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Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering. / Diab, Mohammad; Mercorelli, Paolo; Haus, Benedikt.
Power Engineering and Intelligent Systems - Proceedings of PEIS 2024. Hrsg. / Vivek Shrivastava; Jagdish Chand Bansal; B.K. Panigrahi. Springer Science and Business Media Deutschland, 2024. S. 85-98 (Lecture Notes in Electrical Engineering; Band 1247 LNEE).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

Diab, M, Mercorelli, P & Haus, B 2024, Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering. in V Shrivastava, JC Bansal & BK Panigrahi (Hrsg.), Power Engineering and Intelligent Systems - Proceedings of PEIS 2024. Lecture Notes in Electrical Engineering, Bd. 1247 LNEE, Springer Science and Business Media Deutschland, S. 85-98, 2nd International Conference on Power Engineering and Intelligent Systems - PEIS 2024, Srinagar, Indien, 16.03.24. https://doi.org/10.1007/978-981-97-6714-4_7

APA

Diab, M., Mercorelli, P., & Haus, B. (2024). Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering. In V. Shrivastava, J. C. Bansal, & B. K. Panigrahi (Hrsg.), Power Engineering and Intelligent Systems - Proceedings of PEIS 2024 (S. 85-98). (Lecture Notes in Electrical Engineering; Band 1247 LNEE). Springer Science and Business Media Deutschland. https://doi.org/10.1007/978-981-97-6714-4_7

Vancouver

Diab M, Mercorelli P, Haus B. Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering. in Shrivastava V, Bansal JC, Panigrahi BK, Hrsg., Power Engineering and Intelligent Systems - Proceedings of PEIS 2024. Springer Science and Business Media Deutschland. 2024. S. 85-98. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-97-6714-4_7

Bibtex

@inbook{ad3d75306f9a4c1f99c99dc177b0433f,
title = "Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering",
abstract = "This paper presents the control of a magnetic levitation (Maglev) system using sliding mode control (SMC) and a Backstepping-based variant. The Maglev system considered in this paper has no sensor for measuring the velocity, which is necessary for the application of SMC. For that reason, a Kalman filter is derived to optimally estimate this state variable, effectively rendering this a speed-sensorless approach. Both controllers are evaluated and compared based on extensive simulation studies, with and without additive disturbances, in order to compare their robustness regarding this aspect.",
keywords = "Kalman filter, Maglev system, Sliding mode control, Engineering",
author = "Mohammad Diab and Paolo Mercorelli and Benedikt Haus",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 2nd International Conference on Power Engineering and Intelligent Systems - PEIS 2024, PEIS 2024 ; Conference date: 16-03-2024 Through 17-03-2024",
year = "2024",
doi = "10.1007/978-981-97-6714-4_7",
language = "English",
isbn = "978-981-97-6713-7",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland",
pages = "85--98",
editor = "Vivek Shrivastava and Bansal, {Jagdish Chand} and B.K. Panigrahi",
booktitle = "Power Engineering and Intelligent Systems - Proceedings of PEIS 2024",
address = "Germany",
url = "http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=176808&copyownerid=182588",

}

RIS

TY - CHAP

T1 - Sliding Mode Control Strategies for Maglev Systems Based on Kalman Filtering

AU - Diab, Mohammad

AU - Mercorelli, Paolo

AU - Haus, Benedikt

N1 - Conference code: 2

PY - 2024

Y1 - 2024

N2 - This paper presents the control of a magnetic levitation (Maglev) system using sliding mode control (SMC) and a Backstepping-based variant. The Maglev system considered in this paper has no sensor for measuring the velocity, which is necessary for the application of SMC. For that reason, a Kalman filter is derived to optimally estimate this state variable, effectively rendering this a speed-sensorless approach. Both controllers are evaluated and compared based on extensive simulation studies, with and without additive disturbances, in order to compare their robustness regarding this aspect.

AB - This paper presents the control of a magnetic levitation (Maglev) system using sliding mode control (SMC) and a Backstepping-based variant. The Maglev system considered in this paper has no sensor for measuring the velocity, which is necessary for the application of SMC. For that reason, a Kalman filter is derived to optimally estimate this state variable, effectively rendering this a speed-sensorless approach. Both controllers are evaluated and compared based on extensive simulation studies, with and without additive disturbances, in order to compare their robustness regarding this aspect.

KW - Kalman filter

KW - Maglev system

KW - Sliding mode control

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85212970725&partnerID=8YFLogxK

U2 - 10.1007/978-981-97-6714-4_7

DO - 10.1007/978-981-97-6714-4_7

M3 - Article in conference proceedings

AN - SCOPUS:85212970725

SN - 978-981-97-6713-7

T3 - Lecture Notes in Electrical Engineering

SP - 85

EP - 98

BT - Power Engineering and Intelligent Systems - Proceedings of PEIS 2024

A2 - Shrivastava, Vivek

A2 - Bansal, Jagdish Chand

A2 - Panigrahi, B.K.

PB - Springer Science and Business Media Deutschland

T2 - 2nd International Conference on Power Engineering and Intelligent Systems - PEIS 2024

Y2 - 16 March 2024 through 17 March 2024

ER -

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