Joint angle control by FES using a feedback error learning controller
Joint angle control by FES using a feedback error learning controller
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DOI：
10.1109/tnsre.2005.847355
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发表时间：
2005-09-01
影响因子：
4.9
通讯作者：
Hoshimiya, N
中科院分区：
文献类型：
--
作者：
Kurosawa, K;Futami, R;Hoshimiya, N
研究了功能性电刺激控制器的反馈误差学习方法。该自由电子激光控制器是一个前馈和反馈控制器的混合调节器。前馈控制器在控制时从反馈控制器输出学习被控对象的逆动态。每个控制器采用四层神经网络和比例积分微分（PID）控制器。在计算机模拟和FES实验中控制腕部的掌/背屈角度。一些控制器的参数，如学习速度系数和神经元的数量，在仿真中使用的人工手腕的前向模型确定。前向模型是通过使用一个神经网络，可以模仿受试者的手腕电刺激的反应。然后，六个健全的受试者的手腕控制与FEL控制器提供刺激的拮抗肌对。结果表明，FEL控制器的性能达到了预期的要求，比Chien、Hrones和Reswick方法整定的常规PID控制器具有更好的性能，能够在2 s的周期内快速运动，从而减小了平均跟踪误差，缩短了响应延迟。此外，如果前馈控制器已经预先训练人工前向模型，学习迭代缩短。
The feedback error learning (FEL) scheme was studied for a functional electrical stimulation (FES) controller. This FEL controller was a hybrid regulator with a feedforward and a feedback controller. The feedforward controller learned the inverse dynamics of a controlled object from feedback controller outputs while control. A four-layered neural network and the proportional-integral-derivative (PID) controller were used for each controller. The palmar/dorsi-flexion angle of the wrist was controlled in both computer simulation and FES experiments. Some controller parameters, such as the learning speed coefficient and the number of neurons, were determined in simulation using an artificial forward model of the wrist. The forward model was prepared by using a neural network that can imitate responses of subject's wrist to electrical stimulation. Then, six able-bodied subjects' wrist was controlled with the FEL controller by delivering stimuli to one antagonistic muscle pair. Results showed that the FEL controller functioned as expected and performed better than the conventional PID controller adjusted by the Chien, Hrones and Reswick method for a fast movement with the cycle period of 2 s, resulting in decrease of the average tracking error and shortened delay in the response. Furthermore, learning iteration was shortened if the feedforward controller had been trained in advance with the artificial forward model.