High-Fidelity Virtual Model for Industrial Robot Control Under Uncertain and Disturbed Scenarios: A Comparative Study on the UR5e

BELGACEM, Heni; ABUABIAH, Mohammad; CHIHI, Inès

doi:10.1109/ACCESS.2025.3604653

Article (Scientific journals)

High-Fidelity Virtual Model for Industrial Robot Control Under Uncertain and Disturbed Scenarios: A Comparative Study on the UR5e

BELGACEM, Heni; ABUABIAH, Mohammad; CHIHI, Inès

2025 • In IEEE Access, 13, p. 152063 - 152089

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https://hdl.handle.net/10993/67300

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10.1109/ACCESS.2025.3604653

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Keywords :

Computed torque control; control performance evaluation; dynamic modeling; nonlinear model predictive control; robotic manipulators; robust control; sliding mode control; virtual model; Control performance; Control performance evaluation; Dynamics models; High-fidelity; Nonlinear model predictive control; Performances evaluation; Robotic manipulators; Sliding-mode control; Virtual models; Computer Science (all); Materials Science (all); Engineering (all); Robots; Manipulator dynamics; Service robots; Uncertainty; Kinematics; Computational modeling; Analytical models; Robot sensing systems; Trajectory

Abstract :

[en] Robust control of industrial manipulators under real-world uncertainties is critical for reliable automation. This work presents a comprehensive framework for modeling, control, and performance evaluation of the UR5e robotic manipulator. High-fidelity kinematic and dynamic models are developed and validated against experimental data to create a realistic virtual environment. Four control strategies, including Computed Torque Control, Proportional Integral Derivative, Sliding Mode Control, and Nonlinear Model Predictive Control are implemented and systematically compared. The comparison considers tracking accuracy, robustness, energy efficiency, and computational demand under nominal conditions as well as in the presence of external disturbances, sensor noise, and model uncertainties. Sliding Mode Control demonstrates consistent tracking under disturbances, Nonlinear Model Predictive Control achieves reduced energy consumption with smooth motion profiles, Computed Torque Control provides balanced accuracy and response, and Proportional Integral Derivative performs effectively under low-disturbance conditions. The methodology provides a validated simulation platform for benchmarking robotic control strategies and supports data-driven selection of controllers for industrial applications.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

BELGACEM, Heni ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

ABUABIAH, Mohammad ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; An-Najah National University, Faculty of Engineering and Information Technology, Mechatronics Engineering Department, Nablus, Palestine

CHIHI, Inès ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

External co-authors :

Language :

English

Title :

High-Fidelity Virtual Model for Industrial Robot Control Under Uncertain and Disturbed Scenarios: A Comparative Study on the UR5e

Publication date :

2025

Journal title :

IEEE Access

ISSN :

2169-3536

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

152063 - 152089

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/6287639/10820123/11145441.pdf?arnumber=11145441

Funders :

Luxembourg National Research Fund

Funding text :

This work was supported by Luxembourg National Research Fund (FNR) through the SMOD-SHA Project.

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