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Adaptive sliding mode control for chaotic system synchronization using neural networks.

Nidal Turab1, N Raghu2, Satish Choudhury3

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This study introduces a novel neural network-based sliding mode control for chaotic systems. The advanced method enhances synchronization accuracy and robustness against uncertainties, offering practical applications.

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Area of Science:

  • Control Theory
  • Nonlinear Dynamics
  • Artificial Intelligence

Background:

  • Chaotic systems exhibit extreme sensitivity to initial conditions and parameters.
  • Synchronization of chaotic systems is challenging due to inherent nonlinearities and uncertainties.
  • Traditional control methods struggle with parametric uncertainty and external disturbances in chaotic systems.

Purpose of the Study:

  • To develop an innovative control method for synchronizing and managing chaotic systems.
  • To enhance the stability and precision of chaotic system synchronization using neural networks.
  • To address limitations of conventional sliding mode control in handling uncertainties.

Main Methods:

  • Implementation of a neural network-based sliding mode control framework.
  • Utilization of neural networks for estimating unknown nonlinear functions.
  • Dynamic adjustment of control coefficients for real-time adaptability.
  • Lyapunov-based methods for rigorous stability and robustness proofs.

Main Results:

  • Achieved synchronization of nonlinear chaotic systems within 10 seconds.
  • Demonstrated robust performance despite parametric uncertainty, external disturbances, and unmodeled dynamics.
  • Showcased improved synchronization accuracy and faster convergence times through adaptive control.

Conclusions:

  • The proposed neural network-based sliding mode control offers an effective solution for chaotic system synchronization.
  • The method provides robust and adaptive control, mitigating common challenges in nonlinear systems.
  • Significant potential for industrial applications in secure communications, biological systems, and power grids.