TY - GEN
T1 - Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression
AU - Gao, Zepeng
AU - Chen, Sizhong
AU - Zhao, Yuzhuang
AU - Wu, Zhicheng
AU - Yang, Lin
AU - Hu, Jiang
AU - Chen, Yong
AU - Liu, Baoku
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - Accurate road condition identification is conducive to improving the accuracy of vehicle performance. Aiming at electromagnetic active suspension, a novel method is proposed to realize accurate road condition identification using finite unknown samples. Because actual road condition is changeable, it is not exactly consistent with the standard grade road. Therefore, this paper adopts the power spectral density value Gq(n0) as the identification object to identify the non-standard road condition. Accordingly, back propagation neural network (BPNN) and support vector regression (SVR) are employed to identify road conditions respectively. The results suggest that these two methods have high accuracy for the identification of standard grade roads. However, the random oscillation of road conditions increases the sample uncertainty, which seriously affects the identification accuracy of BPNN. This also causes that the accuracy of road condition identification obtained by SVR with finite sample data is significantly higher than that obtained by BPNN.
AB - Accurate road condition identification is conducive to improving the accuracy of vehicle performance. Aiming at electromagnetic active suspension, a novel method is proposed to realize accurate road condition identification using finite unknown samples. Because actual road condition is changeable, it is not exactly consistent with the standard grade road. Therefore, this paper adopts the power spectral density value Gq(n0) as the identification object to identify the non-standard road condition. Accordingly, back propagation neural network (BPNN) and support vector regression (SVR) are employed to identify road conditions respectively. The results suggest that these two methods have high accuracy for the identification of standard grade roads. However, the random oscillation of road conditions increases the sample uncertainty, which seriously affects the identification accuracy of BPNN. This also causes that the accuracy of road condition identification obtained by SVR with finite sample data is significantly higher than that obtained by BPNN.
KW - Active suspension
KW - Non-standard road condition
KW - Power spectral density value
KW - Road condition identification
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85076850420&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-0474-7_89
DO - 10.1007/978-981-15-0474-7_89
M3 - Conference contribution
AN - SCOPUS:85076850420
SN - 9789811504730
T3 - Lecture Notes in Electrical Engineering
SP - 947
EP - 957
BT - Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019
A2 - Wang, Rui
A2 - Chen, Zengqiang
A2 - Zhang, Weicun
A2 - Zhu, Quanmin
PB - Springer
T2 - 11th International Conference on Modelling, Identification and Control, ICMIC 2019
Y2 - 13 July 2019 through 15 July 2019
ER -