Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control

Wei-Yen Wang, Guan Ming Chen, C. W. Tao

Research output: Contribution to journalConference article

11 Citations (Scopus)

Abstract

In this paper, an output-feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output, the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.

Original languageEnglish
Pages (from-to)3675-3680
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 2003 Nov 24
EventSystem Security and Assurance - Washington, DC, United States
Duration: 2003 Oct 52003 Oct 8

Fingerprint

Anti-lock braking systems
Wheels
Feedback
Closed loop systems
Controllers

Keywords

  • Anti-lock brake system
  • Fuzzy neural control
  • Tracking optimal slip ratios

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control. / Wang, Wei-Yen; Chen, Guan Ming; Tao, C. W.

In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, 24.11.2003, p. 3675-3680.

Research output: Contribution to journalConference article

@article{cbc27cb0ce12448ab6f2455be02fa8a8,
title = "Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control",
abstract = "In this paper, an output-feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output, the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.",
keywords = "Anti-lock brake system, Fuzzy neural control, Tracking optimal slip ratios",
author = "Wei-Yen Wang and Chen, {Guan Ming} and Tao, {C. W.}",
year = "2003",
month = "11",
day = "24",
language = "English",
volume = "4",
pages = "3675--3680",
journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",
issn = "0884-3627",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control

AU - Wang, Wei-Yen

AU - Chen, Guan Ming

AU - Tao, C. W.

PY - 2003/11/24

Y1 - 2003/11/24

N2 - In this paper, an output-feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output, the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.

AB - In this paper, an output-feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output, the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.

KW - Anti-lock brake system

KW - Fuzzy neural control

KW - Tracking optimal slip ratios

UR - http://www.scopus.com/inward/record.url?scp=0242552466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0242552466&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0242552466

VL - 4

SP - 3675

EP - 3680

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

SN - 0884-3627

ER -