Jian Li
Junming Su
*The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 18 - 2024 |
doi: 10.3389/fnbot.2024.1451924
This article is part of the Research Topic Advancing Neural Network-Based Intelligent Algorithms in Robotics: Challenges, Solutions, and Future Perspectives View all 8 articles
Recurrent Neural Network for Trajectory Tracking Control of Manipulator With Unknown Mass Matrix
Provisionally accepted- Jilin Agriculture University, Changchun, China
Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This paper proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator and comparisons validate the effectiveness and superiority of the proposed method.
Keywords: recurrent neural network (RNN), Trajectory tracking, Manipulator control, dynamic model, unknown mass matrix
Received: 20 Jun 2024; Accepted: 22 Jul 2024.
Copyright: © 2024 Li, Su, Fu, Yu, Mao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Junming Su, Jilin Agriculture University, Changchun, China
Haitao Fu, Jilin Agriculture University, Changchun, China
Weilin Yu, Jilin Agriculture University, Changchun, China
Xuping Mao, Jilin Agriculture University, Changchun, China
Zipeng Liu, Jilin Agriculture University, Changchun, China
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