@inproceedings{12c83144e7294cc1ad7e0b9d276d61ec,
title = "PD-type control with neural-network-based gravity compensation for compliant joint robots",
abstract = "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.",
keywords = "Compliant Joint Robot, Gravity Compensation, Neural Network, PD Control, Singular Perturbation Theory",
author = "Yuancan Huang and Zeguo Li and Zonglin Huang and Qiang Huang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Conference on Mechatronics and Automation, ICMA 2015 ; Conference date: 02-08-2015 Through 05-08-2015",
year = "2015",
month = sep,
day = "2",
doi = "10.1109/ICMA.2015.7237593",
language = "English",
series = "2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "831--836",
booktitle = "2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015",
address = "United States",
}