| Autor: |
Zeguo Li, Zonglin Huang, Qiang Huang, Yuancan Huang |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
2015 IEEE International Conference on Mechatronics and Automation (ICMA). |
| DOI: |
10.1109/icma.2015.7237593 |
| Popis: |
Since the gravity terms depend only on the link positions in compliant joint robots, a neural-network-based gravity compensation scheme is conceived while the gravity model is unknown or is too complicated to be expressed explicitly. A PD-type control with this compensation is developed with the high-gain torque inner loop such that singular perturbation theory may be used to analyze the stability and passivity. Finally, three experiments are implemented to validate the effectiveness of the invented PD-type control with neural-network-based gravity compensation. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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