Container crane controller with the use of a neurofuzzy network

Abstract

A container crane has the function of transporting containers from one point to another point. The difficulty of this task lies in the fact that the container is connected to the bridge crane by cables, causing an opening angle while the container is being transported, interfering with the operation at high speeds due to oscillation that occurs at the end point, which could cause accidents. Fuzzy logic (FL) is a mathematical theory that aims to allow the modeling of approximate way of thinking, imitating the human ability to make decisions in uncertain and imprecise environments. The Artificial Neural Networks (ANN) models are made of simple processing units, called artificial neurons, which calculate mathematical functions. The aim of the paper was to present a container crane controller pre-project using an artificial neural network type Multilayer Perceptron (MLP) combined with FL, referred to as Neuro Fuzzy Network (NFN).