Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (11): 1642-1647.DOI: 10.12068/j.issn.1005-3026.2018.11.024

• Mechanical Engineering • Previous Articles     Next Articles

Vehicle Suspension System State Estimation Combining with Interacting Multiple Model Kalman Filter

GU Liang, WANG Zhen-yu, WANG Zhen-feng   

  1. School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081,China.
  • Received:2017-08-21 Revised:2017-08-21 Online:2018-11-15 Published:2018-11-09
  • Contact: WANG Zhen-feng
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Abstract: In order to estimate accurately the vehicle suspension state, an interactive multiple model adaptive Kalman filter(IMMKF)state observer was proposed. Firstly, a standard road excitation model and a quarter vehicle linear model were established. Secondly, by combining recursive least square algorithm with IMMKF theory, an IMMKF state observer was designed based on the augmented suspension model in various working conditions. Finally, the influence on the state estimation of the suspension system with the change of sprung mass under the ISO level C road input excitation was analyzed. The results of simulation and experiment on a quarter of vehicle test rig showed that compared with the tradition Kalman filter(KF)state observer, the estimation accuracy of the proposed IMMKF state observer could be improved beyond 20% with the change of sprung mass.

Key words: state estimation, interacting multiple model Kalman filter(IMMKF), recursive least square algorithm, suspension system, sprung mass

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