Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
PLoS One. 2023 Feb 27;18(2):e0278961. doi: 10.1371/journal.pone.0278961. eCollection 2023.
Balancing a bicycle is typical for the balance control humans perform as a part of a whole range of behaviors (walking, running, skating, skiing, etc.). This paper presents a general model of balance control and applies it to the balancing of a bicycle. Balance control has both a physics (mechanics) and a neurobiological component. The physics component pertains to the laws that govern the movements of the rider and his bicycle, and the neurobiological component pertains to the mechanisms via which the central nervous system (CNS) uses these laws for balance control. This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC). The central concept in this model is a computational system, implemented in the CNS, that controls a mechanical system outside the CNS. This computational system uses an internal model to calculate optimal control actions as specified by the theory of stochastic OFC. For the computational model to be plausible, it must be robust to at least two inevitable inaccuracies: (1) model parameters that the CNS learns slowly from interactions with the CNS-attached body and bicycle (i.e., the internal noise covariance matrices), and (2) model parameters that depend on unreliable sensory input (i.e., movement speed). By means of simulations, I demonstrate that this model can balance a bicycle under realistic conditions and is robust to inaccuracies in the learned sensorimotor noise characteristics. However, the model is not robust to inaccuracies in the movement speed estimates. This has important implications for the plausibility of stochastic OFC as a model for motor control.
自行车平衡对于人类作为一系列行为（行走、跑步、滑冰、滑雪等）的一部分进行的平衡控制来说是典型的。本文提出了一种平衡控制的通用模型，并将其应用于自行车的平衡。平衡控制既有物理（力学）成分，也有神经生物学成分。物理成分涉及支配骑手及其自行车运动的规律，神经生物学成分涉及中枢神经系统（CNS）利用这些规律进行平衡控制的机制。本文提出了基于随机最优反馈控制（OFC）理论的神经生物学成分的计算模型。该模型的核心概念是一种计算系统，它在 CNS 中实现，控制 CNS 外部的机械系统。该计算系统使用内部模型根据随机 OFC 理论计算最优控制动作。为了使计算模型具有合理性，它必须至少对两种不可避免的不准确性具有鲁棒性：（1）CNS 通过与 CNS 附着的身体和自行车的相互作用缓慢学习的模型参数（即内部噪声协方差矩阵），以及（2）取决于不可靠的感觉输入的模型参数（即运动速度）。通过模拟，我证明了该模型可以在现实条件下平衡自行车，并且对学习的感觉运动噪声特性的不准确性具有鲁棒性。然而，该模型对运动速度估计的不准确性不具有鲁棒性。这对随机 OFC 作为运动控制模型的合理性具有重要意义。
