Design of optimized backstepping controller for the synchronization of chaotic Colpitts oscillator using shark smell algorithm

TL;DR

This paper presents an optimized adaptive backstepping controller for synchronizing chaotic Colpitts oscillators, utilizing the shark smell optimization algorithm to enhance accuracy and convergence over traditional methods.

Contribution

The paper introduces a novel application of shark smell optimization to tune backstepping controllers for chaotic oscillator synchronization, outperforming particle swarm optimization.

Findings

The proposed method achieves higher synchronization accuracy.

It converges faster than PSO-optimized controllers.

Simulation results validate the effectiveness of the shark smell algorithm.

Abstract

In this paper, an adaptive backstepping controller has been tuned to synchronize two chaotic Colpitts oscillators in a master slave configuration. The parameters of the controller are determined using shark smell optimization (SSO) algorithm. Numerical results are presented and compared with those of particle swarm optimization (PSO) algorithm. Simulation results show better performance in terms of accuracy and convergence for the proposed optimized method compared to PSO optimized controller or any non-optimized backstepping controller.