Iterative Learning Control of the Redundant Upper Limb for Rehabilitation

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Description/Abstract

In this paper a non-linear iterative learning control approach is developed for application to stroke rehabilitation. The subject is seated in a robotic workstation and functional electrical stimulation is applied to their triceps muscle to assist the tracking of trajectories in a horizontal plane. In addition to rotation about vertical axes through the shoulder and elbow joints, the forearm is also permitted to elevate in order to provide full arm extension. A dynamic model of the human arm is first developed, and then constraints are introduced in order to overcome kinematic redundancy. The expressions necessary to implement the control law are then derived, and experimental results confirm that it is capable of achieving a high level of performance in practice.