Newton-method based iterative learning control for robot-assisted rehabilitation using FES

Precise control of useful movement is critical in providing effective upper limb stroke rehabilitation using functional electrical stimulation (FES). To address the lack of accuracy currently available in clinical practice, this paper develops a general framework based on iterative learning control, an advanced model-based approach that has been successfully employed in three clinical treatment trials. An upper limb model is first developed to encompass unconstrained movements of the upper arm, with, in line with clinical need, additional assistance incorporated via a general class of robotic support mechanism. An iterative learning scheme is then developed to enable a subset of joint angles to be controlled via stimulation of an arbitrary set of muscles. This scheme is the first ILC approach which explicitly addresses coupled multivariable nonlinear dynamics in rehabilitation, enforcing convergence over multiple repetitions of a reaching task. Experiments with six participants confirm practical utility and performance