Center of Gravity Position Estimation of Counterbalanced Forklift Truck Based on Multi Model Data Fusion

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Center of Gravity Position Estimation of Counterbalanced Forklift Truck Based on Multi Model Data Fusion

Xia, Guang / Zhang, Chenhao / Tang, Xiwen / Zhang, Yang / Zhao, Linfeng

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.