Adaptive Stimulation Profiles Adjustment of Functional Electrical Stimulation for Foot Drop Based on Iterative Learning Control

Functional electrical stimulation (FES) offers an effective approach for foot drop (FD) correction by artificially contracting the tibialis anterior (TA) muscle. The majority of current FES systems employ predetermined stimulation parameters and stimulation profiles, which cannot improve gait performances well considering significant time-variance and individuality of muscle response dynamics under electrical stimulation. To mitigate these challenges, this article proposes a model-based closed-loop control scheme to achieve adaptive adjustment of stimulation intensity profiles using Norm-optimal iterative learning control (NOILC). Firstly, a dynamic model for ankle joint utilizing a Hammerstein structure for subject-specific is developed, as well as the parameters identification. Then, a closed-loop controller based on NOILC is designed to allow tracking of desired trajectory of ankle joint angle for foot drop correction according to FES-induced foot motion. Finally, computer simulation tests are performed and verify that the proposed control method can achieve a good tracking performance through adaptive modulation of stimulation intensity profile, and it is consistent with the physiological characteristics of foot motion.