Suspension system state estimation using adaptive Kalman filtering based on road classification

ABSTRACT

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.

KEYWORDS:

• State estimation
• road classification
• AKF
• noise variance
• suspension system

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Yechen Qin http://orcid.org/0000-0003-1928-0113

Additional information

Funding

This work is supported by the National Nature Science Foundation of China (grant number U1564210), Innovative Talent Support Program for Chinese Post Doctorates (grant number BX201600017) and China Postdoctoral Science Foundation (grant number 2016M600934).