TY - GEN
T1 - Road profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System
AU - Qin, Yechen
AU - Dong, Mingming
AU - Zhao, Feng
AU - Langari, Reza
AU - Gu, Liang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - To meet the requirements of excitation information for semi-active suspension control, a new road classification method with application of Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented. Due to distinct system responses for different road levels, the sprung mass acceleration signal was utilized for classification. To analyze the properties of various road inputs from different perspectives, the acceleration signal was first decomposed into 5 categories via wavelet transform, and 11 statistic features were calculated for each category. Then, an improved distance evaluation technique was applied to remove irrelevant features. With the extracted superior features, a new 2-layers ANFIS classifier was implemented to calculate overall road level. Simulation results revealed that the proposed classifier had significantly improved performance compared to all 1-layer ANFIS classifiers for individual category, and can accurately classify road level with negligible time delay.
AB - To meet the requirements of excitation information for semi-active suspension control, a new road classification method with application of Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented. Due to distinct system responses for different road levels, the sprung mass acceleration signal was utilized for classification. To analyze the properties of various road inputs from different perspectives, the acceleration signal was first decomposed into 5 categories via wavelet transform, and 11 statistic features were calculated for each category. Then, an improved distance evaluation technique was applied to remove irrelevant features. With the extracted superior features, a new 2-layers ANFIS classifier was implemented to calculate overall road level. Simulation results revealed that the proposed classifier had significantly improved performance compared to all 1-layer ANFIS classifiers for individual category, and can accurately classify road level with negligible time delay.
UR - https://www.scopus.com/pages/publications/84962019519
U2 - 10.1109/CDC.2015.7402428
DO - 10.1109/CDC.2015.7402428
M3 - Conference contribution
AN - SCOPUS:84962019519
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1533
EP - 1538
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -