Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914039695671296 |
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| author | La, Trung Kien Kaigom, Eric Guiffo |
| author_facet | La, Trung Kien Kaigom, Eric Guiffo |
| contents | In this work, deep neural networks made up of multiple hidden Long Short-Term Memory (LSTM) and Feedforward layers are trained to predict the thermal behavior of the joint motors of robot manipulators. A model-free and scalable approach is adopted. It accommodates complexity and uncertainty challenges stemming from the derivation, identification, and validation of a large number of parameters of an approximation model that is hardly available. To this end, sensed joint torques are collected and processed to foresee the thermal behavior of joint motors. Promising prediction results of the machine learning based capture of the temperature dynamics of joint motors of a redundant robot with seven joints are presented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors La, Trung Kien Kaigom, Eric Guiffo Robotics Artificial Intelligence Emerging Technologies Machine Learning Systems and Control In this work, deep neural networks made up of multiple hidden Long Short-Term Memory (LSTM) and Feedforward layers are trained to predict the thermal behavior of the joint motors of robot manipulators. A model-free and scalable approach is adopted. It accommodates complexity and uncertainty challenges stemming from the derivation, identification, and validation of a large number of parameters of an approximation model that is hardly available. To this end, sensed joint torques are collected and processed to foresee the thermal behavior of joint motors. Promising prediction results of the machine learning based capture of the temperature dynamics of joint motors of a redundant robot with seven joints are presented. |
| title | Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors |
| topic | Robotics Artificial Intelligence Emerging Technologies Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2509.12739 |