International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

In this paper, we study on chaos, one of the most important phenomenons based on complex nonlinear dynamics. We will focus on T-system chaos and in continue, using three synchronization methods, Brain Emotional Learning Based Intelligent Controller (BELBIC), Generalized Backstepping Method (GBM) and adaptive method, the chaotic system will be synchronized. To prove usability of the controllers, the results will be compared with the results obtained by Active Control and Backstepping Controllers. According to the results, proposed controllers synchronize chaotic systems with higher speed, lower setting time, lower overshoot and smaller control signal versus active control and backstepping controllers.

This paper has presented chaos synchronization in the Duffing system using the backstepping approach. Backstepping approach consists of parameters which accept positive values. The parameters are usually chosen optional. The system responses are differently for each value. It is necessary to select proper parameters to obtain a good response because the improper selection of the parameters lead to inappropriate responses. Genetic algorithm can select appropriate and optimal values for the parameters. GA by minimizing the fitness function can find the optimal values for the parameters. This selected fitness function is for minimizing the least square error. Fitness function forces the system error to decay to zero rapidly that it causes the system to have a short and optimal setting time. Fitness function also makes an optimal controller and causes overshoot to reach to its minimum value. This hybrid makes an optimal backstepping controller.

In this paper, we study on chaos, one of the most important phenomenons based on complex nonlinear dynamics. We will focus on T-system chaos and in continue, using three synchronization methods, Brain Emotional Learning Based Intelligent Controller (BELBIC), Generalized Backstepping Method (GBM) and adaptive method, the chaotic system will be synchronized. To prove usability of the controllers, the results will be compared with the results obtained by Active Control and Backstepping Controllers. According to the results, proposed controllers synchronize chaotic systems with higher speed, lower setting time, lower overshoot and smaller control signal versus active control and backstepping controllers.