Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

The rapid development of technology encourages humans to always create various types of renewable innovations, which are useful for facilitating work and fulfill user’s order as desired. Especially in household appliances that use renewable energy sources in the form of solar cell, this implementation produces a fluctuating output power according to the properties of solar cell. So, it needs to be stabilized by zeta converter with the help of technology in the engineering sector, it is carried out by means of an interface as a liaison between the software and the controlled hardware. Therefore, a fuzzy set theory emerged to solve the problem in control design. However, there are other controls that can improve fuzzy deficiencies, called ANFIS. ANFIS has advantages in the learning process from the plant and the rules that will be made by the Neural Network have the main ability in terms of learning and adaptation, then decision making is done by FLC. This paper aims to compare the performance of the FLC and ANFIS as a control to keep stability of the output power of the zeta converter, where the converter work like a buck-boost converter that can increase or decrease the output power to be consumed in order to stabilize. The use of these two controllers can also compare the time at steady state and the constant power before learning occurs and after  learning process. The simulation results show that the accuracy of ANFIS is 99.82% higher than accuracy of FLC which is 98.08%.