论文标题
使用迭代学习控制（ILC）和线性模型的上肢康复机器人的功能电刺激的模拟研究
A Simulation Study of Functional Electrical Stimulation for An Upper Limb Rehabilitation Robot using Iterative Learning Control (ILC) and Linear models
论文作者
论文摘要
在康复任务期间，实施了现有混合中风康复方案的线性模型的比例迭代学习控制（P-ILC）。由于P-ILC的短暂误差生长问题，包括学习衍生限制控制器，以确保在每个试验中受控系统不会超过预定义的速度极限。为了实现这一目标，开发了机器人最终效应器相互作用与中风受试者（植物）的线性传递函数模型以及对刺激控制器的肌肉反应。 0-0.3 m范围的直线点点轨迹是工厂，进料和反馈刺激控制器的参考任务空间轨迹。在每个试验中，基于SAT的有限误差导数ILC算法用作学习约束控制器。开发并模拟了三个控制配置。使用根平方式误差（RMSE）和归一化RMSE评估系统性能。在不同的ILC增益超过16次迭代时，将对照构型组合在一起时，将获得0.0060 m的位移误差。
A proportional iterative learning control (P-ILC) for linear models of an existing hybrid stroke rehabilitation scheme is implemented for elbow extension/flexion during a rehabilitative task. Owing to transient error growth problem of P-ILC, a learning derivative constraint controller was included to ensure that the controlled system does not exceed a predefined velocity limit at every trial. To achieve this, linear transfer function models of the robot end-effector interaction with a stroke subject (plant) and muscle response to stimulation controllers were developed. A straight-line point-point trajectory of 0 - 0.3 m range served as the reference task space trajectory for the plant, feedforward, and feedback stimulation controllers. At each trial, a SAT-based bounded error derivative ILC algorithm served as the learning constraint controller. Three control configurations were developed and simulated. The system performance was evaluated using the root means square error (RMSE) and normalized RMSE. At different ILC gains over 16 iterations, a displacement error of 0.0060 m was obtained when control configurations were combined.
