Deep learning-based smith predictor design for a remote grasping control system-Reference-Cited by-同舟云学术

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Deep learning-based smith predictor design for a remote grasping control system

Published:2022-04-30 Issue:5 Volume:36 Page:2533-2545
ISSN:1738-494X
Container-title:Journal of Mechanical Science and Technology
language:en
Short-container-title:J Mech Sci Technol

Author:

Kim Dong-Eon,Li Ailing,Dau Mai-Ngoc,Kim Hyun-Hee,Chung Wan-Young

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

https://link.springer.com/content/pdf/10.1007/s12206-022-0435-9.pdf

Reference43 articles.

1. K. Takayuk et al., Analysis of humanoid appearances in Human-Robot interaction, IEEE Transactions on Robotics, 24(3) (2008) 725–735.

2. T. M. Park, S. R. Lee and H. Yi, Workspace mapping with adaptive fuzzy control for robotic manipulator in teleoperation, Journal of Mechanical Science and Technology, 34(5) (2020) 2171–2178.

3. F. Xiao et al., Design and evaluation of a 7-DOF cable-driven upper limb exoskeleton, Journal of Mechanical Science and Technology, 32(2) (2018) 855–864.

4. L. Qinkai and T. Hermans, Modeling grasp type improves learning-based grasp planning, IEEE Robotics and Automation Letters, 4(2) (2019) 784–791.

5. A.-A. Alejandro et al., Predictor-based remote tracking control of a mobile robot, IEEE Transactions on Control Systems Technology, 22(6) (2014) 2087–2102.

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