Robust Optimal Generalized Backstepping Approach to Stabilize and Control Hyper-Chaotic System

Abstract

The issue of controlling non-linear systems is one of the most challenging and attractive topics in all applied sciences and industries. So, it has drawn the attention of many researchers and industrialists. One of the challenges in this field is the lack of a comprehensive and structured control method that can be implemented. Therefore, in this paper presents a novel back-stepping method to stabilize and control hyper-chaotic systems considering the uncertainty in chaotic parameters. The proposed algorithm comprises of an improved backtracking method which applies Lyapunov's theory. In This work, the simulation time is optimized and the tracking error is reduced while the robustness feature is improved. The proposed robust optimal generalized backstep method (ROGBM) is validated by a FMND hyper-chaotic system which is controlled by the internal stabilization and tracking the reference signal. The simulation is performed by MATLAB software. The simulation result reveals the higher efficiency of the proposed controller. The proposed design has an improved performance in reference signal tracking and internal stabilization compared with the conventional GBM method.