Estimation of lateral disturbance under parameter uncertainties

Abstract

This paper presents a lateral disturbance estimator based on Motor Driven Power Steering (MDPS)-based driving assistant system considering parameter uncertainties. A vehicle motion can be laterally deviated by lateral disturbance including wind force and load from bank angle. MDPS systems using motors to assist steering torque have become common in production vehicles. The motor-assisted torque can be controlled by the motor overlay torque within a physically feasible range. To determine MDPS motor overlay torque for compensating lateral disturbance, the information of lateral disturbance is necessary and estimated data was used due to measurement difficulties. The proposed estimator includes a tire self-aligning torque estimator based on Kalman filter with a 2-degree-of freedom (2-DOF) bicycle model. A simulation study was conducted to analyze the influence of parameter variation and to investigate the appropriate parameters under various load conditions. The performance of the proposed estimator considering the vehicle parameter uncertainties has been evaluated for several levels of the lateral disturbances. The estimation algorithm has been evaluated via closed-loop simulation and test data.