Roll & Bank Estimation Using GPS/INS and Suspension Deflections
Type of Degreethesis
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This thesis presents three methods that provide and estimate of road bank by decoupling the vehicle roll due to dynamics and roll due road bank. Suspension deflection measurements were used to provide a measurement of the relative roll between the vehicle body frame and the axle frame, or between the sprung mass and the unsprung mass respectively. A method of scaling the suspension deflection measurements to vertical wheel motion was explored. A deflection scaling parameter was found by both a dynamics based method and a suspension geometry based method. The parameter was determined to effectively scale the suspension deflection measurements with minimum error variances over varying vehicle speeds. The relative roll measurement was then incorporated into three different estimation architectures. A vehicle model based Kalman filter (KF) observer and two kinematic navigation model based extended Kalman filters (EKF) were developed. The first EKF used a cascaded approach to incorporate the relative roll measurement. The EKF second, a coupled approach, augmented the state vector with a state for the road bank. The road bank was modeled as a time varying disturbance and a measurement update for the relative roll measurement was developed. All the estimators were used to decouple the vehicle roll due to dynamics and the roll due to bank. Each algorithm was tested in simulation with data from CarSim 6, a vehicle dynamic modeling software package. The estimators were then tested on the Prowler ATV experimental platform at the National Center for Asphalt Technology (NCAT). The KF vehicle model based estimator correctly estimated the road bank under low dynamics in simulation but was susceptible to vehicle model uncertainties and nonlinearities. Both the cascaded and coupled approach performed well for both simulation and experimental data. The EKFs correctly estimated the road crown and banked turns of the NCAT Oval track. The coupled EKF displayed the added benefit of filtering the noise on the bank estimate. Between the three estimation approaches the coupled kinematic based EKF approach was determined to be the best method. The vehicle model based approach proved to be very sensitive to the vehicle model. Small deviations in the model led to large bank errors and poor performance under high dynamics. Both of the kinematic based approaches performed well across all ranges of dynamics and road bank disturbances. However, the coupled approach filtered the noise on the bank estimate which was determined to be advantageous.