Road excitation classification for semi-active suspension system with deep neural networks

Inspired by unsupervised feature learning and deep learning, this paper provides a novel classification method for advanced suspension system based on Deep Neural Networks (DNNs). Sparse autoencoder and softmax regression are chosen to form deep structure and the parameters are trained by deep learning. Aiming at showing the superiority of DNNs based road classification method, a simulation of a B-class vehicle with skyhook control is performed in CarSim, and three measurable system responses, i.e., centre of gravity (C.G.) of sprung mass acceleration, rattle space and unsprung mass acceleration are chosen and three independent classifiers are established. Simulation results show that the classifier using unsprung mass acceleration has the highest accuracy and better performance than existing methods. Because of the adaptive learning ability and the deep structure, the proposed method can save work and provide higher classification accuracy.