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Experimental identification of structural dynamics models is usually based on the modal analysis approach. In the classical modal parameter estimation approach, the baseline data which are processed are Frequency Response Functions measured in laboratory conditions. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test. Hence, the need arises to identify a modal model in operational conditions. This issue is even more complicated by the fact that in most cases, only response data are measurable while the actual loading conditions are unknown. Therefore, the system identification process will need to base itself on output-only data.

Active suspension systems aim at increasing safety by improving vehicle ride and handling performance while ensuring superior passenger comfort. Good control of this active system can only be achieved by providing the control algorithm with reliable and accurate signals for the required quantities. This paper presents the design and development of a state estimator that accurately provides the information required by a sky-hook controller, using a minimum of sensors. The vehicle inertial parameters are estimated by an algorithm based on Monte Carlo simulations and anthropometric data. All state updating is performed using Kalman filters. The resulting performance enhancement has been proven during test drives.