Robust motion estimation for vehicle dynamics applications using simplified models

Sammanfattning: The overall aim of this thesis is to explore the accurate estimation methods for the vehicle motion with relatively cheap sensors. The vehicle states are essential to the vehicle control applications but sometimes expensive sensors are necessary to obtain accurate values. At first, a validation work for the rigid body motion estimation has been done and the results show that accurate linear and rotational accelerations can be achieved only with low-cost accelerome-ters. The main part of this work focuses on developing an estimator for the vehicle body angle, angle rate (including both roll and pitch) and the road an-gle, as a key block of the overall project Vehicle Dynamics Estimation. The estimation results are the inputs of another estimation block: vehicle lateral dynamic estimator; and part of the important inputs of the angle estimator (velocities and the time derivative) also come from the lateral dynamic esti-mator instead of the expensive sensors. The estimation technique employed in this work is the linear augmented Kalman filter with the unknown road angles as the augmented estimation states. The roll and pitch motion are assumed to be decoupled with each other, and the linear mass-damper-spring dynamic model is adopted to obtain the equations of the vehicle states. Some unknown parameters shown in the dynamic equations are identified at first with SimRod testing data and the results are satisfactory. The road angles are modeled as a zero-order random walk model. The bicycle model, vehicle body-road and ve-hicle body-frame kinematics are used to derive the measurement equations of the Kalman filter. After the simulation and measurement inputs are obtained, the process and measurement error covariance are tuned to finally decide the estimation results. Also, SimRod testing data are used to validate the results, and the estimation performance for the vehicle body angle and angle rate are good; while the road angles need to be further validated with more available data set.