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

This research introduces an innovative control method designed for the synchronization and management of chaotic systems through the application of advanced neural network techniques. Specifically, a neural network-based sliding mode control framework is employed to enhance system stability and precision in synchronization tasks. Chaotic systems, particularly when arranged in master-slave configurations, exhibit behaviors that are highly sensitive to initial conditions and parameter variations, making them ideal candidates for the proposed approach. The core of this methodology leverages neural networks to estimate unknown nonlinear functions and dynamically adjust the control coefficients, ensuring high accuracy and adaptability in real-time. One of the key contributions of this study lies in addressing the complex issues of parametric uncertainty, external disturbances, and unmodeled dynamics that typically challenge conventional sliding mode control methods. By incorporating neural networks, the controller is equipped to effectively mitigate these uncertainties, ensuring robust performance even in the face of significant system variability. The adaptive nature of the control system allows for continuous adjustment, resulting in improved synchronization accuracy and faster convergence times. The stability and robustness of the proposed control system are rigorously proven using Lyapunov-based methods. Simulations show that synchronization of nonlinear chaotic systems occurs within 10 s, even under varying conditions. This efficiency demonstrates its practical applications in secure communications, biological systems, and power grids, marking a significant advancement in chaotic system control with broad industrial potential.