No load robustness analysis of AI based controllers & estimators for SRM drive

S. K. Bishnoi; Rajesh Kumar; R. A. Gupta

doi:10.1145/2979779.2979888

No load robustness analysis of AI based controllers & estimators for SRM drive

S. K. Bishnoi, Rajesh Kumar, R. A. Gupta

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

Abstract

In this paper no-load robustness analysis of Artificial Intelligence (AI) based drives using four phases 8/6 poles Switched Reluctance Motor (SRM). Models of SR motor, AI based controllers i.e. fuzzy, ANN & ANFIS and AI based angle estimators i.e. fuzzy, ANN & ANFIS were developed and integrated as fuzzy-fuzzy, ANNANN & ANFIS-ANFIS SRM drives. Simulation of drives has been done for robustness performance of the drives and compared results. Robustness of drives are tested by varying switched reluctance motor physical parameters, including phase winding resistance (R), damping constant (F) and rotor inertia (J) in the SRM model. Robustness performance at startup and steady-state conditions at 500 rpm has been obtained by simulating these drives for no-load condition. Robustness performance has been plotted and compared to figure-out most robust AI based SRM drive.

Original language	English
Title of host publication	International Conference on Advances in Information Communication Technology and Computing, AICTC 2016
Editors	Manoj Kuri, Vishal Goar, S. K. Bishnoi
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450342131
DOIs	https://doi.org/10.1145/2979779.2979888
Publication status	Published - 12 Aug 2016
Externally published	Yes
Event	2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016 - Bikaner, India Duration: 12 Aug 2016 → 13 Aug 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	12-13-August-2016

Conference

Conference	2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016
Country/Territory	India
City	Bikaner
Period	12/08/16 → 13/08/16

Keywords

"switched reluctance motor
Artificial intelligence controllers and estimators
Damping factor (F) and rotor inertia (J)"
Load torque
Robustness parameters i.e. phase winding resistance (R)

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2979779.2979888

Cite this

Bishnoi, S. K., Kumar, R., & Gupta, R. A. (2016). No load robustness analysis of AI based controllers & estimators for SRM drive. In M. Kuri, V. Goar, & S. K. Bishnoi (Eds.), International Conference on Advances in Information Communication Technology and Computing, AICTC 2016 Article a109 (ACM International Conference Proceeding Series; Vol. 12-13-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2979779.2979888

Bishnoi, S. K. ; Kumar, Rajesh ; Gupta, R. A. / No load robustness analysis of AI based controllers & estimators for SRM drive. International Conference on Advances in Information Communication Technology and Computing, AICTC 2016. editor / Manoj Kuri ; Vishal Goar ; S. K. Bishnoi. Association for Computing Machinery, 2016. (ACM International Conference Proceeding Series).

@inproceedings{bfd319f6cdd54c23847314dd3a323eee,

title = "No load robustness analysis of AI based controllers & estimators for SRM drive",

abstract = "In this paper no-load robustness analysis of Artificial Intelligence (AI) based drives using four phases 8/6 poles Switched Reluctance Motor (SRM). Models of SR motor, AI based controllers i.e. fuzzy, ANN & ANFIS and AI based angle estimators i.e. fuzzy, ANN & ANFIS were developed and integrated as fuzzy-fuzzy, ANNANN & ANFIS-ANFIS SRM drives. Simulation of drives has been done for robustness performance of the drives and compared results. Robustness of drives are tested by varying switched reluctance motor physical parameters, including phase winding resistance (R), damping constant (F) and rotor inertia (J) in the SRM model. Robustness performance at startup and steady-state conditions at 500 rpm has been obtained by simulating these drives for no-load condition. Robustness performance has been plotted and compared to figure-out most robust AI based SRM drive.",

keywords = "{"}switched reluctance motor, Artificial intelligence controllers and estimators, Damping factor (F) and rotor inertia (J){"}, Load torque, Robustness parameters i.e. phase winding resistance (R)",

author = "Bishnoi, {S. K.} and Rajesh Kumar and Gupta, {R. A.}",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016 ; Conference date: 12-08-2016 Through 13-08-2016",

year = "2016",

month = aug,

day = "12",

doi = "10.1145/2979779.2979888",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

editor = "Manoj Kuri and Vishal Goar and Bishnoi, {S. K.}",

booktitle = "International Conference on Advances in Information Communication Technology and Computing, AICTC 2016",

}

Bishnoi, SK, Kumar, R & Gupta, RA 2016, No load robustness analysis of AI based controllers & estimators for SRM drive. in M Kuri, V Goar & SK Bishnoi (eds), International Conference on Advances in Information Communication Technology and Computing, AICTC 2016., a109, ACM International Conference Proceeding Series, vol. 12-13-August-2016, Association for Computing Machinery, 2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016, Bikaner, India, 12/08/16. https://doi.org/10.1145/2979779.2979888

No load robustness analysis of AI based controllers & estimators for SRM drive. / Bishnoi, S. K.; Kumar, Rajesh; Gupta, R. A.
International Conference on Advances in Information Communication Technology and Computing, AICTC 2016. ed. / Manoj Kuri; Vishal Goar; S. K. Bishnoi. Association for Computing Machinery, 2016. a109 (ACM International Conference Proceeding Series; Vol. 12-13-August-2016).

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

TY - GEN

T1 - No load robustness analysis of AI based controllers & estimators for SRM drive

AU - Bishnoi, S. K.

AU - Kumar, Rajesh

AU - Gupta, R. A.

PY - 2016/8/12

Y1 - 2016/8/12

N2 - In this paper no-load robustness analysis of Artificial Intelligence (AI) based drives using four phases 8/6 poles Switched Reluctance Motor (SRM). Models of SR motor, AI based controllers i.e. fuzzy, ANN & ANFIS and AI based angle estimators i.e. fuzzy, ANN & ANFIS were developed and integrated as fuzzy-fuzzy, ANNANN & ANFIS-ANFIS SRM drives. Simulation of drives has been done for robustness performance of the drives and compared results. Robustness of drives are tested by varying switched reluctance motor physical parameters, including phase winding resistance (R), damping constant (F) and rotor inertia (J) in the SRM model. Robustness performance at startup and steady-state conditions at 500 rpm has been obtained by simulating these drives for no-load condition. Robustness performance has been plotted and compared to figure-out most robust AI based SRM drive.

AB - In this paper no-load robustness analysis of Artificial Intelligence (AI) based drives using four phases 8/6 poles Switched Reluctance Motor (SRM). Models of SR motor, AI based controllers i.e. fuzzy, ANN & ANFIS and AI based angle estimators i.e. fuzzy, ANN & ANFIS were developed and integrated as fuzzy-fuzzy, ANNANN & ANFIS-ANFIS SRM drives. Simulation of drives has been done for robustness performance of the drives and compared results. Robustness of drives are tested by varying switched reluctance motor physical parameters, including phase winding resistance (R), damping constant (F) and rotor inertia (J) in the SRM model. Robustness performance at startup and steady-state conditions at 500 rpm has been obtained by simulating these drives for no-load condition. Robustness performance has been plotted and compared to figure-out most robust AI based SRM drive.

KW - "switched reluctance motor

KW - Artificial intelligence controllers and estimators

KW - Damping factor (F) and rotor inertia (J)"

KW - Load torque

KW - Robustness parameters i.e. phase winding resistance (R)

UR - http://www.scopus.com/inward/record.url?scp=84999636226&partnerID=8YFLogxK

U2 - 10.1145/2979779.2979888

DO - 10.1145/2979779.2979888

M3 - Conference contribution

AN - SCOPUS:84999636226

T3 - ACM International Conference Proceeding Series

BT - International Conference on Advances in Information Communication Technology and Computing, AICTC 2016

A2 - Kuri, Manoj

A2 - Goar, Vishal

A2 - Bishnoi, S. K.

PB - Association for Computing Machinery

T2 - 2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016

Y2 - 12 August 2016 through 13 August 2016

ER -

Bishnoi SK, Kumar R, Gupta RA. No load robustness analysis of AI based controllers & estimators for SRM drive. In Kuri M, Goar V, Bishnoi SK, editors, International Conference on Advances in Information Communication Technology and Computing, AICTC 2016. Association for Computing Machinery. 2016. a109. (ACM International Conference Proceeding Series). doi: 10.1145/2979779.2979888

No load robustness analysis of AI based controllers & estimators for SRM drive

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this