Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures

Qi SONG; Yong Duan SONG

doi:10.1109/TNN.2011.2175451

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures

Qi SONG^*
, Yong Duan SONG^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

152 Citations (Scopus)

Abstract

This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only- the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations. © 2006 IEEE.

Original language	English
Article number	6099627
Pages (from-to)	2250-2261
Number of pages	12
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	22
Issue number	12
DOIs	https://doi.org/10.1109/TNN.2011.2175451
Publication status	Published - Dec 2011
Externally published	Yes

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 60974052 and Grant 61134001, in part by the Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT0949, in part by the Beijing Jiaotong University Research Program under Project RCS2008ZT002, Grant 2009JBZ001, and Grant 2009RC008, in part by the Innovation Foundation of Beijing Jiaotong University under Grant 2011YJS009, and in part by Major State Basic Research Development Program 973 (2012CB215202).

Keywords

Data-based
fault-tolerant
input nonlinearities
neuroadaptive control

Access to Document

10.1109/TNN.2011.2175451

Cite this

@article{ae57b79bb69c49f5933194f16b4e7160,

title = "Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures",

abstract = "This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only- the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations. {\textcopyright} 2006 IEEE.",

keywords = "Data-based, fault-tolerant, input nonlinearities, neuroadaptive control",

author = "Qi SONG and SONG, \{Yong Duan\}",

year = "2011",

month = dec,

doi = "10.1109/TNN.2011.2175451",

language = "English",

volume = "22",

pages = "2250--2261",

journal = "IEEE Transactions on Neural Networks and Learning Systems",

issn = "2162-237X",

publisher = "IEEE Computational Intelligence Society",

number = "12",

}

TY - JOUR

T1 - Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures

AU - SONG, Qi

AU - SONG, Yong Duan

PY - 2011/12

Y1 - 2011/12

N2 - This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only- the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations. © 2006 IEEE.

AB - This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only- the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations. © 2006 IEEE.

KW - Data-based

KW - fault-tolerant

KW - input nonlinearities

KW - neuroadaptive control

UR - https://www.scopus.com/pages/publications/83655191571

U2 - 10.1109/TNN.2011.2175451

DO - 10.1109/TNN.2011.2175451

M3 - Journal Article (refereed)

AN - SCOPUS:83655191571

SN - 2162-237X

VL - 22

SP - 2250

EP - 2261

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

IS - 12

M1 - 6099627

ER -

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this