ABSTRACT

This paper presents an adaptive optimal control strategy focused on improving ride comfort by minimising the root mean square of accelerations measured at seven locations on the carbody floor. It requires preview knowledge of both the train speed and the standard track quality (not the deterministic track disturbances) and differs from other works in considering pointwise constraints (upper and lower secondary suspension travel limits). Adaptive control is achieved by varying the diameter of the restriction orifice which connects the pneumatic spring to the auxiliary air reservoir, which confers different dynamic stiffness and damping to the front and rear secondary suspensions. Decision maps with the front and rear optimal diameters for five track irregularity levels and six different speeds were obtained using a Monte Carlo strategy based on solving the optimal control problem for 240 stochastic realisations of time-varying track irregularities of 300 s. Results show that the adaptive optimal control provides a successful compromise between comfort and safety which overshadows the conventional passive approach: it improves comfort up to 50% with respect to stiff passive configurations and it guarantees constraint fulfilment unlike compliant passive configurations.

摘要

本文提出了一种自适应最优控制策略，该策略着重于通过最小化在车身底板上七个位置测得的加速度的均方根来提高乘坐舒适性。它需要有关火车速度和标准轨道质量（而不是确定性的轨道干扰）的预备知识，并且在考虑点约束（次级悬架行程上限和下限）方面与其他工作有所不同。通过改变将气动弹簧连接到辅助储气罐的节流孔的直径来实现自适应控制，这将赋予前后副悬架不同的动力刚度和阻尼。基于蒙特卡罗策略，基于求解240 s 300时变轨道不规则随机实现的最优控制问题，使用蒙特卡洛策略获得了具有五个最优轨道速度和六个最优速度的前后最优直径的决策图。结果表明，自适应最优控制在舒适性和安全性之间取得了成功的折衷，这使传统的被动方法黯然失色：相对于刚性被动配置，它可以将舒适性提高多达50％，并且与合规被动配置不同，它可以确保约束的实现。