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@PHDTHESIS{Shahirpour:1021168,
author = {Shahirpour, Arash},
othercontributors = {Vallery, Heike and Clausen, Elisabeth},
title = {{D}igital twin and trajectory tracking for articulated dump
trucks},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-09517},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {As the demand for automation for mining industries
increases, the need for autonomous transport vehicles
becomes increasingly important. Articulated dump trucks
(ADT), one of the main transport vehicles in mining
operation, pose challenges due to their articulated steering
mechanism. While considerable progress has been made in
autonomous vehicle research, the dynamic modeling and
specific control methods for ADTs remain underexplored. This
thesis investigates two main aspects in the deployment of
ADT in mining environments: simulation and control. The
simulation aspect focuses on the challenges of dynamic
modeling, especially due to the unique steering mechanism in
ADTs. Therefore, a dynamic model is initially developed that
emphasizes the indirect incorporation of steering dynamics
to preserve model differentiability. This indirect approach
allows the model to be used in model-based algorithms,
extending its application beyond simulation alone. Following
the successful implementation of the simulation environment,
this thesis investigates Model-Based Predictive Controller
(MPC) to achieve trajectory following for ADTs. The goal is
to develop control strategies that support the full
operational routine of an ADT. Given that phase delays and
system constraints are inherent in ADTs, the MPC approach
offers a viable solution due to its ability to incorporate
system models, including their delays. Furthermore, this
thesis presents the models required for the MPC, covering
various operational scenarios and accounting for system
delays. These delays have been identified through extensive
testing and system identification. However, the system
identification process in this work relies on offline
methods, meaning it does not adapt to changing conditions in
real time. In addition, this thesis investigates the
integration of sideslip angle estimation and compensation as
an important factor influencing control performance,
especially while cornering. The results show that sideslip
compensation improves control performance in such maneuvers.
This thesis also differentiates between full-sized and
compact ADTs, emphasizing that the faster steering dynamics
of compact ADTs require tailored system models. All proposed
methods have been validated through simulations and
experimental setups, confirming the effectiveness and
adaptability of the developed control strategies for
real-world mining applications.},
cin = {416610},
ddc = {620},
cid = {$I:(DE-82)416610_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-09517},
url = {https://publications.rwth-aachen.de/record/1021168},
}
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