Abstract
The premise of improving the ride comfort of all-terrain crane by controlling the active suspensions is to realize the accurate identification of the road level. Existing researches on road level identification using vehicle responses are mostly based on small vehicles with two axles, while few researches on multi-axle large vehicles. This paper analyzes the response parameters of all-terrain crane when driving on typical roads, and proposes a method of road level identification based on the Support Vector Machine (SVM) by using the data of oil pressure and displacement of active suspensions. The training of SVM is completed by using the models of random road and all-terrain crane to generate the response information of vehicle traveling on each level of road. The accuracy of the models and the identification results are verified. The verified results on an independent test set show that the identification accuracy of road level can reach 98%.
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