论文标题
通过贝叶斯优化对半活性悬架控制器进行实验性自动校准
Experimental Automatic Calibration of a Semi-Active Suspension Controller via Bayesian Optimization
论文作者
论文摘要
公路车辆的半活动悬架系统的终端（EOL）通常是一项至关重要且昂贵的任务，需要车辆和控制专家团队以及许多小时的专业驾驶。在本文中，我们提出了一种纯粹的基于数据的调整方法，可以通过依靠几乎没有实验时间并利用贝叶斯优化工具来自动校准专有悬架控制器的参数。还提供了有关如何选择算法自由度的详细方法。在商业多体模拟器以及真实的汽车上评估了所提出的方法的有效性。
The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as well as many hours of professional driving. In this paper, we propose a purely data-based tuning method enabling the automatic calibration of the parameters of a proprietary suspension controller by relying on little experimental time and exploiting Bayesian Optimization tools. A detailed methodology on how to select the most critical degrees of freedom of the algorithm is also provided. The effectiveness of the proposed approach is assessed on a commercial multi-body simulator as well as on a real car.
