Estimation of vehicle suspension dynamics with data fusion for correcting measurement errors

Abstract

This paper presents a novel algorithm that aims to estimate an accurate dynamic model for a vehicle suspension system. The proposed method addresses the uncertainties associated with the system model and unknown road input by incorporating complementary terms. These terms are calculated using information from accelerometers installed on both the sprung and unsprung masses. To correct measurement errors, including bias and noise, the paper suggests fusing the accelerometer data with displacement data. The paper further introduces an adaptive solution for adjusting the estimator coefficients, enabling effective data fusion. An experimental implementation is conducted on a quarter-car suspension platform, equipped with additional sensors to verify the estimated responses. Comparative results with the unknown-input Kalman filter highlight the superior accuracy of the proposed estimator in constructing a precise dynamic model for the system. Additionally, the results demonstrate the effectiveness of data fusion-based adaptive tuning of the estimator coefficients in correcting measurement errors.