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International Journal of Automotive Technology > Volume 24(5); 2023 > Article
International Journal of Automotive Technology 2023;24(5): 1335-1347.
doi: https://doi.org/10.1007/s12239-023-0108-4
CENTER OF GRAVITY POSITION ESTIMATION OF COUNTERBALANCED FORKLIFT TRUCK BASED ON MULTI MODEL DATA FUSION
Guang Xia 1,2, Chenhao Zhang 2, Xiwen Tang 3, Yang Zhang 2, Linfeng Zhao 2
1Key Laboratory for Automated Vehicle Safety Technology of Anhui Province, Hefei University of Technology
2School of Automotive and Traffic Engineering, Hefei University of Technology
3Institute of Radar Confrontation, National University of Defense Technology
PDF Links Corresponding Author.  Guang Xia  , Email. xiaguang008@hfut.edu.cn
ABSTRACT
The center of gravity of a forklift truck, a crucial parameter for vehicle stability, changes with different loads during operation. We propose an estimation algorithm for the center of gravity position suitable for a counterbalanced forklift truck. By installing sensors on the fork, we use an inclinable platform and propose a static joint center of gravity measurement method. For straight-line driving, we establish a longitudinal dynamics model and propose a nonlinear H∞ estimation algorithm. For steering conditions, we establish a roll dynamics model and propose a forgetting factor recursive least square estimation algorithm. A data fusion algorithm for the forklift truck’s center of gravity position under various working conditions is proposed. The fusion of these estimation results yields the best estimated center of gravity height. We validate the algorithm’s effectiveness using a hardware-in-the-loop simulation platform under different working conditions. The experiments demonstrate the algorithm’s fast parameter fitting, wide applicability, and accurate position control within a 5 % error range.
Key Words: Counterbalanced forklift truck, Center of gravity, Parameter estimation, Multi model data fusion
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