Dynamic state estimation of low and high speed passenger sedan using Linear Kalman Filter on dry and wet surface

Control of dynamic system often need the overall information of the states of the system. Instead of using expensive sensors for measuring the states estimators can be an economical alternative for this task. In this paper, the Linear Kalman Filter (LKF) is used as a state estimator in order to measure the states of a medium passenger sedan and also functioned as an efficient filter for eliminating added noise in the input. The linear time invariant vehicle model consisting of error variables such as the error of the distance of the center of gravity (c.g.) of the vehicle from the center line of the lane and the orientation error of the vehicle with respect to the road is used in this research which is quite apposite to develop a lateral control system. For the modelling of the passenger sedan, single track model of car-like ground vehicle is used. The experiments done in this paper account for both dry and wet road surfaces and also variation of the speed of the vehicle is implemented. This paper proposes the implementation of LKF as a state measuring sensor which is demonstrated in Matlab/Simulink environment.