Citation

Singh KB, Taheri S. Int. J. Veh. Perform. 2019; 5(4): 329-376.

Copyright

(Copyright © 2019, Inderscience Publishers)

DOI

10.1504/IJVP.2019.104082

PMID

unavailable

Abstract

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, a real-time estimate of the vehicle handling dynamic states and tyre-road contact parameters are invaluable for enhancing the performance of current vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production cars are equipped with onboard sensors (e.g., a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors) which when used in conjunction with certain model based observers can be used to identify relevant vehicle states for optimal control of comfort, stability and handling. However, some key variables such as the tyre forces, road bank/grade angles, and the tyre-road friction coefficient, which have a significant impact on vehicle handling performance and safety are difficult to measure using sensors already onboard vehicles. This paper introduces an integrated vehicle state estimator comprising a series of model-based and kinematic-based observers for estimating these unmeasurable states. Using an appropriate vehicle model, kinematic equations of motion and vehicle sensor data, the unknown vehicle states as well as the tyre-road contact forces are estimated by implementing a series of observers arranged in a cascade structure. Key estimated signals include the vehicle side slip angle (β), tyre longitudinal/lateral/vertical forces, and the tyre-road friction coefficient (μ). The performance of the proposed estimators has been evaluated via computer simulations conducted using the vehicle dynamics software CarSim®. An effectively designed merging scheme ensures robust estimation performance even during the vehicle manoeuvres which show highly nonlinear tyre characteristics and in the existence of road inclination or bank angle.

Keywords: state estimation; parameter estimation; SMO; sliding mode observer; KF; Kalman filter; RLS; recursive least squares.

Language: en