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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13330 |
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| _version_ | 1866915960092360704 |
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| author | Vicente-Martinez, Jorge Ramirez-Laboreo, Edgar |
| author_facet | Vicente-Martinez, Jorge Ramirez-Laboreo, Edgar |
| contents | This paper presents a new approach to accurately simulating 3D overhead cranes with friction. Although nonlinear friction dynamics has a significant impact on these systems, accurately modeling this phenomenon in simulations is a significant challenge. Traditional methods often rely on imprecise approximations of friction or require excessive computational times for reliable results. To address this, we present a hybrid dynamical model that features a trade-off between high-fidelity friction modeling and computational efficiency. Furthermore, we present a step-by-step algorithm for the comprehensive estimation of all unknown system parameters, including friction. This methodology is based on Bayesian Linear Regression and Least Squares (LS) estimations. Finally, experimental validation with a laboratory crane confirms the effectiveness of the proposed modeling and estimation approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A hybrid dynamic model and parameter estimation method for accurately simulating overhead cranes with friction Vicente-Martinez, Jorge Ramirez-Laboreo, Edgar Systems and Control This paper presents a new approach to accurately simulating 3D overhead cranes with friction. Although nonlinear friction dynamics has a significant impact on these systems, accurately modeling this phenomenon in simulations is a significant challenge. Traditional methods often rely on imprecise approximations of friction or require excessive computational times for reliable results. To address this, we present a hybrid dynamical model that features a trade-off between high-fidelity friction modeling and computational efficiency. Furthermore, we present a step-by-step algorithm for the comprehensive estimation of all unknown system parameters, including friction. This methodology is based on Bayesian Linear Regression and Least Squares (LS) estimations. Finally, experimental validation with a laboratory crane confirms the effectiveness of the proposed modeling and estimation approach. |
| title | A hybrid dynamic model and parameter estimation method for accurately simulating overhead cranes with friction |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.13330 |