A study on feedback error learning controller for functional electrical stimulation: generation of target trajectories by minimum jerk model.

The Feedback Error Learning controller was found to be applicable to functional electrical stimulation control of wrist joint movements in control with subjects and computer simulation tests in our previous studies. However, sinusoidal trajectories were only used for the target joint angles and the artificial neural network (ANN) was trained for each trajectory. In this study, focusing on two-point reaching movement, target trajectories were generated by the minimum jerk model. In computer simulation tests, ANNs trained with different number of target trajectories under the same total number of control iterations (50 control trials) were compared. The inverse dynamics model (IDM) of the controlled limb realized by the trained ANN decreased the output power of the feedback controller and improved tracking performance to unlearned target trajectories. The IDM performed most effectively when target trajectory was changed every one control trial during ANN training.