Abstract
Abstract
This study assesses the problem of describing the vertical dynamics of a real commercial passenger vehicle, using vibration acceleration measurements at five distinct points for the estimation of vector autoregressive moving average models (VARMA). To this, a novel parametric identification method is used, based on the implementation of a hybrid optimization algorithm and a corresponding estimation procedure. The specific methodology interconnects the diverse characteristics of deterministic and stochastic optimization techniques, combining high convergence rate with increased reliability in the search for global optimum. Consequently, the corresponding estimation procedure is split into two parts, due to the mixed linear-nonlinear relationship between the prediction errors and the parameter vector, and assures stability and invertibility in the resulting models.
Keywords: system identification, hybrid methods, vehicle dynamics.