An Autonomous Steering Control Scheme for Articulated Heavy Vehicles Using Non-Linear Model Predictive Control Technique

2022-01-0344

03/29/2022

Authors Abstract
Content
This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite efficient freight transportation in economic and environmental benefits, AHVs exhibits poor low-speed maneuverability and low high-speed lateral stability due to their multi-unit vehicle configurations, large sizes, high centers of gravity. In addition, North America’s harsh weather during winters makes the scenario more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in transient curved path negotiations. Heavy goods vehicles represent a 7.5 times higher risk than passenger cars in highway operation. Moreover, human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs. To increase the safety of AHVs, an autonomous steering control scheme is designed for a tractor/semi-trailer using a non-linear model predictive control (NLMPC) technique. To this end, a 4 degrees of freedom (DOF) non-linear yaw-plane model is developed to represent the tractor/semi-trailer. The 4-DOF model considers the tractor motions of longitudinal, lateral and yaw, as well as the trailer yaw motion. To demonstrate the effectiveness of the NLMPC-based autonomous steering control scheme, co-simulations are conducted, in which the NLMPC controller is designed using MatLab/SimuLink and the virtual tractor/semi-trailer is generated in TruckSim. Keywords – autonomous steering control, non-linear model Predictive Control, articulated heavy vehicles, tractor/semi-trailer, 4 degrees of freedom non-linear yaw-plane model, co-simulations.
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Citation
Sharma, T., Huang, W., and He, Y., "An Autonomous Steering Control Scheme for Articulated Heavy Vehicles Using Non-Linear Model Predictive Control Technique," SAE Technical Paper 2022-01-0344, 2022, .
Additional Details
Publisher
Published
Mar 29, 2022
Product Code
2022-01-0344
Content Type
Technical Paper
Language
English