Differential Evolution-Based Parameter Tuning in Model-Free Adaptive Control

We introduce a stochastic optimization algorithm, a variation of Opposition-based Multi-objective Differential Evolution with replacement of current population by random individuals in specifics periods, to tune parameters of a Model-Free Adaptive Control System. It is often hard to set such parameters and they are selected according to qualitative analysis of the system response. Evolutionary Algorithms with a single objective function have been used to set the parameters of controllers. Hence, a multi-objective approach was used to solve this problem in order to optimize more than one performance index. In this study, we propose two objective functions in order to approximate the functions of the desired output and the system response. The simulation results suggest the effectiveness of the method to determine the parameters of the controller.