Design, Modeling, and Control of a Hardware-in-the-Loop Testbed for Off-Road Vehicles

Abstract This paper presents the design, modeling, and control of a hardware-in-the-loop (HIL) testbed for off-road vehicles. The proposed HIL testbed employs a transient hydrostatic dynamometer to load a diesel engine to emulate any loading cycles of a wheel loader, which is a representative off-road vehicle. A fully validated wheel loader model is used to calculate the engine load, including both the drive and work functions. Besides, iterative learning control (ILC) has been designed for the loading torque tracking of the hydrostatic dynamometer to ensure accurate emulation of real-world operation scenarios. The developed HIL testbed is used to demonstrate more than 26% energy benefits of automated wheel loaders through systematic optimization compared with human-operated wheel loaders. This HIL testbed serves as a robust platform for advancing research and development across various off-road vehicles, including excavators, tractors, and harvesters.

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Publication Details

Journal
ASME Letters in Dynamic Systems and Control
Published
2024-09-09
DOI
https://doi.org/10.1115/1.4066465
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Design, Modeling, and Control of a Hardware-in-the-Loop Testbed for Off-Road Vehicles

Connor Edson, Zongxuan Sun, Jie Yao, Gaonan Zhao
ASME Letters in Dynamic Systems and Control
Iterative Learning Control Systems
article

Design, Modeling, and Control of a Hardware-in-the-Loop Testbed for Off-Road Vehicles

Connor Edson, Zongxuan Sun, Jie Yao, Gaonan Zhao
article en

Abstract

Abstract This paper presents the design, modeling, and control of a hardware-in-the-loop (HIL) testbed for off-road vehicles. The proposed HIL testbed employs a transient hydrostatic dynamometer to load a diesel engine to emulate any loading cycles of a wheel loader, which is a representative off-road vehicle. A fully validated wheel loader model is used to calculate the engine load, including both the drive and work functions. Besides, iterative learning control (ILC) has been designed for the loading torque tracking of the hydrostatic dynamometer to ensure accurate emulation of real-world operation scenarios. The developed HIL testbed is used to demonstrate more than 26% energy benefits of automated wheel loaders through systematic optimization compared with human-operated wheel loaders. This HIL testbed serves as a robust platform for advancing research and development across various off-road vehicles, including excavators, tractors, and harvesters.

ASME Letters in Dynamic Systems and ControlVol. 4(4)
University of Minnesota (US), Twin Cities Orthopedics (US)
U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Office of Energy Efficiency
Openalex Percentile: Top 84%
Iterative Learning Control Systems
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