Prediction of bicycle dynamics parameters based on machine learning

B. Li
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Abstract

There are many control methods for the stable motion of a bicycle, and some scholars have verified that the stable motion of the bicycle can be achieved through the proportional control of the inclination of the body. This paper combines machine learning with bicycle dynamics parameter prediction, and uses neural network to fit the relationship between the dynamic parameters at time t and t+Δt, and predict the inclination of the body at time t+Δt. The fitting result proves that the neural network method can effectively fit the law, and the prediction effect is better.
基于机器学习的自行车动力学参数预测
自行车稳定运动的控制方法有很多种,有学者已经验证,通过对车身倾斜度的比例控制可以实现自行车的稳定运动。本文将机器学习与自行车动力学参数预测相结合,利用神经网络拟合t时刻与t+Δt时刻的动力学参数关系,预测t+Δt时刻车身的倾斜度。拟合结果证明,神经网络方法能有效拟合规律,预测效果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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