Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 353, Issue 4, Pages 834-856Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2016.01.005
Keywords
-
Categories
Funding
- International Cooperation Research Program of China [2015DFE72810]
- Higher School Discipline Innovation Intelligence Plan (111 plan)
Ask authors/readers for more resources
This paper researched an estimation method based on the Minimum Model Error (MME) criterion combing with the Extended Kalman Filter (EKF) for 4WD vehicle states. A general 5-input 3-output and 3 states estimation system was established, considering both the arbitrary nonlinear model error and the white Gauss measurement noise. Aiming at eliminating the estimation error caused by the arbitrary nonlinear model error, the prediction algorithm for the dynamic tire force error was deduced based on the MME criterion, based on which the system model can be effectively updated for higher estimation accuracy. The estimation algorithm was applied to a two-motor-driven vehicle during a double-lane-change process with varying speed under simulative experimental condition. The results showed that the dynamic tire force error could be effectively found for updating the system model, and higher estimation accuracy of the vehicle states were achieved, when compared with the traditional EKF estimator. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available