Adaptive Backstepping Control of Chaotic Systems with Actuator Fault by Using PSO Dynamic Re-initialization with Time-Varying Control Parameters

Adaptive backstepping control is used in this study to control chaos in a faulty chaotic system. To obtain adaptive backstepping control parameters, typically use the time-consuming trial and error process. Therefore, optimization methods have recently been used to find adaptive backstepping control parameters. These methods of optimization take advantage of minimizing the cost function, which is a weighted function focused on reducing system error and value of control effort, this can be achieved in many ways including Genetic Algorithms (GAs), Evolutionary Programming (EV), Particle Swarm Optimization (PSO), etc. PSO is usually disturbed by local optima trapping and early convergence problems due to the actuator s fault, which affects the performance of the controller. The new PSO procedure, Dynamic Re-initialization of time-varying control parameters, is proposed for solving both challenges, which is faster than the methods discussed so far, its settling time is very short after any change in the occurrence of the fault signal. It has relatively little control effort compared to other methods. The preferred methodology is validated on an example of the chaotic system; of Genesio-Tesi and produces more consistent search results compared to typical PSO

Adaptive Backstepping Control of Chaotic Systems with Actuator Fault by Using PSO Dynamic Re-initialization with Time-Varying Control Parameters Keywords:

Adaptive Backstepping Control , Particle Swarm Optimization (PSO) , Dynamic Re-initialization , Chaotic System