Equation Discovery of KUKA 6-DoF Industrial Robotic Manipulator Dynamics with Backlash Using Lasso Model Selection Criteria

The dynamic equations of robot manipulators are crucial for control system design and system analysis. Manipulator robots driven by electric motors with gearboxes often experience backlash and friction during movement, leading to inaccuracies in dynamic modeling. This study proposes functions to model backlash and friction forces to enhance the accuracy of 6-DoF robot manipulator dynamic modeling. Additionally, the nonlinear dynamic equations of the manipulator robot were derived using the LASSO model selection criteria and the Variable Segregation Algorithm (LMSCVS) method. The results demonstrate that the LMSCVS method can produce dynamic equations for robot manipulators with an improved accuracy of up to 95% when using the proposed library functions to model backlash and friction.

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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/icitee62483.2024.10808945
Citations
2
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.66

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article

Equation Discovery of KUKA 6-DoF Industrial Robotic Manipulator Dynamics with Backlash Using Lasso Model Selection Criteria

Oyas Wahyunggoro, Swadexi Istiqphara, Adha Imam Cahyadi
2 citations
Fault Detection and Control Systems
0.66
article

Equation Discovery of KUKA 6-DoF Industrial Robotic Manipulator Dynamics with Backlash Using Lasso Model Selection Criteria

Oyas Wahyunggoro, Swadexi Istiqphara, Adha Imam Cahyadi
article en
2 citations

Abstract

The dynamic equations of robot manipulators are crucial for control system design and system analysis. Manipulator robots driven by electric motors with gearboxes often experience backlash and friction during movement, leading to inaccuracies in dynamic modeling. This study proposes functions to model backlash and friction forces to enhance the accuracy of 6-DoF robot manipulator dynamic modeling. Additionally, the nonlinear dynamic equations of the manipulator robot were derived using the LASSO model selection criteria and the Variable Segregation Algorithm (LMSCVS) method. The results demonstrate that the LMSCVS method can produce dynamic equations for robot manipulators with an improved accuracy of up to 95% when using the proposed library functions to model backlash and friction.

Universitas Gadjah Mada (ID)
Ministry of Finance
Openalex Percentile: Top 28%
Fault Detection and Control Systems
0.66
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Equation Discovery of KUKA 6-DoF Industrial Robotic Manipulator Dynamics with Backlash Using Lasso Model Selection Criteria — Oyas Wahyunggoro, Swadexi Istiqphara, et al. · (2024) | TGRS Research Map | TGRS