Pavement grade recognition algorithm based on sprung mass acceleration

As the input of vehicle suspension system, road excitation directly affects the dynamic response of the system, and then affects the vehicle ride comfort, handling stability and reliability and other indicators. In the field of active suspension control, the road grade recognizer provides road feedforward information to the active suspension control algorithm. For semi-Active suspension control, the result of road grade recognition affects the adjustment of parameter weight, and then determines the direction of vehicle ride comfort or handling stability. Therefore, with the development of intelligent vehicles, more and more attention has been paid to the research of pavement grade recognition methods. In this paper, a pavement grade recognition algorithm based on suspension dynamic response is proposed. By extracting the features of sprung mass acceleration signals and inputting them into probabilistic neural network (PNN) for training, a pavement grade recognition classifier is obtained. Finally, through the test set data, it is verified that the classifier has a high accuracy of pavement grade recognition, and can meet the requirements of semi-Active suspension control.