Abstract
In this paper, we present a lifelong deep learning-based control of robotic manipulators with nonstandard adaptive laws using singular value decomposition (SVD) based direct tracking error driven (DTED) approach. Moreover, we incorporate concurrent learning (CL) to relax persistency of excitation condition and elastic weight consolidation (EWC) for lifelong learning on different tasks in the adaptive laws. Simulation results confirm theoretical conclusions.
Recommended Citation
I. Ganie and S. Jagannathan, "Adaptive Control of Robotic Manipulators using Deep Neural Networks," IFAC-PapersOnLine, vol. 55, no. 15, pp. 148 - 153, Elsevier; International Federation of Automatic Control (IFAC), Jul 2022.
The definitive version is available at https://doi.org/10.1016/j.ifacol.2022.07.623
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
concurrent learning; deep neural networks; elastic weight consolidation; lifelong learning; SVD
International Standard Serial Number (ISSN)
2405-8963
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Jul 2022
Comments
Office of Naval Research, Grant N00014-21-1-2232