Upper Limb Rehabilitation Exoskeleton

Abstract

Bilateral robotic training offers a promising solution for upper-limb motor recovery; however, conventional Mirror Therapy (MT) systems typically provide fixed assistance and rely solely on the motion of the unimpaired limb. This paper presents a bilateral intention-driven control strategy for an upper-limb rehabilitation exoskeleton that fuses motor intentions from both the healthy and affected arms. By integrating inertial signals, surface Electromyography (sEMG), and interaction torque, the system employs a fuzzy variable admittance controller combined with sliding mode control to generate adaptive assistive torque that reflects the user’s physical engagement. Experiments involving 4 healthy participants and 11 post-stroke patients were conducted under three control conditions: no interlimb coupling, fixed admittance, and variable admittance. Compared to fixed-parameter control, the proposed variable admittance strategy improved bilateral coordination by 18.6%, reduced interlimb motion lag from 1.19 s to 0.54 s, enhanced intention consistency by 18.33%, and increased average interaction torque by 23.13%. These results demonstrate that combining bilateral intention fusion with adaptive admittance control can significantly enhance movement synchronization and voluntary engagement, offering a promising control paradigm to enhance patient engagement and motion coordination for rehabilitation robotics.