Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7121308 | Measurement | 2018 | 32 Pages |
Abstract
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 (R2â¯=â¯0.99, RMSEâ¯=â¯0.5 and EFâ¯=â¯0.99) than the ANFIS and hence, was suggested for estimating the engine torque.
Keywords
ANFISFCMEGTANNIESPESCRDRBFRMSERPMLSDinternal combustioncompression ignitionModel efficiencymembership functionanalysis of varianceANOVATractorspark ignitionleast significant differenceSubtractive clusteringroot mean squared errorAdaptive neuro fuzzy inference systemArtificial Neural Networkcoefficient of determinationcompletely randomized designRadial basis functionFuzzy C-Means
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Majid Rajabi-Vandechali, Mohammad Hossein Abbaspour-Fard, Abbas Rohani,