How to Accurately Determine the Position on a Known Course in Road Cycling

Wolf, Steffen; Dobiasch, Martin; Gonzalez, Alexander Artiga; Saupe, Dietmar

doi:10.1007/978-3-319-67846-7_11

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the first step, after beginning the ride, the current speed, power, and GPS position are measured. Based on the GPS and speed measurements, the current position on the course is estimated employing a Kalman filter [22]. It was shown in a field experiment under realistic cycling conditions, that this approach achieved an accuracy of less than 1 m , which should be sufficiently good for this application.…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Adaptive feedback system for optimal pacing strategies in road cycling

2019

Self Cite

View full text Add to dashboard Cite

In road cycling, the pacing strategy plays an important role, especially in solo events like individual time trials. Nevertheless, not much is known about pacing under varying conditions. Based on mathematical models, optimal pacing strategies were derived for courses with varying slope or wind, but rarely tested for their practical validity. In this paper, we present a framework for feedback during rides in the field based on optimal pacing strategies and methods to update the strategy if conditions are different than expected in the optimal pacing strategy. To update the strategy, two solutions based on model predictive control and proportional-integral-derivative control, respectively, are presented. Real rides are simulated inducing perturbations like unexpected wind or errors in the model parameter estimates, e.g., rolling resistance. It is shown that the performance drops below the best achievable one taking into account the perturbations when the strategy is not updated. This is mainly due to premature exhaustion or unused energy resources at the end of the ride. Both the proposed strategy updates handle those problems and ensure that a performance close to the best under the given conditions is delivered.

show abstract

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%