Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152141
Title: Data-driven learning for robot control with unknown Jacobian
Authors: Lyu, Shangke
Cheah, Chien Chern
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Lyu, S. & Cheah, C. C. (2020). Data-driven learning for robot control with unknown Jacobian. Automatica, 120, 109120-. https://dx.doi.org/10.1016/j.automatica.2020.109120
Project: A1883c008
Journal: Automatica
Abstract: Unlike most control systems, kinematic uncertainty is present in robot control systems in addition to dynamic uncertainty. The use of different types of external sensors in various configurations also results in different sensory transformation or Jacobian matrices and thus leads to different kinematic models. Currently, there is no systematic theoretical framework in developing data-driven neural network (NN) learning and control methods for task-space tracking control of robots with unknown kinematics and dynamics. The existing NN controllers are limited to either dynamic control or kinematic control without considering the interaction between the inner control loop and the outer control loop. In this paper, a NN based data driven offline learning algorithm and an online learning controller are proposed, which are combined in a complementary way. The proposed task-space control algorithms can be implemented on robotic systems with closed control architecture by considering the interaction with the inner control loop. Theoretical analyses are presented to show the stability of the systems and experimental results are presented to illustrate the performance of the proposed learning algorithms.
URI: https://hdl.handle.net/10356/152141
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2020.109120
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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