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Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

Ahmed M. Elmogy1,2,*, Wael M. Elawady2

1 Computer Engineering Department, Prince Sattam Bin Abdelaziz University, Alkharj, 24567, KSA
2 Department of Computers and Control Engineering, Tanta University, Tanta, 13457, Egypt

* Corresponding Author: Ahmed M. Elmogy. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 385-400. https://doi.org/10.32604/iasc.2023.032349

Abstract

One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty. Many researchers have been working on developing such type of controllers. One of the most efficient techniques employed to develop such controllers is sliding mode control (SMC). However, the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications. In this paper, the drawbacks of low order traditional sliding mode control (FOTSMC) are resolved by presenting a novel adaptive radial basis function neural network–based generalized rth order sliding mode control strategy for nth order uncertain nonlinear systems. The proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under consideration. The approximation errors are completely considered in the developed approach. The proposed approach can be used with any order of sliding mode and thus can be generally used with various types of applications. The global stability of the proposed control approach is proved through Lyapunov stability criterion. The proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling uncertainties. The simulations results show that the proposed approach provide superior performance compared with other approaches in the literature.

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Cite This Article

APA Style
Elmogy, A.M., Elawady, W.M. (2023). Neuro-based higher order sliding mode control for perturbed nonlinear systems. Intelligent Automation & Soft Computing, 36(1), 385-400. https://doi.org/10.32604/iasc.2023.032349
Vancouver Style
Elmogy AM, Elawady WM. Neuro-based higher order sliding mode control for perturbed nonlinear systems. Intell Automat Soft Comput . 2023;36(1):385-400 https://doi.org/10.32604/iasc.2023.032349
IEEE Style
A.M. Elmogy and W.M. Elawady, "Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems," Intell. Automat. Soft Comput. , vol. 36, no. 1, pp. 385-400. 2023. https://doi.org/10.32604/iasc.2023.032349



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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