Adaptive weighted fuzzy ILC for personalized FES trajectory reconstruction using an anatomical prior

Objective. The clinical translation of closed-loop functional electrical stimulation (FES) systems is hindered by three interrelated bottlenecks: the trade-off between algorithmic complexity and embedded real-time feasibility, inefficient personalization, and a lack of empirical validation of human-machine cooperative performance in stroke patients.Approach. We propose a physiology-informed adaptive weighted fuzzy iterative learning control (AW-FILC) algorithm, implemented on a custom STM32F407-based embedded FES platform. The core innovation is a dual-layer adaptive mechanism that incorporates subcutaneous fat thickness, which is a key biophysical determinant of current attenuation measurable, as a feedforward prior. This prior is fused with real-time kinematic error feedback to dynamically modulate the learning gain and penalty factor.Main results. In simulations across two heterogeneous musculoskeletal models, AW-FILC achieves convergence within 5-7 iterations, with steady-state tracking accuracy of 10^-5for normalized torque and Significance. This work provides a computationally efficient, physiologically adaptive control core for embedded FES devices and empirically validates that AW-FILC-driven active rehabilitation bridges the gap between pathological and physiological coordination.