Adaptive optimal control of pneumatic suspensions for comfort improvement of flexible railway vehicles using Monte Carlo simulations.

This paper presents an adaptive optimal control strategy focused on improving ride comfort by minimising the frequency-weighted acceleration measured at three locations on a flexible carbody floor. Front and rear suspensions adapt the restriction orifice, which connects each pneumatic spring to its auxiliary air reservoir, thus conferring different dynamic stiffness and damping depending on train speed and track quality. In this work, as opposed to previous publications by our group, the optimal control problem aims at the particular frequency range which affects passenger comfort in accordance to standard UNE-EN-12299. In addition, a rigorous statistical analysis based on Monte Carlo and Bootstrap sampling techniques has been used to deal with the stochastic nature of track irregularities. Results prove that focusing on the comfort frequency range improves the performance of the adaptive suspension, and that the proposed statistical analysis of optimisation results guarantees the appropriate selection of optimal diameters. [ABSTRACT FROM AUTHOR]