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A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates

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  • Eric Maris

Abstract

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.

Suggested Citation

  • Eric Maris, 2023. "A bicycle can be balanced by stochastic optimal feedback control but only with accurate speed estimates," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-33, February.
    • Handle: RePEc:plo:pone00:0278961
    DOI: 10.1371/journal.pone.0278961
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    References listed on IDEAS

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    1. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
    2. repec:plo:pcbi00:1000419 is not listed on IDEAS
    3. Paul L. Gribble & Stephen H. Scott, 2002. "Overlap of internal models in motor cortex for mechanical loads during reaching," Nature, Nature, vol. 417(6892), pages 938-941, June.
    4. Arne J Nagengast & Daniel A Braun & Daniel M Wolpert, 2010. "Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-15, July.
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