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@PHDTHESIS{Schubert:1024626,
author = {Schubert, Philipp},
othercontributors = {Abel, Dirk and Corves, Burkhard},
title = {{P}redictive control strategies for safe payload handling
in crane-based offshore operations},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2026-00201},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Crane-based loading operations present an integral part of
today’s ocean economy, an industry that is projected to
become even more vital due to the emergence of offshore
windparks as part of a more sustainable future. At the same
time, offshore operations are getting increasingly
challenging as drilling platforms and wind parks move from
shallow waters to open sea, where more severe sea states are
common. So-called knuckle boom cranes (KBC) are deployed
e.g. on supply vessels and offer increased flexibility
during payload handling thanks to an additional articulated
boom. To this day, most loading operations are controlled
manually requiring highly trained crane operators and
additional personnel overseeing operation. Increasing the
level of automatization promises a simplified handling task,
reduced costs and improved operational safety. Yet, only
solutions for vertical payload stabilization are established
in industrial practice. The objective of sway control
attracted interest from academia, while an holistic approach
to spatial payload stabilization through automated control
remains an open gap. In context of this thesis project,
predictive control strategies directed towards more
efficient and safe offshore operations are researched. After
reviewing common modeling approaches, a control-oriented
model of vessel, crane and payload is derived, which forms
the basis of the investigated model predictive payload
controller. Different formulations of the underlying optimal
control problem are assessed for control performance and
real-time feasibility. In particular, a control scheme is
put forward leveraging the differential flatness of the
crane-payload system in order to invert the system
equations. It further motivates a payload-centric approach
to payload stabilization and trajectory tracking. The
flatness-based model predictive controller (FMPC) is
compared to established linear as well as nonlinear versions
of MPC. The considered predictive control topology is
complemented by a target selector yielding optimized crane
configurations and a receding horizon observer providing
estimates of the system state alongside short-time
predictions of the vessel motions. The controller designs
are studied in simulation for different sea states. The
control performance is shown to be directly linked to the
available capacity of the crane’s hydraulic actuators.
Also, the added benefit of optimizing the crane
configuration based on the crane’s manipulability index is
demonstrated. Last, first validation trials of a model
predictive payload controller in a robot-based test bench
are presented suggesting that MPC can be used to induce
damping and reduce payload oscillations. The thesis
concludes with a discussion of operational safety from an
automated control perspective.},
cin = {416610},
ddc = {620},
cid = {$I:(DE-82)416610_20140620$},
pnm = {SFI Offshore Mechatronics initiative (5127328-7)},
pid = {G:(RCN)5127328-7},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2026-00201},
url = {https://publications.rwth-aachen.de/record/1024626},
}
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