In a method for controlling at least one component of a chassis of a vehicle, a parameterization of a reactive controller of the at least one component of the chassis is changed depending on a current certainty of sensor data of a roadway section to be driven detected by a sensor system, such that, when driving on the roadway section, in the case of increased uncertainty of the sensor data, the reactive controller controls with a lower reaction time with respect to a normal operation.

A system for determining a displacement velocity signal for controlling an active wheel suspension of a land vehicle by open-loop and/or closed-loop control includes at least one Kalman filter, and at least one acceleration sensor arranged on a sprung mass of the land vehicle to sense a vertical acceleration of the sprung mass and to generate a corresponding acceleration signal supplied to the Kalman filter. The Kalman filter includes a mathematical motion model of the sprung mass, and input states of the Kalman filter include a vertical acceleration of the sprung mass, a vertical displacement velocity of the sprung mass, and a vertical displacement distance of the sprung mass. A displacement measurement signal having a value 0 is supplied continuously to the Kalman filter to determine the displacement velocity signal. Constant noise variance values of a measurement noise covariance matrix of the Kalman filter that are assigned to the displacement measurement signal are, in each case, set at one half of a maximum vertical displacement distance of the sprung mass.

Realized is a technique for estimating a state quantity of a vehicle, which technique is applicable to estimation of a vehicle weight and allows an increase in accuracy and speed of the estimation. A state quantity estimating device includes a data storing section (101), a predictive quantity computing section (102), an obtaining section (107), a Kalman gain computing section (103), an estimated quantity computing section (104) which calculates an estimated state quantity and estimated covariance, and a process noise covariance correcting section (106) which corrects process noise covariance. The estimated state quantity, the estimated covariance, and the process noise covariance, each of which has been calculated or corrected, are written in the data storing section (101) as a state quantity, state covariance, and process noise covariance, respectively, and are used in a next computation for estimating a state quantity.