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A little damping goes a long way: a simulation study of how damping influences task-level stability in running

2020

Article

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It is currently unclear if damping plays a functional role in legged locomotion, and simple models often do not include damping terms. We present a new model with a damping term that is isolated from other parameters: that is, the damping term can be adjusted without retuning other model parameters for nominal motion. We systematically compare how increased damping affects stability in the face of unexpected ground-height perturbations. Unlike most studies, we focus on task-level stability: instead of observing whether trajectories converge towards a nominal limit-cycle, we quantify the ability to avoid falls using a recently developed mathematical measure. This measure allows trajectories to be compared quantitatively instead of only being separated into a binary classification of ‘stable' or ‘unstable'. Our simulation study shows that increased damping contributes significantly to task-level stability; however, this benefit quickly plateaus after only a small amount of damping. These results suggest that the low intrinsic damping values observed experimentally may have stability benefits and are not simply minimized for energetic reasons. All Python code and data needed to generate our results are available open source.

Author(s): Steve Heim and Matthew Millard and Charlotte Le Mouel and Alexander Badri-Spröwitz
Journal: Biology Letters
Volume: 16
Number (issue): 9
Year: 2020
Month: September

Department(s): Dynamic Locomotion, Intelligent Control Systems
Bibtex Type: Article (article)

DOI: 10.1098/rsbl.2020.0467
URL: https://doi.org/10.1098/rsbl.2020.0467

BibTex

@article{heim2020damping,
  title = {A little damping goes a long way: a simulation study of how damping influences task-level stability in running},
  author = {Heim, Steve and Millard, Matthew and Mouel, Charlotte Le and Badri-Spr{\"o}witz, Alexander},
  journal = {Biology Letters},
  volume = {16},
  number = {9},
  month = sep,
  year = {2020},
  url = {https://doi.org/10.1098/rsbl.2020.0467},
  month_numeric = {9}
}