Abstract
The scientific and effective prediction of drawbar pull is of great importance in the evaluation of military vehicle trafficability. Nevertheless, the existing prediction models have demonstrated lots of inherent limitations. In this framework, a multiple-kernel relevance vector machine model (MkRVM) including Gaussian kernel and polynomial kernel is proposed to predict drawbar pull. Nonlinear decreasing inertia weight particle swarm optimization (NDIWPSO) is employed for parameter optimization. As the relations between drawbar pull and its influencing factors have not been tested on real vehicles, a series of experimental analyses based on real vehicle test data are done to confirm the effective influencing factors. A dynamic testing system is applied to conduct field tests and gain required test data. Gaussian kernel RVM, polynomial kernel RVM, support vector machine (SVM) and generalized regression neural network (GRNN) are also used to compare with the MkRVM model. The results indicate that the MkRVM model is a preferable model in this case. Finally, the proposed novel model is compared to the traditional prediction model of drawbar pull. The results show that the MkRVM model significantly improves the prediction accuracy. A great potential of improved RVM is indicated in further research of wheel-soil interactions.
Highlights
The current study intends to predict military vehicles’ drawbar pull utilizing improved relevance vector machine (RVM) for evaluating vehicle trafficability
All the other devices were turned on and made sure they were working in normal condition; (3) The vehicle equipped with the dynamic testing system was tested along the planned route and the dynamic responses were recorded by the data acquisition system; (4) After each travel, the level of vertical load and inflation pressure were adjusted
The obtained data were processed for four levels of moving velocity, five levels of vertical load, three levels of inflation pressure and six levels of slip ratio in order to determine the effect of velocity on drawbar pull
Summary
The current study intends to predict military vehicles’ drawbar pull utilizing improved relevance vector machine (RVM) for evaluating vehicle trafficability. Before establishing the improved RVM model, it is of great importance to confirm the effective influencing factors of drawbar pull. It is indicated from the existing models that drawbar pull can be influenced by wheel diameter/width, inflation pressure (Ip), moving velocity (V), vertical load (W), wheel slip ratio (s), passing times and soil conditions [1]. Considering that this study is intended for a better evaluation of military vehicles’ trafficability and to provide instructions for the usage of military vehicles before specific missions, the focus is laid upon the factors, i.e., moving velocity, vertical load, inflation pressure and wheel slip ratio. The proposed RVM model can be established and tested
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