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首页> 外文学位 >Comprehensive Control Strategies for a Seven Degree of Freedom Upper Limb Exoskeleton Targeting Stroke Rehabilitation.
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Comprehensive Control Strategies for a Seven Degree of Freedom Upper Limb Exoskeleton Targeting Stroke Rehabilitation.

机译：针对卒中康复的七个自由度上肢外骨骼的综合控制策略。

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摘要

In this dissertation control algorithms are developed and tested for the EXO-UL7 towards a control strategy for stroke rehabilitation. EXO-UL7 is a seven degree of freedom (Dof) powered upper limb exoskeleton that was initially designed at the University of Washington and further refined at the University of California Santa Cruz. Admittance control, swivel angle prediction and neural control of the device have been implemented and subjects tested performance of the device and control strategy. After an initial summery of the state of the art and details of the existing system. Each control strategy and performance from testing is explored.;Admittance control uses force sensors on EXO-UL7 to control the movement of the device by moving in the direction that user pressed on the device. Because EXO-UL7 is a redundant manipulator and supports the entire configuration of the arm, a single force sensor on the device end effector is not enough to fully define the movement. Additional force sensors on the device that are located at each attachment point of the machine interface allows for the full configuration of the device to be specified. This turns the under defined problem into an over defined problem (too many force signals for the number of Dof). Two strategies are developed to project the signals onto a seven degree subspace. The first adds the force vectors in task space then uses the Inverse kinematics to develop joint trajectories. The second uses the Jacobian transpose to map the forces first to instantaneous joint torques. Then the torques are added in joint space and finally joint trajectories are developed from joint torques. Six subjects performed a peg in hole experiment and it was found that task based admittance control had about 11% lower interaction energy required to do the task. It was also shown that with both admittance controllers the subjects did the tasks slower than with no control at all. Kinematic and dynamic constraints reduced the bandwidth of the system. To improve the bandwidth, predictive algorithms are employed.;Swivel angle prediction is the first predictive algorithm implements to improve the performance of the device. With this control strategy the configuration of the redundant space is related to the end effector position. by observing human behavior it was noted that one of the fundamental task preformed by the human arm was to bring food to the mouth. By maximizing the manipulability of the device in the direction of the mouth, a simple stable closed form prediction of the configuration of the device was achieved. Comparing movement from motion capture to predicted motion showed a good correlation and testing of the algorithm on the exoskeleton device was conducted. 4 subjects conducted a peg in hole task. An 11.22% reduction of interaction energy was achieved when compared to Admittance control alone. This algorithm can be used for motion following as in the current set up, or to predict what the arm configuration should be when working with disabled populations. Although this method predicted motion very well, it only provided prediction of the one Dof redundant space of the arm.;To further extend the prediction capability and motion following of the device, neural control was implemented in which electro-myography (EMG) is used to read the nerve impulses to the muscle. Although this signal only relates muscle force to isometric muscle contraction, using other system parameters read from Exo-UL7 such as the joint, position and velocity, a Hill based muscle model predicts the muscle force and ultimately the muscle torque. Due to an inherent delay between when the nerve impulse can first be detected and when the muscle contracts (somewhere on the order of 50-100 ms) the motion can be predicted before the arm begins to move. The model has many variables that need to be predicted for each individual so before each subject test an parameter fitting is conducted. Four subjects preformed a peg-in-hole test. It was shown that the interaction power increased compared to admittance control, but the completion time decreased. With further examination it was noted that the interaction force and energy when using the neural control was the same as with the admittance control. This implies that with the same force we achieved a higher velocity, which means that the system had a higher overall gain. The performance gains were not uniform through out all the subjects. The parameter fit conducted for each subject did not guarantee convergence to even a local minimum and there are still opportunities to improve the system performance. (Abstract shortened by UMI.).
机译：在本文中，针对EXO-UL7开发了控制算法并对其进行了测试，以控制中风康复的控制策略。 EXO-UL7是由七自由度（Dof）驱动的上肢外骨骼，最初是在华盛顿大学设计的，然后在加利福尼亚大学圣克鲁斯分校进一步完善。设备的导纳控制，旋转角度预测和神经控制已实施，受试者测试了设备的性能和控制策略。在对现有技术和现有系统进行了初步总结之后。探索了每种控制策略和测试的性能。导纳控制使用EXO-UL7上的力传感器，通过沿用户按下设备的方向移动来控制设备的移动。由于EXO-UL7是冗余操纵器，并且支持手臂的整个配置，因此设备末端执行器上的单个力传感器不足以完全定义运动。设备上位于机器接口每个连接点上的其他力传感器允许指定设备的完整配置。这将定义不足的问题转变为定义过度的问题（对于Dof数量，力信号太多）。开发了两种策略来将信号投影到7度子空间上。首先将力矢量添加到任务空间中，然后使用逆运动学来开发关节轨迹。第二种方法使用雅可比移调法将力首先映射到瞬时关节扭矩。然后在关节空间中添加扭矩，最后根据关节扭矩形成关节轨迹。六名受试者在洞中进行了钉子实验，发现基于任务的导纳控制具有比完成任务所需的交互能量低约11％的交互能量。还表明，使用两个导纳控制器，受试者的工作速度都比完全没有控制时要慢。运动和动态约束减少了系统的带宽。为了提高带宽，采用了预测算法。旋转角度预测是第一种实现算法以提高设备性能的预测算法。通过这种控制策略，冗余空间的配置与末端执行器的位置有关。通过观察人类行为，可以注意到人类手臂执行的基本任务之一就是将食物带到嘴里。通过最大程度地提高设备在口方向上的可操作性，可以实现对设备配置的简单稳定的闭合形式预测。从运动捕获到预测运动的运动进行比较显示出良好的相关性，并在外骨骼设备上对该算法进行了测试。 4名受试者进行了挂洞任务。与单独的导纳控制相比，相互作用能降低了11.22％。该算法可用于当前设置中的跟随运动，或预测在与残疾人配合使用时手臂的配置。尽管这种方法能够很好地预测运动，但是它只能预测手臂的一个自由度冗余空间。为了进一步扩展设备的预测能力和运动跟踪，实施了神经控制，其中使用了肌电图（EMG）读取对肌肉的神经冲动。尽管此信号仅将肌肉力量与等距肌肉收缩相关，但使用从Exo-UL7读取的其他系统参数（例如关节，位置和速度），基于Hill的肌肉模型可以预测肌肉力量，并最终预测肌肉扭矩。由于在首次检测到神经冲动和肌肉收缩（大约在50-100毫秒左右）之间存在固有的延迟，因此可以在手臂开始移动之前预测运动。该模型具有许多需要针对每个个体进行预测的变量，因此在进行每个主题测试之前，都要进行参数拟合。四名受试者进行了钉孔测试。结果表明，与导纳控制相比，相互作用力增加，但完成时间减少。进一步检查发现，使用神经控制时的相互作用力和能量与导纳控制相同。这意味着在相同的力下，我们获得了更高的速度，这意味着系统具有更高的整体增益。在所有科目中，成绩提升都不均匀。对每个主题进行的参数拟合不能保证收敛到局部最小值，仍然有机会改善系统性能。 （摘要由UMI缩短。）。

著录项

  • 作者

    Miller, Levi Makaio.;

  • 作者单位

    University of Washington.;

  • 授予单位 University of Washington.;
  • 学科 Health Sciences Rehabilitation and Therapy.;Engineering Mechanical.;Engineering Robotics.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 166 p.
  • 总页数 166
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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