Neuro-fuzzy scheme plus optimal PI control for controlling DC-DC converters.

Abstract

The importance of solar systems as a renewable source has recently captured significant efforts from governments around the world to reduce their dependency on fossil fuels. Improving the efficiency of these systems, in general, is an important topic. Specifically, enhancing the control method used for controlling the DC-DC converter occupies a significant place in the efforts dedicated to increasing the efficiency of the solar system. The nonlinearity of such systems might deteriorate the performance of traditional controllers, such as PIDs. Therefore, this paper suggests a new scheme which consists of a PI controller and a neuro-fuzzy controller in a cascade manner. Particle swarm optimization is used for optimally designing the parameters of the PI controller for improving the system's dynamic and steady-state responses. Also, the proposed PI controller has only two tuning parameters which significantly reduce the design time. The system inputs of the proposed fuzzy controller are the power and the current changes, and its output is the duty cycle which directly affects the desired output voltage. On the other hand, the input of the PI controller is the error signal between the desired voltage and the output voltage of the DC-DC converter which forms the controlled variable. The main contribution of the paper is to enhance the efficiency of the system via connecting the optimal PI and neuro-fuzzy controller, which can store human experiences. Different scenarios are used to test the proposed controller and obtain optimal maximum power point tracking under different weather conditions. Results show that the proposed controller significantly improves the efficiency percentage of the DC-DC converter relative to the neuro-fuzzy controller alone by at least 15%. Also, the integral square error index indicates the proposed method has small tracking errors either for the setpoint tracking or disturbance rejection.