Abstract
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 and parameter estimator that accurately provides the information required by a sky-hook control law, using a minimum of sensors. The vehicle inertial parameters are estimated by an algorithm based on Monte Carlo simulations and anthropometric data. All vehicle state updating is performed using Kalman filters. The presented estimation algorithm has been successfully adopted in an active suspension system on a prototype vehicle and the resulting performance enhancement has been validated experimentally during test drives.
KEYWORDS: Active suspension system, vehicle inertial parameter estimation, vehicle state estimation, Monte Carlo simulations, Kalman filtering.