RETRACTED: Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique
ISSN: 0264-4401
Article publication date: 30 May 2023
Issue publication date: 2 June 2023
Retraction notice
The publisher of Engineering Computations wishes to retract the article Anh, H.P.H. and Dat, N.T. (2023), “Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique”, Engineering Computations, Vol. 40 No. 3, pp. 657-678, https://doi.org/10.1108/EC-06-2022-0399. It has come to our attention that a large portion of this article is taken, without full and proper attribution, from an earlier work by A.J. Al-Mahasneh, S.G. Anavatti, M.A. Garratt, and M. Pratama (2021) “Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51 No. 4, pp. 2525-2535, https://doi.org/10.1109/TSMC.2019.2915950. The submission guidelines for Engineering Computations make it clear that articles must be original. The authors of this article would like to note that they do not agree with the content of this notice. The publisher of the journal sincerely apologizes to the readers.
Abstract
Purpose
The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.
Design/methodology/approach
In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.
Findings
A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.
Originality/value
A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.
Keywords
Acknowledgements
The authors acknowledge the Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number DS 2022-20-09.
Citation
Anh, H.P.H. and Dat, N.T. (2023), "RETRACTED: Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique", Engineering Computations, Vol. 40 No. 3, pp. 657-678. https://doi.org/10.1108/EC-06-2022-0399
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited