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

Abstract

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.