Modeling of Draft Force Variation in a Winged Share Tillage Tool Using Fuzzy Table Look-Up Scheme

Abstract

Currently Artificial Intelligence (AI) methods such as fuzzy logic have also a great importance in both modelling and control. The main purpose of this research is to explore the intelligent way to model soil-tool interaction for a winged share tillage tool. A Fuzzy Inference System (FIS) model, with Mamdani min-max method and 24 rules was developed based on table look-up scheme in order to predict draft requirements of two winged share tillage tools in a loam soil under varying operating conditions. Tests were taken in soil bin. The trials were conducted in different working depths and working speeds of winged shares. The input parameters of the FIS were working depth, working speed and share width. The output from the FIS was the draft requirement of the winged share. The results of the developed FIS were compared with the test data of experimental results. The coefficient of determination of relationships was found 0.92 and Root Mean Squares of Errors (RMSE) was 0.33 for draft force. Such results indicate that the developed FIS model for draft prediction could be considered as an alternative and practical tool for predicting draft requirement of tillage implements under the selected experimental conditions.