Improving active suspension performance by means of advanced vehicle state and parameter estimation

Active suspension systems aim to increase safety by improving vehicle ride and handling performance while ensuring superior passenger comfort. To achieve good control of this system, the control algorithm must be provided with reliable and accurate input signals. 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.