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Sport Sciences for Health
Published in: Sport Sciences for Health 2/2017

01-08-2017 | Original Article

An optimal control solution to the predictive dynamics of cycling

Authors: Andrea Zignoli, Francesco Biral, Barbara Pellegrini, Azim Jinha, Walter Herzog, Federico Schena

Published in: Sport Sciences for Health | Issue 2/2017

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Abstract

Purpose

Pure predictive dynamics aims at predicting the set of driving inputs in the absence of any a priori data and can be applied in movement science to generate biomechanical variables in many different what-if scenarios. The objective of this research was to solve the problem of the predictive dynamics of sub-maximal cycling by means of an optimal control computational algorithm that makes use of an indirect method.

Methods

To this, a 2D two-legged seven bodies three degrees of freedom model of the lower limbs of a cyclist has been developed and validated against the average behaviour of eight well-trained cyclists pedalling at different sub-maximal intensities (100, 220, 300 W) at constant cadence (90 rpm). Experimental data adopted in model validation consists of the hip, knee, ankle joint centre and crank kinematics and the right/left crank torques.

Results

It has been found that the model can replicate the major features of pedalling biomechanics and the ability of a cyclist to deliver a larger torque if a larger power output is required and the cadence is kept constant. The reported mismatches with experimental data get smaller as the power output increases.

Conclusions

It is suggested that: (1) an optimal control based on an indirect method approach can provide a solution to the predictive dynamics of sub-maximal cycling, (2) predictive dynamics adapts accordingly to real data for changes in power output.
Appendix
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Metadata
Title
An optimal control solution to the predictive dynamics of cycling
Authors
Andrea Zignoli
Francesco Biral
Barbara Pellegrini
Azim Jinha
Walter Herzog
Federico Schena
Publication date
01-08-2017
Publisher
Springer Milan
Published in
Sport Sciences for Health / Issue 2/2017
Print ISSN: 1824-7490
Electronic ISSN: 1825-1234
DOI
https://doi.org/10.1007/s11332-017-0370-9

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