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International Journal of Automotive Technology > Volume 22(6); 2021 > Article
International Journal of Automotive Technology 2021;22(6): 1529-1543.
doi: https://doi.org/10.1007/s12239-021-0132-1
ANTI-ROLLOVER CONTROL BASED ON STABLE ZONE PARTITION OF COUNTERBALANCED FORKLIFT TRUCKS
Guang Xia1,3, Yan Xia1,3, Xiwen Tang2, Linfeng Zhao3, Jinfang Hu3
1Hefei University of Technology
2National University of Defense Technology
3Hefei University of Technology
PDF Links Corresponding Author.  Guang Xia , Email. aguang008@hfut.edu.cn
ABSTRACT
To improve the anti-rollover capacity of a counterbalanced forklift, a two-stage rollover dynamic model is established on the basis of the forklift structure. Stable zones are divided according to the two-stage lateral load transfer rate: stable region, relatively stable region, dangerous zone and abnormal dangerous zone. An anti-rollover layered control strategy based on stable zone partition is proposed, and different anti-rollover control actuators are selected: dynamic balance weight, anti-rollover cylinder and steering cylinder. Anti-rollover controllers consist of the upper stable region identification controller, the middle-level controller based on model predictive control (MPC) and the lower layer executive controller. The upper stable region identification controller performs stable zone recognition based on the two-stage lateral load transfer rate. The middlelevel MPC controller calculates the required control torque with the body’s lateral angle and yaw rate as the control objectives. The lower layer executive controller controls the balance weight, anti-rollover cylinder and the steering cylinder according to the improved chain incremental allocation method to meet the target control torque. Simulation and real vehicle tests based on MATLAB/Simulink show that the anti-rollover control based on stable zone partition can greatly reduce the risk of forklift rollover and improve the forklift safety.
Key Words: Counterbalanced forklift truck, Stable zone partition, Lateral load transfer rate, Model predictive control, Improved chain incremental allocation

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