Mehrabi Naser, Sharif Razavian Reza, Ghannadi Borna, McPhee John
Systems Design Engineering, University of Waterloo Waterloo, ON, Canada.
Front Comput Neurosci. 2017 Jan 13;10:143. doi: 10.3389/fncom.2016.00143. eCollection 2016.
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.
本文研究了最优反馈控制在人体手臂自主运动轨迹规划中的应用。采用具有有限预测时域的非线性模型预测控制器（NMPC）作为最优反馈控制器，以预测平面伸手任务的手部轨迹规划与执行。NMPC具有完全预测性，无需运动跟踪或肌电图数据即可获得肢体轨迹。为阐述这一概念，使用了由三对拮抗肌驱动的两自由度肌肉骨骼平面手臂模型来模拟人体手臂动力学。本研究基于神经系统在目标导向运动中使肌肉用力最小化这一假设。给出了预测时域长度对伸手任务的轨迹、速度分布和肌肉活动的影响。NMPC对手部到达固定和移动目标轨迹的预测与动态优化得到的轨迹以及实验轨迹吻合良好。然而，NMPC预测的手部速度和肌肉激活与实验结果或动态优化结果的吻合度欠佳。
