A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

Abstract

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

Analysis of inertia, damping, and synchronization characteristics in grid-connected photovoltaic systems with fuzzy logic control

“…whereas the Sugeno technique can be applied to the modelling of any inference system with a linear or constant membership function at its output. Similar to the Mamdani method [24], the first two phases of the Sugeno technique involve fuzzifying inputs and applying fuzzy operator.…”

Pendekatan Fuzzy Logic dalam Perhitungan Harga Rental Truck Crane

“…Namun, topik yang kurang dibahas adalah pilihan sistem inferensi fuzzy. Dalam artikel ini, dua kelas Mamdani dan Sugeno dibahas untuk pengontrol terbaik, yang paling stabil adalah metode Sugeno [12]. Logika fuzzy yang digunakan adalah fuzzy Mamdani yang merupakan salah satu metode inferensi paling umum hingga sampai saat ini digunakan untuk masalah pada kontrol logika fuzzy.…”

A maximum efficiency point tracking control method for ultrasonic motors with temperature compensation

“…Efficiency is an important part that people pay great attention to. The pursuit of higher efficiency is a common goal in various projects, such as, solar power generation, electric vehicle charging, smart home, et al [14][15][16]. The main goal of this paper is to track the maximum efficiency point of the USM and reduce the rate of temperature rise.…”

“…Therefore, compensation for the drift of motor speed and efficiency caused by changes in resonant frequency and suppression of the temperature rise of the motor through managing control variables is an important research topic [11][12][13].Efficiency is an important part that people pay great attention to. The pursuit of higher efficiency is a common goal in various projects, such as, solar power generation, electric vehicle charging, smart home, et al [14][15][16]. The main goal of this paper is to track the maximum efficiency point of the USM and reduce the rate of temperature rise.…”