2023
Muhammad Ayaz Hussain; Ioannis Iossifidis
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller Journal Article
In: arXiv:2309.04698 [cs.RO], 2023.
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, I.2.6, I.2.8, Quantitative Biology - Neurons and Cognition
@article{ayazhussainAdvancementsUpperBody2023,
title = {Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller},
author = {Muhammad Ayaz Hussain and Ioannis Iossifidis},
url = {https://doi.org/10.48550/arXiv.2309.04698},
doi = {10.48550/arXiv.2309.04698},
year = {2023},
date = {2023-09-09},
urldate = {2023-09-09},
journal = {arXiv:2309.04698 [cs.RO]},
abstract = {In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, I.2.6, I.2.8, Quantitative Biology - Neurons and Cognition},
pubstate = {published},
tppubtype = {article}
}
In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.
2022
Felix Grün; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Invariance to Quantile Selection in Distributional Continuous Control Journal Article
In: arXiv:2212.14262 [cs.LG], 2022.
Links | BibTeX | Tags: Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)
@article{grunInvarianceQuantileSelection2022,
title = {Invariance to Quantile Selection in Distributional Continuous Control},
author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://arxiv.org/abs/2212.14262},
doi = {10.48550/ARXIV.2212.14262},
year = {2022},
date = {2022-12-29},
urldate = {2022-12-29},
journal = {arXiv:2212.14262 [cs.LG]},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)},
pubstate = {published},
tppubtype = {article}
}