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A proportional iterative learning control (P-ILC) for linear models of anexisting hybrid stroke rehabilitation scheme is implemented for elbowextension/flexion during a rehabilitative task. Owing to transient error growthproblem of P-ILC, a learning derivative constraint controller was included toensure that the controlled system does not exceed a predefined velocity limitat every trial. To achieve this, linear transfer function models of the robotend-effector interaction with a stroke subject (plant) and muscle response tostimulation controllers were developed. A straight-line point-point trajectoryof 0 - 0.3 m range served as the reference task space trajectory for the plant,feedforward, and feedback stimulation controllers. At each trial, a SAT-basedbounded error derivative ILC algorithm served as the learning constraintcontroller. Three control configurations were developed and simulated. Thesystem performance was evaluated using the root means square error (RMSE) andnormalized RMSE. At different ILC gains over 16 iterations, a displacementerror of 0.0060 m was obtained when control configurations were combined.

nigerian journal of technology

A Simulation Study of Functional Electrical Stimulation for An Upper  Limb Rehabilitation Robot using Iterative Learning Control (ILC) and Linear  models