FPGA Realization and Lyapunov–Krasovskii Analysis for a Master-Slave Synchronization Scheme Involving Chaotic Systems and Time-Delay Neural Networks

In this paper, the trajectory tracking control and the field programmable gate array (FPGA) implementation between a recurrent neural network with time delay and a chaotic system are presented. The tracking error is globally asymptotically stabilized by means of a control law generated from the Lyap...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-09, Vol.2021, p.1-17
Main Authors: Perez-Padron, J., Posadas-Castillo, C., Paz-Perez, J., Zambrano-Serrano, E., Platas-Garza, M. A.
Format: Article
Language:English
Subjects:
Behavior
Chaos theory
Control theory
Dynamical systems
Equilibrium
Field programmable gate arrays
Mathematical functions
Mathematical problems
Neural networks
Recurrent neural networks
Synchronism
Time lag
Tracking control
Tracking errors
Trajectory control
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Summary:In this paper, the trajectory tracking control and the field programmable gate array (FPGA) implementation between a recurrent neural network with time delay and a chaotic system are presented. The tracking error is globally asymptotically stabilized by means of a control law generated from the Lyapunov–Krasovskii and Lur’e theory. The applicability of the approach is illustrated by considering two different chaotic systems: Liu chaotic system and Genesio–Tesi chaotic system. The numerical results have shown the effectiveness of obtained theoretical results. Finally, the theoretical results are implemented on an FPGA, confirming the feasibility of the synchronization scheme and showing that it is hardware realizable.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/2604874