Synchronization of a Sprott Chaotic System Using Sliding Mode Control

Abstract

The synchronization of chaotic systems remains a vital area of research due to their broad range of applications in fault detection, secure communications, and power electronics. This thesis proposes a Sliding Mode Control (SMC) strategy to achieve synchronization between a master and a slave chaotic system, where the slave is dynamically guided to follow the behavior of the master system. The work addresses key challenges such as sensitivity to initial conditions, parameter variations, and external disturbance factors that commonly impair synchronization accuracy. To overcome these challenges, a sliding mode control law is formulated to drive the synchronization error to zero, thereby ensuring precise and stable synchronization. To further enhance controller performance, Particle Swarm Optimization (PSO), an evolutionary algorithm inspired by the social behavior of bird flocking, is used to fine-tune the SMC parameters. The optimization process aims to improve synchronization accuracy and reduce convergence time. Simulation results, conducted in MATLAB, demonstrate that the PSO-optimized SMC significantly improves the system’s performance. Compared to a conventional PID controller, the proposed method reduces the settling time by 36.6% under normal conditions and by 94.57% in the presence of external disturbances. These results demonstrate the robustness and efficiency of the proposed method, highlighting its strong potential for real-world applications in chaotic system synchronization within the domains of power system monitoring, secure sensor data integration, and cybersecurity for IoT devices.