A dynamic tire model based on HPSO-SVM

In order to accurately describe the force mechanism of tires on agricultural roads and improve the life cycle of agricultural tires, a tire-deformable terrain model was established. The effects of tread pattern, wheel spine, tire sidewall elasticity, inflation pressure and soil deformation were considered in the model and fitted with a support vector machine (SVM) model. Hybrid particle swarm optimization (HPSO) was used to optimize the parameters of SVM prediction model, of which inertia weight and learning factor were improved. To verify the performance of the model, a tire force prediction model of agricultural vehicle with the improved SVM method was investigated, which was a complex nonlinear problem affected by many factors. Cross validation (CV) method was used to evaluate the training precision accuracy of the model, and then the improved HPSO was adopted to select parameters. Results showed that the choice randomness of specifying the parameters was avoided and the workload of the parameter selection was reduced. Compared with the dynamic tire model without considering the influence of tread pattern and wheel spine, the improved SVM model achieved a better prediction performance. The empirical results indicate that the HPSO based parameters optimization in SVM is feasible, which provides a practical guidance to tire force prediction of agricultural transport vehicles.