Control in the loop for synchronization of nonlinear chaotic systems via adaptive intuitionistic neuro-fuzzy: a comparative study

Abstract

This paper proposes control in the loop (CIL) for the synchronization between two nonlinear chaotic systems at the existence of uncertainties and disturbances using an adaptive intuitionistic neuro-fuzzy (AINF) control scheme. The chaotic systems have been subedited as one is the master and the other is the slave. They both have different initial conditions and parameters. The variation in the initial conditions leads to the butterfly effect, the concept that is well known in the chaos field and means that both systems diverge over time. Therefore, AINF control scheme has been proposed in this paper as a powerful scheme to get over this problem effectively. The main objective of using the AINF control scheme is that it collects the features of its contents. As the intuitionistic fuzzy gives the system flexibility and helps the controller collecting more information about the problem. The neural networks give the controller the ability to learn over time. The experimental results were obtained to verify the applicability and effectiveness of the proposed control scheme against external disturbance and model uncertainties with comparative study.

Internal Synchronization Using Adaptive Control

“…In this subsection, IMSS has been demonstrated for L6DHCS having the switches based on a single error. However, there will be such 30 switches (see equation (10)) but it has been discussed only first three switches. In the proposed study, for the simulation of three switches, the initial conditions x 1 ð0Þ = −1, x 2 ð0Þ = 2, x 3 ð0Þ = 1, x 4 ð0Þ = −1, x 5 ð0Þ = − 6:45, and x 6 ð0Þ = 1 and parameters in the chosen controlling technique k 11 = 1, k 12 = 1, k 13 = 1, k 21 = 0:21, k 22 = 0:63, k 23 = 1:001, k 31 = 1, k 32 = 1, k 33 = 1, k 41 = 1, and k 42 = 1 are chosen for simulation on Mathematica.…”

“…This is the reason why chaos synchronization has drawn the attention of scholars from all across. Various as well as numerous methods have been presented for synchronization of chaotic systems, such as active control [4][5][6][7], backstepping control [8,9], fuzzy control [10,11], impulsive control [12][13][14], event-triggered-based neural network [15,16], output feedback control [17,18], projective synchronization [19], and sliding mode control [20][21][22]. It is worth mentioning the recent contribution of various synchronization techniques that have been proposed.…”

Synchronization of Chaotic Memristive System with Sinusoidal Colpitts Oscillator based on Adaptive Feedback Control Method

“…One of the most important goals of controlling chaotic systems is to stabilize the balance points and stabilize the periodic circuits that exist in chaotic attractions [29][30][31]. Various control methods for synchronizing chaotic systems have been proposed in the literature, including fuzzy control [32][33][34], backstepping control [35,36], adaptive control [30,32,[37][38][39][40], sliding mode control [41,42], fractional order control [43,44], observer control methods [27,43,45] and pointed to the feedback linearization method [46,47]. In practical applications, system state variables are usually not available, so designing a observer based on estimating system state variables is mandatory.…”