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New insights on robust control of tilting trains with combined uncertainty and performance constraints

A rigorous study on optimized robust control is presented for non-preview (nulling-type) high-speed tilting rail vehicles. The scheme utilizes sensors on the vehicle’s body, contrary to that of preview tilt (which uses prior rail track information). Tilt with preview is the industrial norm nowadays...

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Main Authors: Hassan, Fazilah, Zolotas, Argyrios, Halikias, George
Format: Article
Language:English
Published: MDPI 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/105677/1/FazilahHassan2023_NewInsightsonRobustControlofTilting.pdf
http://eprints.utm.my/105677/
http://dx.doi.org/10.3390/math11143057
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spelling my.utm.1056772024-05-15T06:40:51Z http://eprints.utm.my/105677/ New insights on robust control of tilting trains with combined uncertainty and performance constraints Hassan, Fazilah Zolotas, Argyrios Halikias, George TK Electrical engineering. Electronics Nuclear engineering A rigorous study on optimized robust control is presented for non-preview (nulling-type) high-speed tilting rail vehicles. The scheme utilizes sensors on the vehicle’s body, contrary to that of preview tilt (which uses prior rail track information). Tilt with preview is the industrial norm nowadays but is a complex scheme (both in terms of inter-vehicle signal connections and when it comes to straightforward fault detection). Non-preview tilt is simple (as it essentially involves an SISO control structure) and more effective in terms of (the localization of) failure detection. However, the non-preview tilt scheme suffers from performance limitations due to non-minimum-phase zeros in the design model (due to the compound effect of the suspension dynamic interaction and sensor combination used for feedback control) and presents a challenging control design problem. We proposed an optimized robust control design offering a highly improved non-preview tilt performance via a twofold model representation, i.e., (i) using the non-minimum phase design model and (ii) proposing a factorized design model version with the non-minimum phase characteristics treated as uncertainty. The impact of the designed controllers on tilt performance deterministic (curving acceleration response) and stochastic (ride quality) trade-off was methodically investigated. Nonlinear optimization was employed to facilitate fine weight selection given the importance of the ride quality as a bounded constraint in the design process. MDPI 2023-07 Article PeerReviewed application/pdf en http://eprints.utm.my/105677/1/FazilahHassan2023_NewInsightsonRobustControlofTilting.pdf Hassan, Fazilah and Zolotas, Argyrios and Halikias, George (2023) New insights on robust control of tilting trains with combined uncertainty and performance constraints. Mathematics, 11 (14). pp. 1-19. ISSN 2227-7390 http://dx.doi.org/10.3390/math11143057 DOI:10.3390/math11143057
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Hassan, Fazilah
Zolotas, Argyrios
Halikias, George
New insights on robust control of tilting trains with combined uncertainty and performance constraints
description A rigorous study on optimized robust control is presented for non-preview (nulling-type) high-speed tilting rail vehicles. The scheme utilizes sensors on the vehicle’s body, contrary to that of preview tilt (which uses prior rail track information). Tilt with preview is the industrial norm nowadays but is a complex scheme (both in terms of inter-vehicle signal connections and when it comes to straightforward fault detection). Non-preview tilt is simple (as it essentially involves an SISO control structure) and more effective in terms of (the localization of) failure detection. However, the non-preview tilt scheme suffers from performance limitations due to non-minimum-phase zeros in the design model (due to the compound effect of the suspension dynamic interaction and sensor combination used for feedback control) and presents a challenging control design problem. We proposed an optimized robust control design offering a highly improved non-preview tilt performance via a twofold model representation, i.e., (i) using the non-minimum phase design model and (ii) proposing a factorized design model version with the non-minimum phase characteristics treated as uncertainty. The impact of the designed controllers on tilt performance deterministic (curving acceleration response) and stochastic (ride quality) trade-off was methodically investigated. Nonlinear optimization was employed to facilitate fine weight selection given the importance of the ride quality as a bounded constraint in the design process.
format Article
author Hassan, Fazilah
Zolotas, Argyrios
Halikias, George
author_facet Hassan, Fazilah
Zolotas, Argyrios
Halikias, George
author_sort Hassan, Fazilah
title New insights on robust control of tilting trains with combined uncertainty and performance constraints
title_short New insights on robust control of tilting trains with combined uncertainty and performance constraints
title_full New insights on robust control of tilting trains with combined uncertainty and performance constraints
title_fullStr New insights on robust control of tilting trains with combined uncertainty and performance constraints
title_full_unstemmed New insights on robust control of tilting trains with combined uncertainty and performance constraints
title_sort new insights on robust control of tilting trains with combined uncertainty and performance constraints
publisher MDPI
publishDate 2023
url http://eprints.utm.my/105677/1/FazilahHassan2023_NewInsightsonRobustControlofTilting.pdf
http://eprints.utm.my/105677/
http://dx.doi.org/10.3390/math11143057
_version_ 1800082646993207296
score 13.211869

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