Road roughness estimation based on discrete Kalman filter with unknown input

ABSTRACT

The road roughness acts as a disturbance input to the vehicle dynamics, and causes undesirable vibrations associated with the ride and handing characteristics. Furthermore, the accurate measurement of road roughness plays a key role in better understanding a vehicle dynamic behaviour and active suspension control systems. However, the direct measurement by laser profilometer or other distance sensors are not trivial due to technical and economic issues. This study proposes a new road roughness estimation method by using the discrete Kalman filter with unknown input (DKF-UI). This algorithm is built on a quarter-car model and uses the measurements of the wheel stroke (suspension deflection), and the acceleration of the sprung mass and unsprung mass. The estimation results are compared to the measurements by laser profilometer in-vehicle test.

KEYWORDS:

• Kalman filter
• unknown input
• quarter-car suspension model
• road roughness estimation

Acknowledgements

The authors would like to thank Sang-Hyeok Seo and Yun-Ho Jung (HanKook tire) for providing technical assistance during the in-vehicle test.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Gi-Woo Kim  https://orcid.org/0000-0003-4625-0382

Additional information

Funding

This work was supported by the INHA UNIVERSITY Research Grant.