Open Access
ARTICLE
Optimal Learning Slip Ratio Control for Tractor-semitrailer Braking in a Turn based on Fuzzy Logic
Jinsong Donga, Hongwei Zhanga, Ronghui Zhangb,*, Xiaohong Jinc, Fang Chend
a Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, China.
b Xinjiang Laboratory of Perception & Control Technology for IOT, Xinjiang Nor-West Star Information Technology Co., Ltd, Urumqi 830011, China.
c Department of Transportation, School of Mines, China University of Mining and Technology, Xuzhou 221000, China.
d Xinjiang Communications Construction Group Co., Ltd, Urumqi 830016, China.
* Corresponding Author: Ronghui Zhang,
Intelligent Automation & Soft Computing 2018, 24(3), 563-570. https://doi.org/10.31209/2018.100000023
Abstract
The research on braking performance a of tractor-semitrailer is a hard and
difficult point in the field of vehicle reliability and safety technology. In this paper,
the tire braking model and the dynamic characteristic model of the brake torque
with the variable of the controlling air pressure were established. We also
established a nonlinear kinematic model of the tractor-semitrailer when it brakes
on a curve. The parameters and variables of the model were measured and
determined by the road experiment test. The optimal control strategy for the
tractor-semitrailer based on the optimal slipping ratio was proposed. Then the
PID controller and the fuzzy controller were designed respectively. Simulation
results show that the reasonable control strategy can significantly improve the
braking directional stability when a tractor-semitrailer runs on a curving road. The
research results provide technical references for improving the lateral stability
when a tractor-semitrailer brakes on a curve, and it also provides a technical
reference for the road traffic safety.
Keywords
Cite This Article
J. Dong, H. Zhang, R. Zhang, X. Jin and F. Chen, "Optimal learning slip ratio control for tractor-semitrailer braking in a turn based on fuzzy logic,"
Intelligent Automation & Soft Computing, vol. 24, no.3, pp. 563–570, 2018.