Journal article 801 views
A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
,
Qing Wu
IEEE Transactions on Neural Networks and Learning Systems, Volume: 32, Issue: 4, Pages: 1776 - 1787
Swansea University Author:
Shuai Li
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1109/tnnls.2020.2991088
Abstract
Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to b...
| Published in: | IEEE Transactions on Neural Networks and Learning Systems |
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| ISSN: | 2162-237X 2162-2388 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa56719 |
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| Abstract: |
Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to be infinitely large. The second one is the research of robustness that remains in the analyses of stability and asymptotic convergence. To simultaneously enhance the convergence performance and robustness, this article proposes a new ZNN model by using a supertwisting (ST) algorithm, termed STZNN model, for the tracking control of mobile robot manipulators. The proposed STZNN model inherently possesses the advantages of finite-time convergence and robustness making the control process fast and robust. The bridge from the sliding mode control to the ZNN is built, and the essential connection between the ST algorithm and ZNN is explored by constructing a unified design process. Theorems and proofs about global stability, finite-time convergence, and robustness are provided. Finally, path-tracking applications, comparisons, and tests substantiate the effectiveness and superiority of the STZNN model for the tracking control handling of mobile robot manipulators. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
4 |
| Start Page: |
1776 |
| End Page: |
1787 |