In this paper, a new control algorithm for active suspension systems is presented. Latter is designed to improve low frequency comfort, which gains importance with autonomous driving and the possibility for nondriving tasks like in-car working. The algorithm is based on model predictive controllers and makes use of environment sensor information and extends preview by taking the future trajectory of the vehicle into account. A simulative benchmark demonstrates the advantages compared to existing control algorithms. Vertical- as well as roll- and pitch-acceleration can be significantly reduced by at least 20% compared to a non-preview controller up to a frequency of 5 Hz. A prototype vehicle implementation of the new controller enables a subjective and objective review of the resulting vehicle motion and confirms the results of the simulation. Despite of limitations on the precision of the preview information, vertical acceleration could be reduced by 5.5% in this implementation. It was shown that a future series implementation is possible and can be used to improve autonomous driving comfort by increasing low frequency suspension performance and efficient integration of additional features such as curve tilting.

在本文中，提出了一种新的主​​动悬架系统控制算法。后者旨在提高低频舒适度，这在自动驾驶和非驾驶任务（如车内工作）的可能性中变得越来越重要。该算法基于模型预测控制器，利用环境传感器信息，通过考虑车辆的未来轨迹来扩展预览。模拟基准展示了与现有控制算法相比的优势。与频率高达 5 Hz 的非预览控制器相比，垂直以及滚动和俯仰加速度可以显着降低至少 20%。新控制器的原型车辆实施能够对产生的车辆运动进行主观和客观的审查，并确认模拟结果。尽管预览信息的精度受到限制，但在此实现中垂直加速度可以降低 5.5%。结果表明，未来的系列实施是可能的，可用于通过提高低频悬架性能和有效集成曲线倾斜等附加功能来提高自动驾驶舒适性。