Powered Ankle Exoskeleton Control Based on sEMG-Driven Model Through Adaptive Fuzzy Inference

Powered ankle exoskeletons have become efficient ability-enhancing and rehabilitation tools that support human body movements. Traditionally, the control schemes for ankle exoskeletons were implemented relying on precise physical and kinematic models. However, this approach resulted in poor coordination of human–machine coupled motion and an increase in the wearer’s energy consumption. To solve the cooperative control issue between the wearer and the ankle exoskeleton, this work introduces an adaptive impedance control method for the ankle exoskeleton that is based on the surface electromyography (sEMG) of the calf muscles. The proposed method achieves cooperative control by leveraging an experience-based fuzzy rule interpolation (E-FRI) approach to dynamically adjust the impedance model parameters. This adaptive mechanism is driven by the wearer’s calf sEMG signals, which capture the wearer’s movement state. The adaptive impedance model then computes the desired torque for the ankle exoskeleton. To validate and evaluate the system, the control method was implemented on a simplified ankle exoskeleton. Experimental validation with five healthy participants (age 19 ± 1.35 years) demonstrated significant improvements over conventional fixed-impedance approaches: mean RMS reductions of 19.7% in gastrocnemius activation and 21.4% in soleus activation during treadmill walking. This study establish a new paradigm for responsive exoskeleton control through symbiotic integration of neuromuscular signals and adaptive fuzzy inference, offering critical implications for rehabilitation robotics and assistive mobility technologies.