Volume 22, Issue 2 (JIAEEE Vol.22 No.2 2025)
Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(2): 81-90 |
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Kariminia A, Zarabadipour H. Synchronization of Fractional-Order Chaotic Systems in the Presence of Order Uncertainty Using Projection Recurrent Neural Network based Sliding Mode Control. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (2) :81-90
URL: http://jiaeee.com/article-1-1478-en.html
URL: http://jiaeee.com/article-1-1478-en.html
Imam Khomeini International University
Abstract: (851 Views)
In this paper, the synchronization of fractional-order chaotic Lorenz and Chen systems using a constrained optimal control approach has been investigated. The control algorithm consists of two operative loops where the instant estimation of fractional order using a numerical optimizer is carried out through the inner loop, while the control laws are generated using sliding mode control principles based on exponential reaching conditions. The proposed method to estimate the fractional order is based on forming linear regression for the fractional-order equations of the Lorenz and Chen systems. A performance index is defined to satisfy the reaching conditions where the upper and lower bounds of optimization variables are obtained according to the variation range of fractional order. Therefore, a constrained quadratic programming problem is established where it is instantly minimized using a projection recurrent neural network. This optimizer with a simple structure and having asymptotic stability is able to determine the optimal variables with a high convergence rate. The estimated fractional order is used to update the control signals. The proposed algorithm has high performance in the presence of fractional order uncertainty in comparison with robust control approaches.
Keywords: Fractional-order chaotic Lorenz and Chen systems, synchronization, sliding mode control, projection recurrent neural network, constrained quadratic programming, uncertainty in fractional order
Type of Article: Research |
Subject:
Control
Received: 2022/06/9 | Accepted: 2023/04/24 | Published: 2025/08/15
Received: 2022/06/9 | Accepted: 2023/04/24 | Published: 2025/08/15
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