Fuzzy knowledge-based model for prediction of traction force of an electric golf car
The methods of artificial intelligence are widely used in soft computing technology due to its remarkable prediction accuracy. How ever, artificial intelligent models are trained using large amount of data obtained from the operation of the off-road vehicle. In contrast, fuzzy knowledge-based models...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd.
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/2058/ http://irep.iium.edu.my/2058/ http://irep.iium.edu.my/2058/ http://irep.iium.edu.my/2058/1/Fuzzy_knowledge-based_model_for_prediction_of_traction_force.pdf |
| Summary: | The methods of artificial intelligence are widely used in soft computing technology due to its remarkable prediction accuracy. How ever, artificial intelligent models are trained using large amount of data obtained from the operation of the off-road vehicle. In contrast, fuzzy knowledge-based models are developed by using the experience of the traction in order to maintain the vehicle traction as required with utilizing optimum power. The main goal of this paper is to describe fuzzy knowledge-based model to be practically applicable to a reasonably wide class of unknown nonlinear systems. Compared with conventional control approach, fuzzy logic approach is more efficient for nonlinear dynamic systems and embedding existing structured human knowledge into workable mathematics. The purpose of
this study is to investigate the relationship between vehicle’s input parameters of power supply (PI) and moisture content (MC) and output parameter of traction force (TF). Experiment has been conducted in the field to investigate the vehicle traction and the result has been compared with the developed fuzzy logic system (FLS) based on Mamdani approach. Results show that the mean relative error of actual and predicted values from the FLS model on TF is found as 7%, which is less than the acceptable limit of 10%. The goodness of fit of the prediction value from FLS is found close to 1.0 as expected and hence shows the good performance of the developed system.
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