Parametric Identification of Vehicle’s Vertical Dynamics Using Vector Autoregressive Moving Average Models Available to Purchase

The assessment of vertical dynamics in modern ground vehicles is a difficult task with crucial importance, as it appears to be possessed by a number of conflicting objectives, such as ride comfort and stability. Thus, the effective use of possible control units is depended by the successful description of the vertical performance. The aim of this study is to provide a closed description of vehicles’ vertical dynamics using VARMA models, which are estimated by means of a novel, hybrid optimization algorithm and a corresponding estimation procedure. The hybrid algorithm interconnects the diverse characteristics of its deterministic and stochastic counterparts, while the estimation procedure assures the stability and invertibility requirements in the resulted models. For the practical implementation of the above, a five dimensional VARMA model is used for the description of a passenger vehicle, through the acquisition of noise–corrupted vertical acceleration measurements.