Adaptive Adjustment of FES Profiles Using Norm-Optimal Iterative Learning Control for Foot Drop Correction

Functional electrical stimulation (FES) is widely used as an assistive method for foot drop correction. However, existing FES controllers often induce unnatural muscle activation through rigid stimulation or lack adaptability to dynamic changes in gait performance. This study proposed an FES profile optimization method to achieve natural and adaptive stimulation in order to compensate for disturbances through the following two steps: 1) a Hammerstein-structured ankle joint dynamic model was developed to establish the relationship between the FES profiles and musculoskeletal dynamic response and 2) utilizing this model, a Norm-Optimal Iterative Learning Control (NOILC)-based FES controller was designed, and an optimal control learning gain was determined to adjust FES profiles for automatic correction of trajectory tracking errors. The proposed controller's performance was evaluated using kinematic data from five stroke patients and compared with that under two conditions: no FES and fixed-profile FES. The experimental results showed that the proposed controller could result in ankle dorsiflexion motions closer to the reference trajectory, and the maximum dorsiflexion angle during the swing phase was significantly improved by 3.92° relative to the no FES condition and by 2.06° relative to the fixed-profile FES condition. This study indicates that the proposed controller can provide natural and adaptive FES profiles, enhancing gait performance for stroke patients and showing promising potential for clinical application.