Development of a prediction model for estimating tractor engine torque based on soft computing and low cost sensors

Torque estimation needs intensive efforts and costly sensors. In this research, a model was proposed based on soft computing to estimate the ITM285 tractor engine torque using some low cost sensors. To this end, two models including the radial basis function (RBF) neural network and adaptive neuro fuzzy inference system (ANFIS) were used. Thirteen training algorithms were examined to train the RBF. These algorithms were compared using three statistical methods, namely k-fold cross validation, completely randomized design (CRD) and least significant difference (LSD). Moreover, three methods, namely grid partitioning (GP), sub-clustering (SC) and fuzzy c-means (FCM), were used to construct the fuzzy inference system (FIS). However, the FCM was the most suitable method. The sensitivity analysis showed that only measuring engine speed, fuel mass flow and exhaust gas temperature was sufficient for proper engine torque estimation. The RBF had a better performance (R2 = 0.99, RMSE = 0.5 and EF = 0.99) than the ANFIS and hence, was suggested for estimating the engine torque.