Passive suspension optimization of a quarter car using preview control with the spectral decomposition method

Abstract

In this paper, control of a quarter car vehicle model with optimized passive suspension elements is presented. The vehicle is considered to travel on a rough road which is modeled as the power spectral density of the random road excitation given by integrated white noise that can be approximated by a deterministic step input. The weighted sum of the control force, suspension travel and road holding is minimized by using the optimal preview control law and the spectral decomposition method is used for obtaining the response. The parameters of a passive suspension system, namely spring stiffness and damping coefficient are optimally determined by the mean square equivalence of control force of the passive suspension to control force obtained by the stochastic optimal preview controller. The optimal parameters are also calculated by coordinating the passive suspension performance with the performance of the active preview control and the results show that the optimized passive system performance closely tracks the active system performance.