Functional neuromuscular stimulation limb motion control using adaptive iterative learning algorithm

A discrete time-varying nonlinear model with undetermined and unexpected perturbations is firstly investigated according to the nonlinear phenomena of the limb muscles. A general expression based on the adaptive iteration learning control algorithm is developed, and the control algorithm structure block diagram is then presented. Based on the multi- purpose functional neuromuscular stimulation (FNS) limb motion control system, the clinical experiments on motion trajectory- following of elbow flexion and wrist flexion were successfully conducted by meaas of both the adaptive iteration learning control algorithm and the conventional control algorithm. The clinical experimental results demonstrated that the adaptive iteration learning control algorithm is more suitable for the improvement of the dynamic response characteristics and the stabilization of limb motion than conventional control algorithm. Furthermore, the stimulated patients have not any bad physiological reactions because the output electrical stimulation pulses generated by the adaptive iteration learning control algorithm vary gently.