Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression

Zepeng Gao; Sizhong Chen; Yuzhuang Zhao; Zhicheng Wu; Lin Yang; Jiang Hu; Yong Chen; Baoku Liu

doi:10.1007/978-981-15-0474-7_89

Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression

Zepeng Gao^*, Sizhong Chen, Yuzhuang Zhao, Zhicheng Wu, Lin Yang, Jiang Hu, Yong Chen, Baoku Liu

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 G_q(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.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019
Editors	Rui Wang, Zengqiang Chen, Weicun Zhang, Quanmin Zhu
Publisher	Springer
Pages	947-957
Number of pages	11
ISBN (Print)	9789811504730
DOIs	https://doi.org/10.1007/978-981-15-0474-7_89
Publication status	Published - 2020
Event	11th International Conference on Modelling, Identification and Control, ICMIC 2019 - Tianjin, China Duration: 13 Jul 2019 → 15 Jul 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	582
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Modelling, Identification and Control, ICMIC 2019
Country/Territory	China
City	Tianjin
Period	13/07/19 → 15/07/19

Keywords

Active suspension
Non-standard road condition
Power spectral density value
Road condition identification
Support vector regression

Access to Document

10.1007/978-981-15-0474-7_89

Cite this

Gao, Z., Chen, S., Zhao, Y., Wu, Z., Yang, L., Hu, J., Chen, Y., & Liu, B. (2020). Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression. In R. Wang, Z. Chen, W. Zhang, & Q. Zhu (Eds.), Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019 (pp. 947-957). (Lecture Notes in Electrical Engineering; Vol. 582). Springer. https://doi.org/10.1007/978-981-15-0474-7_89

Gao, Zepeng ; Chen, Sizhong ; Zhao, Yuzhuang et al. / Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression. Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. editor / Rui Wang ; Zengqiang Chen ; Weicun Zhang ; Quanmin Zhu. Springer, 2020. pp. 947-957 (Lecture Notes in Electrical Engineering).

@inproceedings{0861e41c3a2343d3a9da11195705ca3b,

title = "Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression",

abstract = "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.",

keywords = "Active suspension, Non-standard road condition, Power spectral density value, Road condition identification, Support vector regression",

author = "Zepeng Gao and Sizhong Chen and Yuzhuang Zhao and Zhicheng Wu and Lin Yang and Jiang Hu and Yong Chen and Baoku Liu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 11th International Conference on Modelling, Identification and Control, ICMIC 2019 ; Conference date: 13-07-2019 Through 15-07-2019",

year = "2020",

doi = "10.1007/978-981-15-0474-7_89",

language = "English",

isbn = "9789811504730",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "947--957",

editor = "Rui Wang and Zengqiang Chen and Weicun Zhang and Quanmin Zhu",

booktitle = "Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019",

address = "Germany",

}

Gao, Z, Chen, S, Zhao, Y , Wu, Z , Yang, L, Hu, J, Chen, Y & Liu, B 2020, Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression. in R Wang, Z Chen, W Zhang & Q Zhu (eds), Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. Lecture Notes in Electrical Engineering, vol. 582, Springer, pp. 947-957, 11th International Conference on Modelling, Identification and Control, ICMIC 2019, Tianjin, China, 13/07/19. https://doi.org/10.1007/978-981-15-0474-7_89

Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression. / Gao, Zepeng; Chen, Sizhong; Zhao, Yuzhuang et al.
Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. ed. / Rui Wang; Zengqiang Chen; Weicun Zhang; Quanmin Zhu. Springer, 2020. p. 947-957 (Lecture Notes in Electrical Engineering; Vol. 582).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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 -

Gao Z, Chen S, Zhao Y , Wu Z , Yang L, Hu J et al. Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression. In Wang R, Chen Z, Zhang W, Zhu Q, editors, Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. Springer. 2020. p. 947-957. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-0474-7_89

Stochastic Road Condition Identification for Electromagnetic Active Suspension Based on Support Vector Regression

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Keywords

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