Sharif Razavian Reza, Ghannadi Borna, McPhee John
Systems Design Engineering,University of Waterloo,Waterloo, ON N2L 3G1, Canadae-mail:
Fellow ASMEProfessorSystems Design Engineering,University of Waterloo,Waterloo, ON N2L 3G1, Canadae-mail:
J Biomech Eng. 2019 Mar 1;141(3). doi: 10.1115/1.4042185.
This paper presents a computational framework for the fast feedback control of musculoskeletal systems using muscle synergies. The proposed motor control framework has a hierarchical structure. A feedback controller at the higher level of hierarchy handles the trajectory planning and error compensation in the task space. This high-level task space controller only deals with the task-related kinematic variables, and thus is computationally efficient. The output of the task space controller is a force vector in the task space, which is fed to the low-level controller to be translated into muscle activity commands. Muscle synergies are employed to make this force-to-activation (F2A) mapping computationally efficient. The explicit relationship between the muscle synergies and task space forces allows for the fast estimation of muscle activations that result in the reference force. The synergy-enabled F2A mapping replaces a computationally heavy nonlinear optimization process by a vector decomposition problem that is solvable in real time. The estimation performance of the F2A mapping is evaluated by comparing the F2A-estimated muscle activities against the measured electromyography (EMG) data. The results show that the F2A algorithm can estimate the muscle activations using only the task-related kinematics/dynamics information with ∼70% accuracy. An example predictive simulation is also presented, and the results show that this feedback motor control framework can control arbitrary movements of a three-dimensional (3D) musculoskeletal arm model quickly and near optimally. It is two orders-of-magnitude faster than the optimal controller, with only 12% increase in muscle activities compared to the optimal. The developed motor control model can be used for real-time near-optimal predictive control of musculoskeletal system dynamics.
本文提出了一种利用肌肉协同作用对肌肉骨骼系统进行快速反馈控制的计算框架。所提出的运动控制框架具有层次结构。层次结构中较高层的反馈控制器处理任务空间中的轨迹规划和误差补偿。这个高层任务空间控制器仅处理与任务相关的运动学变量，因此计算效率高。任务空间控制器的输出是任务空间中的一个力向量，该向量被馈送到低层控制器以转换为肌肉活动命令。采用肌肉协同作用以使这种力到激活（F2A）映射在计算上高效。肌肉协同作用与任务空间力之间的明确关系允许快速估计产生参考力的肌肉激活。基于协同作用的F2A映射通过一个可实时求解的向量分解问题取代了计算量大的非线性优化过程。通过将F2A估计的肌肉活动与测量的肌电图（EMG）数据进行比较来评估F2A映射的估计性能。结果表明，F2A算法仅使用与任务相关的运动学/动力学信息就能以约70%的准确率估计肌肉激活。还给出了一个预测模拟示例，结果表明这种反馈运动控制框架能够快速且近乎最优地控制三维（3D）肌肉骨骼手臂模型的任意运动。它比最优控制器快两个数量级，与最优情况相比肌肉活动仅增加12%。所开发的运动控制模型可用于肌肉骨骼系统动力学的实时近最优预测控制。
