A new control method based on type-2 fuzzy neural PI controller to improve dynamic performance of a half-bridge DC–DC converter

Abstract

Due to nonlinear behavior and the uncertainty with converter structure, controller design is difficult and accompanied with complexities. In this paper, a new control method based on Type-2 fuzzy neural PI (T2FN PI) controller is applied to improve the dynamic response of half-bridge DC–DC converters for different operation conditions. The T2FNN PI controller amends the specifications of the converter system by controlling its duty cycle of switching. In addition, proposed controller can property handle the uncertainty which is associated to converter structure, measuring devices and measured control signals.The T2FN PI controller is a combination of type-2 fuzzy linguistic process and neural network learning capability. The back-propagation algorithm is applied to train the parameters of T2FNN PI controller. Furthermore, PSO algorithm is applied to obtain the T2FN PI training pattern.In order to evaluate the performance of the proposed T2FN PI controller, it has been compared with a Fuzzy controller and the conventional PI controller for five different operating conditions. The simulation results show efficiency of the T2FNN PI controller in compare to Fuzzy controller and PI controller in terms of better rejection of disturbances, faster transient response and less peak overshoot.