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A Study on Feedback Error Learning Controller for Functional Electrical Stimulation: Generation of Target Trajectories by Minimum Jerk Model

机译：用于功能性电刺激的反馈误差学习控制器的研究：通过最小Jerk模型生成目标轨迹

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摘要

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.
机译：发现反馈反馈学习控制器适用于腕关节运动的功能性电刺激控制，包括受试者和计算机模拟测试。但是，正弦轨迹仅用于目标关节角度，并且针对每个轨迹训练了人工神经网络（ANN）。在这项研究中，着眼于两点到达运动，目标轨迹是由最小加速度模型产生的。在计算机仿真测试中，比较了在相同的控制迭代总数（50个控制试验）下以不同数量的目标轨迹训练的人工神经网络。由受过训练的人工神经网络实现的受控肢体的逆动力学模型（IDM）降低了反馈控制器的输出功率，并改善了对未经学习的目标轨迹的跟踪性能。在ANN训练期间，每进行一次对照试验，改变目标轨迹时，IDM的执行效率最高。

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