Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model

J. J. Rath; K. C. Veluvolu; D. Zhang; Q. Zhang; M. Defoort

doi:10.1007/978-3-642-40849-6_18

Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model

J. J. Rath
, K. C. Veluvolu
, D. Zhang
, Q. Zhang
, M. Defoort

School of Electronics Engineering

Kyungpook National University
Shanghai Jiao Tong University
Université de Lille

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

3 Scopus citations

Abstract

The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this paper, a super-twisting algorithm based sliding mode observer is proposed to estimate the road adhesion coefficient, treated as an unknown input in the dynamics of a quarter-vehicle. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results show the effectiveness of the proposed observer in estimation of the road adhesion coefficient that changes with surface variations.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings
Publisher	Springer Verlag
Pages	202-213
Number of pages	12
Edition	PART 2
ISBN (Print)	9783642408489
DOIs	https://doi.org/10.1007/978-3-642-40849-6_18
State	Published - 2013
Event	6th International Conference on Intelligent Robotics and Applications, ICIRA 2013 - Busan, Korea, Republic of Duration: 25 Sep 2013 → 28 Sep 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8103 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Intelligent Robotics and Applications, ICIRA 2013
Country/Territory	Korea, Republic of
City	Busan
Period	25/09/13 → 28/09/13

Keywords

LuGre friction model
road adhesion coefficient
Super-twisting algorithm
torsional tyre model

Access to Document

10.1007/978-3-642-40849-6_18

Cite this

Rath, J. J., Veluvolu, K. C., Zhang, D., Zhang, Q., & Defoort, M. (2013). Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model. In Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings (PART 2 ed., pp. 202-213). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8103 LNAI, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-642-40849-6_18

Rath, J. J. ; Veluvolu, K. C. ; Zhang, D. et al. / Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model. Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings. PART 2. ed. Springer Verlag, 2013. pp. 202-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{c2863d18c2c748cf9ec1e882aec9f504,

title = "Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model",

abstract = "The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this paper, a super-twisting algorithm based sliding mode observer is proposed to estimate the road adhesion coefficient, treated as an unknown input in the dynamics of a quarter-vehicle. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results show the effectiveness of the proposed observer in estimation of the road adhesion coefficient that changes with surface variations.",

keywords = "LuGre friction model, road adhesion coefficient, Super-twisting algorithm, torsional tyre model",

author = "Rath, \{J. J.\} and Veluvolu, \{K. C.\} and D. Zhang and Q. Zhang and M. Defoort",

year = "2013",

doi = "10.1007/978-3-642-40849-6\_18",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "202--213",

booktitle = "Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings",

address = "Germany",

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Rath, JJ, Veluvolu, KC, Zhang, D, Zhang, Q & Defoort, M 2013, Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model. in Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8103 LNAI, Springer Verlag, pp. 202-213, 6th International Conference on Intelligent Robotics and Applications, ICIRA 2013, Busan, Korea, Republic of, 25/09/13. https://doi.org/10.1007/978-3-642-40849-6_18

Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model. / Rath, J. J.; Veluvolu, K. C.; Zhang, D. et al.
Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings. PART 2. ed. Springer Verlag, 2013. p. 202-213 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8103 LNAI, No. PART 2).

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

TY - GEN

T1 - Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model

AU - Rath, J. J.

AU - Veluvolu, K. C.

AU - Zhang, D.

AU - Zhang, Q.

AU - Defoort, M.

PY - 2013

Y1 - 2013

N2 - The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this paper, a super-twisting algorithm based sliding mode observer is proposed to estimate the road adhesion coefficient, treated as an unknown input in the dynamics of a quarter-vehicle. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results show the effectiveness of the proposed observer in estimation of the road adhesion coefficient that changes with surface variations.

AB - The estimation of friction coefficient for a vehicle when it traverses on different surfaces has been an important issue. In this paper, a super-twisting algorithm based sliding mode observer is proposed to estimate the road adhesion coefficient, treated as an unknown input in the dynamics of a quarter-vehicle. By estimating the road adhesion coefficient, the coefficient of friction can be estimated. Simulation results show the effectiveness of the proposed observer in estimation of the road adhesion coefficient that changes with surface variations.

KW - LuGre friction model

KW - road adhesion coefficient

KW - Super-twisting algorithm

KW - torsional tyre model

UR - https://www.scopus.com/pages/publications/84884895685

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DO - 10.1007/978-3-642-40849-6_18

M3 - Conference contribution

AN - SCOPUS:84884895685

SN - 9783642408489

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings

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ER -

Rath JJ, Veluvolu KC, Zhang D, Zhang Q, Defoort M. Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model. In Intelligent Robotics and Applications - 6th International Conference, ICIRA 2013, Proceedings. PART 2 ed. Springer Verlag. 2013. p. 202-213. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-40849-6_18

Estimation of road adhesion coefficient using higher-order sliding mode observer for torsional tyre model

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Keywords

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