Adaptive feedback system for optimal pacing strategies in road cycling
Adaptive feedback system for optimal pacing strategies in road cycling
复制标题
DOI：
10.1007/s12283-019-0294-5
复制
发表时间：
2019-03-01
影响因子：
1.7
通讯作者：
Saupe, Dietmar
中科院分区：
文献类型：
--
作者：
Wolf, Stefan;Biral, Francesco;Saupe, Dietmar
在公路自行车比赛中，配速策略起着重要作用，尤其是在个人计时赛等单人比赛中。然而，对于不同条件下的起搏知之甚少。基于数学模型，针对不同坡度或风力的路线推导了最佳配速策略，但很少测试其实际有效性。在本文中，我们提出了一个基于最佳节奏策略的现场骑行反馈框架，以及在条件与最佳节奏策略中预期不同时更新策略的方法。为了更新策略，提出了两种分别基于模型预测控制和比例积分微分控制的解决方案。模拟真实的游乐设施，会引起意外风等扰动或模型参数估计错误（例如滚动阻力）。结果表明，考虑到策略未更新时的扰动，性能会低于可实现的最佳性能。这主要是由于骑行结束时过早耗尽或未使用能源造成的。拟议的策略更新都解决了这些问题，并确保在给定条件下提供接近最佳的性能。
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.