Vehicle Parameter Identification and its Use in Control for Safe Path Following

This thesis develops vehicle parameter identification algorithms, and applies identified parameters to a controller designed for safe path following.A tire-road friction coefficient is estimated using an in-tire accelerometer to measure acceleration signals directly from the tires. The proposed algorithm first determines a tire-road contact patch with a radial acceleration profile.The estimation algorithm is based on tire lateral deflections obtained from lateral acceleration measurements only inside the contact patch. A new model is derived for the lateral deflection profiles, which provides robustness to orientation-variation of the accelerometer body frame during tire rotation.A novel algorithm is developed to identify three inertial parameters: sprung mass, yaw moment of inertia, and longitudinal position of the center of gravity. A correlation of inertial parameters is derived and is used for the identification algorithm.Inertial parameters and vehicle states are simultaneously estimated with a dual unscented Kalman filter based on a nonlinear vehicle model.In order to activate and de-activate different modes of the proposedalgorithm, a local observability analysis is performed with the nonlinear vehicle model.The performance and robustness of the proposed approach are demonstrated with extensive CarSim simulations and experimental tests on a flat road with a constant tire-road friction coefficient.Following a curved road can be dangerous if autonomous vehicles do not take roll motion into consideration.A control algorithm is designed to prevent a dangerous vehicle state induced by roll motion while following a curved road.Roll motion is suppressed throughout cornering with model predictive control.A four-wheel nonlinear vehicle model with roll dynamics and a tire brush model are utilized for the prediction of the vehicle state. An optimal balance in the trade-off between vehicle speed androll motion is achieved with full braking as a control actuator. Identified vehicle inertial parameters are incorporated into the designed controller.CarSim simulations illustrate the performance of the proposed controller and the effect of the vehicle parameter estimator.