Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction
Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction
复制标题
DOI：
10.1371/journal.pcbi.1007414
复制
发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Jean, Frederic
中科院分区：
文献类型：
--
作者：
Berret, Bastien;Jean, Frederic
了解生物运动控制的基础是运动神经科学中的一个重要问题。最优控制理论是一个领先的框架，合理化这个问题的计算条款。以前，最优控制模型已经被设计成确定性或随机设置，以考虑运动控制的不同方面（例如，平均行为与试验间变异性）。虽然这些方法对运动控制产生了有价值的见解，但它们通常无法解释肌肉共同收缩。与运动功能相关的一组肌肉（例如跨越关节的激动肌和拮抗肌）的共同收缩有助于调节神经肌肉骨骼系统的机械阻抗（例如关节粘弹性），并且被认为主要受来自大脑的下行信号的影响。在这里，我们提出了一个理论，建议运动规划的一个主要目标可能是发出前馈（开环）电机命令，最佳地指定力和阻抗，根据嘈杂的神经肌肉骨骼动力学和最优性标准的基础上努力和方差。我们表明，所提出的框架自然占了以前的几个实验结果，通过肌肉共同收缩上肢的力和阻抗的调节。随机最优（闭环）控制，预编程反馈增益，但需要在线状态估计过程，通过长延迟的感觉反馈回路，然后可以补充这个标称前馈电机命令，以完全确定肢体的机械阻抗。建议的随机最优开环控制理论可能会提供新的见解的前馈/反馈控制机制的一般衔接和合理的肌肉共同收缩的神经控制运动的发生。
Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upperlimb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb's mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.