Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation
Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation
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DOI：
10.1109/tro.2015.2503726
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发表时间：
2016-02-01
影响因子：
7.8
通讯作者：
O'Malley, Marcia K.
中科院分区：
文献类型：
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作者：
Pehlivan, Ali Utku;Losey, Dylan P.;O'Malley, Marcia K.
当受试者参与康复方案时，神经损伤后上肢的机器人康复最成功。因此，开发最大化主体参与的辅助控制策略是一个活跃的研究领域，旨在促进神经可塑性，进而增加运动协调恢复的潜力。不幸的是，最先进的控制策略要么忽视更复杂的主体能力，要么假设基础模式控制主体行为，因此可能会进行次优干预。在本文中，我们提出了一个最小的辅助按需（mAAN）控制器的上肢康复机器人。该控制器采用无传感器力估计来动态地确定主体输入，而不需要任何关于主体能力的本质的基本假设，并计算具有可调节的位置误差极限的相应辅助扭矩。我们的自适应输入估计方案，以产生快速，稳定，准确的测量，无论受试者的相互作用，并超过了目前的方法，估计只有位置相关的力输入的用户的性能。本文介绍了两个额外的算法，以进一步促进不同程度的障碍的主体的积极参与。首先，边界修改算法，它改变了允许的误差。其次，提出了一种衰减干扰抑制算法，该算法鼓励能够引导参考轨迹的受试者。在RiceWrist-S外骨骼中用健康受试者实验证明了mAAN控制器和伴随的算法。
Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.