STATE ESTIMATION OF ROAD SURFACE AND VEHICLE SYSTEM USING A KALMAN FILTER

In this paper, an approach to estimating the state variables of a vehicle system and the absolute displacement of a road surface is presented. The Kalman filter theory is applied to an augmented system which consists of the vehicle system and the shaping filter. The shaping filter is taken so that the power spectral density of output is equal to that of the displacement of the road surface, while white noise is taken as input. The Pade approximation is used in the augmented system to take account of the time delay between the input values of the front and rear wheels. The results obtained using the Pade approximation are compared with the results obtained by assuming that the inputs of the four wheels are independen of each other. A method for identifying the shaping filter in the augmented system, which may be effective in constructing the augmented system adaptively in actual driving case, is also presented. Some simulation models are examined to validate the presented methods.