A Model-Free Finite-Time Control Technique for Synchronization of Variable-Order Fractional Hopfield-like Neural Network

• Published in: Fractal and fractional Vol. 7; no. 5; p. 349
• Main Authors: Alsaade, Fawaz W., Al-zahrani, Mohammed S., Yao, Qijia, Jahanshahi, Hadi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.04.2023
• Subjects: Analysis; Closed loop systems; Closed loops; Complex variables; Control systems; Controllers; Convergence; Feedback control; Hopfield-like neural network; Methods; neural network estimator; Neural networks; Performance evaluation; Robust control; Signal processing; super-twisting sliding mode; Synchronism; Time synchronization; variable-order fractional; Voice recognition
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract7050349

Although the literature presents promising techniques for the control of integer-order systems, control and synchronizing fractional systems still need further improvement to ensure their robustness and convergence time. This study aims to address this issue by proposing a model-free and finite-time super-twisting control technique for a variable-order fractional Hopfield-like neural network. The proposed controller is enhanced with an intelligent observer to account for disturbances and uncertainties in the chaotic model of the Hopfield-like neural network. The controller is able to regulate the system even when its complex variable-order fractional dynamic is completely unknown. Moreover, the proposed technique guarantees finite-time convergence of the closed-loop system. First, the dynamics of the variable-order fractional Hopfield-like neural network are examined. Then, the control design is described and its finite-time stability is proven. The controller is then applied to the variable-order fractional system and tested under two different scenarios to evaluate its performance. The results of the simulations demonstrate the excellent performance of the proposed method in both scenarios.